Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

William Karesh, DVM, Senior Vice President of EcoHealth Alliance will serve as project advisor
on veterinary topics as well as liaise with data-providing organizations, such as OIE and
ProMED, as required. Dr. Karesh is President of the Working Group on Animal Health and
COVID-19 Task Forces for OIE, Co-Chair of the Wildlife Health Specialist Group of the IUCN.

Facilities: EcoHealth Alliance is a 48-year old scientific research NGO that specializes in
multidisciplinary research and surveillance of the spread of zoonotic emerging diseases.
EcoHealth Alliance is based in New York City with 10,000 ft? of office space. The scientific and
technology staff of 30 are supported by a core administrative staff of 8 people who are available
for work on this project and are funded through core funds.

EcoHealth Alliance is equipped with fiber optic Internet access and video conferencing facilities
to facilitate easy communication between collaborators. EcoHealth Alliance employees have
around the clock access to servers, VPNs, encryption software, and IT support. We use standard
office suite services including Google Apps (Hosted email and web-based collaboration
software), Microsoft Office, and Slack, running on Apple macOS, Ubuntu Linux, and Windows
Operating Systems. EHA teams work in multiple computer languages (Python, NodeJS, and R
programming languages), development frameworks (Agile, continuous integration), and
machine-learning toolboxes. Our development operations are based on reproducible,
containerized frameworks (Ansible, Docker). We perform high-performance computation across
our dedicated cluster of System76 multi-core serversand with NVIDIA deep-learning GPUs and
spot cloud somputs instances through Amazon Web Services EC2 and Microsoft Virtual
Machines, We use a variety of other cloud services for storage (AWS S3, Azure Blob),
continuous integration (GitHub, Gitlab), and other tasks.

Work to be Performed
Tasks in Year. Task.Subtask format
YEAR 1:

Task 1.1 — Develop and deploy project infrastructure: Infrastructure engineering consists of
provisioning a best-in-class computational environment that enables continuous model
development and testing and allows for research and models to be reproduced and extended by
external teams. It includes continuous maintenance and documentation of project codebase.

Subtasks

1.1.1 Layout a provision project network

1.1.2 Set up continuous integration pipelines

1.1.3 Implement backup and data verification jobs
1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation
1.1.6 Perform code review, testing and validation



Task 6,

1) Major Activities: Hospital-based AES and VHF studies

2) Specific Objectives: 1.6,.1-OY4.6.1 Hospital staff identified and trained on study protocols

3) Significant results: The KGMU hospital staff team has been identified, including collaborators
in the KGMU Department of Pediatrics, Medicine, Neurology, Microbiology, as well as research and
laboratory associates and assistants. The team awaits the materials and supplies required for a full
training on study protocols and methadologies. The human clinical survey has been created as a
standardized qualitative and quantitative data collection tool.

4) Major Outcomes: 7 hospital collaborators have been identified and 6 laboratory and hospital
staff have been hired for this study. The hiring and training of the hospital-based team will allow for
samples to be taken from a clinical setting in an ethical, efficient, and scientifically sound manner. Data
from this study will generate new hypotheses about the prevalence of NiV and EBOV related viruses in
India, as well as support the human behavioral risk underpinning these diseases of interest.

Task 7.

1) Major Activities: Wildlife, livestock, and human biosurveillance in rural communities

2) Specific Objectives:

1.7.1.-OY4.7.1. study sites selected

1.7.2-OY5.7.2 human, wildlife, and domestic animal samples collected and tested

3) Significant results: While the Indian subcontinent has been experiencing the extremely
disruptive effects of the COVID-19 pandemic, the project has thus far been able to address the matter of
site selection using a literature review, expert knowledge, and species mapping. Experts in bat biology
have been utilized to map bat species distribution in UP and Kerala, and expect use these tools to inform
further efforts in site selection when travel is safe. Areas in UP and Kerala where key bat species are
found will be considered in conjunction with overlapping areas of livestock farming, as well as human
settlements.

species Distribution Map in Uta Pradesh f

4) Major Outcomes: Bat distribution mapping efforts have been undertaken by members of the
Animal Health team across UP and Kerala. A literature review of relevant bat species has been
performed as a foundational source of information relevant to site selection, which will inform the mapping
exercise and in-person field visits and reconnaissance once is reasonably safe. A standardized data
collection survey tool has been developed to be used in human community sampling efforts.



RESEARCH & RELATED BUDGET - Budget Period 1 OMB Number: 4040-0001
Expiration Date: 12/31/2022

ORGANIZATIONAL DUNS:

Enter name of Organization: = |..-;:ea1-h Alliance

Budget Type: Project [_] Subaward/Consortium Budget Period: 1 _—_‘Start Date: End Date:

A. Senior/Key Person

Months

Requested Fringe Funds
Prefix First Middle Last Suftix Base Salary (3) Cal. Acad. Sum. Salary ($) Benefits ($) Requested ($)
Ee on TOL a a oe

Project Role: [pr;/r1 ]

lone 1 Taal TI

1s 220.05]

Project Role: [senicr Engineer
[ Jemma l [veadersona | l L | a2, 800.9] 15, 640.03] ae.03]
Project Role: [Research Setentist
Total Funds requested for all Senior
Additional Senior Key Persons: Add Attachment l[ | Key Persons in the attached file LF
Total Senior/Key Person
B. Other Personnel
Number of Months Requested Fringe Funds
Personnel Project Role Cal. Acad. Sum. Salary (8) Benefits ($) Requested (8)

Post Doctoral Associates

Graduate Students

LE] Undergraduate Students [ ]
L__] L_]
[|
[

Secretarial/Clerical

Total Number Other Personnel Total other Personnel [

Total Salary, Wages and Fringe Benefits (A+B)

II |
II |
|| |
|| |
| |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


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Grant Training DCP Matrix

Biosecurity incident management policies and
procedures for suspected or confirmed illicit
access to pathogens, intruder alerts, and other
biosecurity incidents.



Grant Training DCP Matrix

activities, including engagement with
appropriate stakeholders and partners at
national, regional, and international levels.

3.1.4 Resource mobilization and
management

Knowledge, understanding and proficiency in
the planning, acquisition, mobilization,
allocation, management and tracking of
financial and non-financial resources for
research projects, including proposal
development and grant writing.

4. Management,
leadership, and
advocacy

4.1 Critical skills, knowledge
and behaviors necessary to
effectively manage projects
and programs, lead teams,
champion solutions, inspire
action, and sustain
programs.

4.1.1 Plan, implements, and
evaluates a project or program

Knowledge, understanding and proficiency in
program planning, implementation,
monitoring and evaluation, including
development and measurement of
appropriate indicators, and advocacy, or
management/oversight of the process.

4.1.2 Apply program management
techniques throughout the project or
program

Knowledge, understanding and proficiency in
program management, including timeline and
deliverables management, team building, and
generating high-level stakeholder support,

4.1.3 Lead culturally diverse teams to
complete a project or program

Knowledge, understanding and proficiency in
developing and leading culturally diverse
teams, ensuring collaboration among team
members, and mentorship of future team
leaders.



Should you have additional questions or concerns regarding this case, you may seek
dispute resolution services from the DTRA FOIA Public Liaison or the Office of Government
Information Services (OGIS). The DTRA FOIA Public Liaison, Mr. Mario Vizcarra, may be
contacted by phone at (703)767-1792 or by email at dtrafoiaprivacy@mail.mil. The contact
information for OGIS can be found at www.archives.gov/ogis.

Sincerely,

Eugene McGitt

Eugene McGirt
FOIA/Privacy Act Specialist
Freedom of Information/Privacy Act Office

Enclosure(s):
As stated


Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in

Jordan & Strengthening Regional Disease Surveillance Capacity/PI Kar

h

Thrust Area 6 - CCWMD Research with Global Partners FRCWMD

Task Year 1 Year 2 Year 3 oY 1 OY 2
Determine the presence of MERS-Co nd other zoonoses among livestock and/or poultry in Jordan
1.1. Identify sites for project study
1.2 Obtain local permissions and approvals 10 work with animals
1.3 Conduct bislogical sampling in animals within the study area
14. Conduct lab testing tor AL MERS-Co¥, brucellosis and leptospirosis
Dete he o, the extent to bun D ock/po D d a on
2.1 identify sites and conduct enrollment for human study component
2.2 Obtain local permissions and approvals to work with human subjects
2.3 Conduct biological sampling
2.4 Conducttesting for AL MERS-Co¥, brucellosis and leptospirosis
3.1 Obtain local permissions and approvals to work with human subjects
3.2 Conduct behavioral risk factor surveys in people
3.3 Analyze epi causal inference to identity modifiable risk fact
4.1 Generate maps of laboratory results
3.2 Generate risk maps with human bel rish data
treagthen local capacity for detection and reporting of MERS-Co ad other zoonoses in Jordan, Iraq, and Lebano

5.1 Cunduct project Kick-utf meeting in Amman with lucal stakebolders

3.2 Train local project stall in proper tecliqa

5.3 Host annual piutners and stakeholders necting

3.4 Complete wmual report and share with parners and stakeholders

5.5 Conduct local/regional training workshops

3.6 Data oxmagement

5.7 Conduct presentationsémectings at times and places specified

Task 6: Identify specific modifiable risk factors for human infection with

these zoonos:

6.1 Conduct causal inference analyses based on Task 1,2. 3

6.2 Share results of analyses at annual partners and stakeholders meeting

6.3 Submit written report to DTRA and local partners and stakeholders



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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

17 Agarwal, V., Reddy, N. J. K. & Anand, A. Unsupervised Representation Learning of
DNA Sequences. arXiv e-prints, arXiv:1906.03087 (2019).
<https://ui.adsabs. harvard.edu/abs/201 9arXiv 1 90603087A>.



Grant Training DCP Matrix

communication or reporting across
surveillance, laboratory, and other data
sources, epidemic intelligence, data analysis,
and field epidemiological and outbreak
investigation techniques, such as contact
tracing, and decision-making in support of
these activities.

6.1.3 Communication systems

Knowledge and proficiency with development,
implementation, and sustainment of
communications systems for outbreak
situational awareness and response, including
multisectoral and intrasectoral coordination
(including via emergency operations centers or
equivalent assets), operational risk
communication, and public risk
communication.

7. Biosafety

7.1 The application of
knowledge, techniques, and
equipment to prevent
accidental exposure to and
release of emerging and
high consequence
pathogens (EHCP).

7.1.1 Biohazard Identification and
classification

Knowledge, understanding and proficiency
with identification and classification of
biohazards associated with EHCPs, for
laboratories, hospitals, other biomedical and
clinical facilities, and the community, including
biohazard risk assessment and
characterization, and biohazard identification
policies and compliance procedures.

7.1.2 Biohazard Control Measures
(e.g., personal protective equipment,
safety practices and equipment,
facility design, and administrative
controls).

Knowledge, understanding and proficiency
with development and implementation of
international best practices and procedures for
EHCP biosafety and biohazard control
measures (substitution, elimination,
mitigation) for personnel, equipment, facilities
(laboratories, hospitals, and other biomedical
and clinical facilities) and the community.





Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Attachment 1 - Statement ef Work (SOW)
Project Title: Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Document Date: May 28, 2020

Objective

The project’s objectives are to determine the efficacy of machine learning (ML) techniques in
predicting epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats,
both intentional (e.g. novel genetically-modified pathogens) and natural (e.g. zoonotic).
Specifically, we will answer three questions that will enable rapid decision-making based on ML
models of epidemic impacts:

Q1: How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

Q2: What variables are most predictive of epidemic impacts at early stages of an outbreak?
Q3: At what point in epidemic growth do hybrid models that incorporate mechanistic
epidemiological components become more accurate than pure ML models?

This work will contribute to fundamental knowledge of properties and variables that contribute
most to predictive accuracy at early-stage stages in emerging outbreak prediction and
forecasting. It will enable the development of robust and deployable predictive models at early
stages of outbreaks, and support informed choices for data collection and model selection.

Scope

The awardee proposes a two-year study to determine the efficacy of ML techniques in predicting
epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats. The
awardee team shall focus on the following major goals and milestones:

e Year 1, Milestone 1 (6 months): Create and curate predictor database, project
infrastructure, continuous integration/validation, establish data-imputation and automatic
predictor workflow (Tasks 1.1-1.2).

® Year 1, Milestone 2 (12 months): Bayesian additive regression tree (BART) model fitting
and tuning, determination of variable contribution to prediction accuracy (Task 1.3)

@ Option Year, Milestone 3 (18 months): Maintain and document project infrastructure,
extend the database to include additional predictors, extend the BART model to ingest
genetic data (Tasks 2.1-2.3)

© Option Year, Milestone 4 (24 months): Construction of hybrid ML/mechanistic model
structure, comparative testing of pure and hybrid version, determination of timing when
current data overtakes transfer data (Task 2.4)

Background

Situational awareness and risk assessment are critical at early stages of disease outbreaks, when
interventions can best mitigate adverse outcomes. Yet decision-makers often face a paucity of
data at these stages. Traditional modeling methods to forecast disease impact severity rely
heavily on prior knowledge or assumptions about mechanisms of disease spread progression, and
they frequently do not anticipate the actual mechanisms.'? They also take considerable time and
effort to develop.


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Subject: RE: [Non-DoD Source] Re: FRBAA 14-6-2-0454
Importance: High

Good Afternoon Dr. Chmura,

T hope that you enjoyed a nice weekend. We have some additional clarification question to add to
our initial clarification request. Please let me know if you have any questions or concerns. Thank you in advance!

1. The proposal contains a 36.8% Indirect Rate for EHA however the latest NICRA for FY21 dated
April 1, 2020 has 35.84% from 7/1/20 to 6/30/21. Please provide some additional information to support the use of
36.8% or update the proposal, if needed.

2. Please provide quotes, website link, or basis of estimate to substantiate cost proposed for Luminex
machine in YI (EHA) and freezer in YI (KGMU)?


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Task 1.2 — Engineer Project Database: Data engineering consists of curating, integrating and
cross-linking and validating relevant data sets from various sources into a project database. This
allows data with different sources, space and time scales, formats, and completeness to be used
in common models establishes a database that can be used as a common resource.

Subtasks

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.3 Cross-link data sources via common ontologies

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data
cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location
information

1.2.6 Update data from live sources and clean and maintain data continuously

Task 1.3 -ML Medel Development and Testing: The core ML development task consists of
designing, implementing, testing, and analyzing performance of ML model structures and
iteratively improving them.

Subtasks

1.3.1 Implement “practical naive” model baseline

1.3.2 Design Bayesian additive regression tree (BART) model structure

1.3.3 Design alternative generalized additive model (GAM) structure

1.3.4 Implement within-disease, all-disease, and partial-pooled disease model versions
1.3.5 Measure variable contribution by re-fitting

1.3.6 Analyze prediction-level variable contributions and model performance

1.3.7 Iteratively update models to improve performance

OPTION YEAR:

Task 2.1 — Develop and deploy project infrastructure: In the option year we will continue to
perform maintenance and documentation of project codebase to ensure reproducibility and
reusability of additional components

Subtasks
2.1.1 Write and maintain codebase documentation
2.1.2 Perform code review, testing and validation

Task 2.2 — Engineer Project Database: In the option year we will extend the database to
include additional predictors and continuously maintain and update to accumulate more data
from upstream sources.

Subtasks

2.2.1 Extend database schema to include genetic data and auto-encoded variables

2.2.2 Write pipelines to ingest genetic data

2.2.3 Cross-link data sources via common ontologies

2.2.4 Write QA/QC tests for data and perform data cleaning


OMB Number: 4040-0001
Expiration Date: 12/31/2022

APPLICATION FOR FEDERAL ASSISTANCE 3. DATE RECEIVED BY STATE | State Application Identifier
SF 424 (R&R) | ll
1. TYPE OF SUBMISSION 4. a. Federal Identifier [

[_]Pre-application [XJApplication [“]Changed/Corrected Application | b. Agency Routing Identifier |

2, DATE SUBMITTED Applicant Identifier

[ ] [ Sacre eo

5, APPLICANT INFORMATION Organizational DUNS: [077 J
Legal Name: [ucolieal=h Alliance ]
Department: [ ] Division ]

Street: [4 34th Street |

Street2: [suite 2702

City [ fork ] County Parish ]

State: | NY: Mew York ] Province: | ]
Country: | USA: UNITED STATES ] ZIP / Postal Code: [19991-2317 ]

Person to be contacted on matters involving this application

Prefix (be, First Name: [cam | Midste Name: [—S—*dY

LastName: Rows ]  Suttix: |

Position/Title: [princi pa at, Camputationa: Research |

Street: [z

Steet2: [size 1701

City: [New vor ] County / Parish: | ]

State: [ NY: New < | Province: [ ]
Country: | USA: UNITED STATES ] ZIP‘ Postal Code: Znnn= 232% |
Phone Number: [2123594469 ] Fax Number: [z ]

Email: [ressfecchealthalliance erg ]
6. EMPLOYER IDENTIFICATION (Ef) or (TIN)
7. TYPE OF APPLICANT: | M: Nonpeolis with S012 IRS Suacus
Other (Specify): { ]
Small Business Organization Type ["] Women Owned _[[] Socially and Economically Disadvantaged

21172849

> than Ingiatucuon of Fig

> Educasionl

8. TYPE OF APPLICATION: If Revision. mark appropriate box(es).
DJ New  [_] Resubmission (DJA. Increase Award [] B. Decrease Award[_]C. Increase Duration [_]D. Decrease Duration
[_] Renewal [7] Continuation [_] Revision CIE. Other (specity):[ ]
's this application being subrvitied to other agencies? Yes[] No[<J_ What other Agencies? |
9, NAME OF FEDERAL AGENCY: 10, CATALOG OF FEDERAL DOMESTIC ASSISTANCE NUMBER: |1
Defense Threal. Reduclion Agency J] TILE: foziencific Reseavcl: Combacing Weapons of Mas

11. DESCRIPTIVE TITLE OF APPLICANT'S PROJECT:

Inredicning Biothreat Tmpas

From Rarly

Stage Dara via Transfer Learning

12, PROPOSED PROJECT: 13. CONGRESSIONAL DISTRICT OF APPLICANT
Start Date Ending Date
] [ ef 30/ 2022 ] [errata

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has: none
Investigator: Emma Mendelsohn

Support: 2 Current (1 Pending LJ Submission Planned in Near Future (_] *Transfer of Support
Project/Proposal Title:

Investigating Impact of Nutritional Deficiencies on Pandemics

Source of Support: Eleanor Crooks Foundation

Total Award Amount: $70,000 Total Award Period Covered: 05/01/2020 — 10/31/2020

Location of Project: Global

Person-Months Per Year Committed to the Cal: 2 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
REPEL: Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DHS

Total Award Amount: $566,274.26 Total Award Period Covered: 09/05/2019-09/04/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 6 Acad: Sumr:

Support: L] Current L] Pending LJ Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: C1] Current L] Pending LJ Submission Planned in Near Future (_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS

NECESSARY
AAI



LABOR Information Prime 1 (Fill-in)

Base Period | Option! | Option | Option | Option Iv Supporting Documentation Supporting
Rationale
Direct | Escalation | Escalation | Escalation | Escalation
Labor Category (Note 1) Name labor Rate | _ Rate Rate Rate Rate (Note 2) (Note 3)

Principal Scientist floss, Noam $55.29 | $58.05_| $0.00
Research Scientist Mendelsohn, Emma sa.a7_| 42.91 | 342.91
Senior Engineer Young, Rob $50.48_| $53.00 | $53.00
indirect Rates

Base Period| Option! | Option I Option iv Basis of Rate ‘Applied Against (Note 5) Suppor
Rate Category Rate Rate Rate Rate | (Example: FPRA, FPRP, Estimate, ete.)
Fringe Benefits 36.80%]  36.80%| 36.80% Estimate Direct labor costs
Labor Overhead
Material Handling
(General and Administrative 35.84%] 35.84%] 35.8406 Provisional (PROV) FFotal direct costs
Facilities Cost of Money
Note 1: Add additional labor categaries if needed.
Note Provide the location (section, page} in the propasal’s Cost Narrative/Supporting Documentation (BAA para 3.5.2.2.1} to support your price basis (e.g. copy of current payroll record or signed offer letter)
Note 3: Provide the location (section, page) in the proposals Tectinicel Basis of Estimate (BAA para 3.5.2.1.2) of the rationale for the appropriateness and necessity of the proposed lebor categories and the labor hours allocated tc

Note 4: ‘Add additional indirect rates as needed,

If the offeror does not have a Forward Pricing Rate Agreement (FPRA), Forward Pricing Rate Recommendation (FPRR), or provisional billing rates; in order to assist the Government in
evaluating the reasonableness of your proposed indirect rates, please provide the following information:

1. Performance data used to develop your proposed indirect rates, This typically consists of pool costs and base costs that demonstrate how the indirect rates were
derived.
2. Information regarding your projections for out years, including your assumptions and methad for developing these estimates.


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has: none
Investigator: Robert Young

Support: 2 Current (1 Pending LJ Submission Planned in Near Future (_] *Transfer of Support
Project/Proposal Title:

REPEL: Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DTRA

Total Award Amount: Total Award Period Covered: 04/09/20 19-03/09/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 6 Acad: Sumr:

Support: LJ Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:

Source of Support: DHS

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: L] Current L] Pending LJ Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: C1] Current L] Pending LJ Submission Planned in Near Future (_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS

NECESSARY
AAI



Identification and characterization of AI subtypes circulating among poultry across Jordan will
provide critical information needed to understand baseline activity and risks. If human AI
infections are detected, the project could identify causal factors in poultry-to-human influenza
transmission, and therefore inform interventions to prevent future primary cases. Given the
human AI infections in neighboring countries, this is a critical gap to be filled in Jordan.

Other zoonoses, such as Brucella spp. and Leptospira spp., also cause morbidity in Jordan, and
surveillance for them can be easily integrated into the research study as secondary outcomes. All
samples may be made available to other DTRA- or USG-funded researchers.

Scientific Impact for C-WMD Science: Given that transmission of high-consequence zoonotic
pathogens such as MERS and AI, whether by nature or ill intent, poses a significant threat to
global security, existing gaps in biosurveillance/detection capabilities, prevention/response
activities, and reporting weakens our ability to counter biological WMD threats. Over 60% of
emerging infectious diseases originate in animals, so it is imperative to strengthen local capacity
to conduct biosurveillance for zoonotic disease threats to understand existing baselines of
transmission, characterize risk factors to inform prevention activities that can interrupt
transmission, and preempt and/or rapidly respond to emerging disease threats.

Our primary objectives are to characterize causal factors in animal-to-human transmission
of MERS, AI, and other zoonoses in Jordan and te strengthen regional capacity for
detection of these pathogens in order to reduce the threat of infectious diseases. Our
proposed project will rigorously test the following initial hypotheses:
Hi. Livestock species and poultry in Jordan show evidence of infection with MERS, AI,
and/or other assayed zoonoses. (Output: Geospatial maps of naturally-occurring zoonotic
viruses and bacteria capable of infecting humans developed with and shared with
government authorities)
H2. Humans occupationally exposed to livestock and/or poultry have a greater risk of
infection with MERS, AI, and/or other assayed zoonoses compared to other community
members in Jordan. (Output: Geospatial risk maps developed with and shared with
government authorities)
H3. Some specific occupational practices that promote exposure to specific animal
biohazards (e.g., blood, urine, feces, and/or nasal secretions) pose a greater risk of
infection with MERS, AI, and other assayed zoonoses compared to others. (Output:
Enhanced understanding of modifiable risk factors to inform government authorities on
prevention activities)

Knowledge generated from this project will confirm the presence of MERS, AI, and other
zoonoses at human-livestock/poultry interfaces in Jordan, characterize specific
occupational practices as causal factors in human infection with these zoonoses, and
identify modifiable risk factors that may be used to inform prevention activities. The project
will build and boost local capacity for prevention, detection, and reporting of zoonotic disease
threats in Jordan, Iraq and Lebanon through regional cross-training workshops held in Jordan, all
of which have substantial camel and poultry production (Fig. 1). This will fill capacity gaps,
strengthen collaborative relationships, and foster goodwill in a politically fragile region,
enhancing our ability to combat biological threats.

EcoHealth Alliance 2


RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Senior/Key Person 2

Prefix: [ ] 7 First Name: [Enns ] Middle Name: [

* Last Name: [x | Suffix:[ ]

Position/Title: gientist Department: | ]
Organization Name: [Ecolicall: Rlliance ] Division: |

* sweet: [é3 Lh 8 ]

Street2 [seize 1c ]

“City: fnew ors ] County’ Parish: [ ]

“State: [wy: New Yoos ] Province: | ]

* Country: [11

UNITED STATES

* Zip / Postal Code:

* Phone Number:[21238644 50 ] Fax Number: [2123294465 ]

+ E-Mail: [nende_solingecohea: Chal Lance ang ]

Credential, e.g., agency login: ]

roh Scientist

* Project Role: [ocher Professional ] Other Project Role Category: [7

Degree Type: |

Attach Biographical Sketch [iese

] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support | ] [Delete Attachment | [ View Attachment |

PROFILE - Senior/Key Person 3

* First Name:[Peter ] Middle Name: | ]
] Suttix:[ ]
Position/Title: u Department: [ ]
Organization Name Health Alliance ] Division: [
*Streett: |46¢ Wess 240h Surees ]
Street2: |s.e 1761 ]
“City, rew yore County/ Parish: [ ]
*State: [yi new yore ] Province: | ]

* Country: [usa: UNTTRD STATRS

* Phone Number:[1212 2204466 Fax Number: [12123594445 ]

* E-Mail: [saszakSecohea_tha.-iance.org ]

Credential, e.g., agency login

szak ]

* Project Role:

] Other Project Role Category:

Senicr Adviser

Degree Type: [rit ]

Attach Biographical Sketch [1241 pascak_= l[ ] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support [1242 pascak_c6?.pd | | [Delete attachment | | View Attachment |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Y1 Accomplishments

Task 1: Determine the presence of MERS-CoV, Al, and other zoonoses amon
livestock andpoultry in Jordan.

Subtask 1.1 Identify sites for
project study

Activities and results this
year:

o Project staff reviewed site selection criteria and

collaborated with the team to produce a solution for .-
spatial sampling using a spatial sampling application.
This application was developed by an EHA scientist for
potential applications in studies that sample from mixed
human and animal subjects, and from the environment.

Project staff reviewed site selection criteria and with — Figure 1. Map of the study area and
input from local subject matter experts (SMEs). With villages possible to sample from
their input, the list of sites was narrowed down to

include villages with the three interface sites represented and sufficient human
population. Using the Shiny App, the team then randomly selected five sites in Jordan
from this narrowed down list.

Subtask 1.2 Obtain local permissions os . 4
andapprovals to work with animals

Activities and results this year:

fo)

The animal owner consent forms were finalized with
input

from local subject matter experts (SMEs) familiar with
sampling communities.

The JUST IACUC for research with animal subjects
was submitted and full approval was obtained.

JUST approval for use of the animal owner consent
forms

was obtained.

ACURO approved the animal IACUC and determined
the projects protocol was a surveillance project. No
further action is required.

The first draft of the animal sampling

protocol has been completed and is currently

being reviewed by partners.

Figure 2. Approval from JUST IRB

Biological Threats Reduction Program



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

William Karesh, DVM, Senior Vice President of EcoHealth Alliance will serve as project advisor
on veterinary topics as well as liaise with data-providing organizations, such as OIE and
ProMED, as required. Dr. Karesh is President of the Working Group on Animal Health and
COVID-19 Task Forces for OIE, Co-Chair of the Wildlife Health Specialist Group of the IUCN.

Facilities: EcoHealth Alliance is a 48-year old scientific research NGO that specializes in
multidisciplinary research and surveillance of the spread of zoonotic emerging diseases.
EcoHealth Alliance is based in New York City with 10,000 ft? of office space. The scientific and
technology staff of 30 are supported by a core administrative staff of 8 people who are available
for work on this project and are funded through core funds.

EcoHealth Alliance is equipped with fiber optic Internet access and video conferencing facilities
to facilitate easy communication between collaborators. EcoHealth Alliance employees have
around the clock access to servers, VPNs, encryption software, and IT support. We use standard
office suite services including Google Apps (Hosted email and web-based collaboration
software), Microsoft Office, and Slack, running on Apple macOS, Ubuntu Linux, and Windows
Operating Systems. EHA teams work in multiple computer languages (Python, NodeJS, and R
programming languages), development frameworks (Agile, continuous integration), and
machine-learning toolboxes. Our development operations are based on reproducible,
containerized frameworks (Ansible, Docker). We perform high-performance computation across
our dedicated cluster of System76 multi-core serversand with NVIDIA deep-learning GPUs and
spot cloud somputs instances through Amazon Web Services EC2 and Microsoft Virtual
Machines, We use a variety of other cloud services for storage (AWS S3, Azure Blob),
continuous integration (GitHub, Gitlab), and other tasks.

Work to be Performed
Tasks in Year. Task.Subtask format
YEAR 1:

Task 1.1 — Develop and deploy project infrastructure: Infrastructure engineering consists of
provisioning a best-in-class computational environment that enables continuous model
development and testing and allows for research and models to be reproduced and extended by
external teams. It includes continuous maintenance and documentation of project codebase.

Subtasks

1.1.1 Layout a provision project network

1.1.2 Set up continuous integration pipelines

1.1.3 Implement backup and data verification jobs
1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation
1.1.6 Perform code review, testing and validation



Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

o Ayesha Al-Kofahi, Research
assistant
o Nour Alhoda, Research as
o Graduate Students
o Dr. Yasmin Daradkeh
o Dr. Farah Al Nabulsi
o Undergraduate Students
o Leen Al Bayari
o Abdelrahman Talafha
o Royal Scientific Society
o Dr. Ghadeer Alzghoul
o Dr. Mu’men Alrwashdeh
o WHO
o Dr. Lora Al-Sawalha, One Health
Professional Officer
o EMPHNET
o Dr. Moad Rahamneh
© United Animal Health Inc.
o Dr. Nadim Amarin, marketing and
Technical Manager, META
© Private Sector Veterinarians
o Dr. Zaidoun Hijazeen
o Dr. Brittany Crook, AUS
o DTRA
o LTC. Todd Dahmann

Attendance for Day Two
o Ministry of Health

o Dr. Alaa Hamdallah
Dr. Saed Alnserat
Dr. Nizar Masouda
Dr. Akef Sleihat
Dr. Hayel El-Buriqi
Dr. Fatima Zerriouh
Ministry of Agriculture
Dr. Esam Hawa, Chief Veterinary
Officer
o Dr. Majed Hawaosheh

eooagqaoo|]d

Biological Threats Reduction Program

Dr. Samer Aldwairi
Dr. Khaldoon Malkawi
Dr. Hassan Al-Husainat
Dr. Marwan Awabdeh
Dr. Haitham Athamneh
Dr. Safa Atoom, Head of Microbiology
Unit
Ministry of Environment
o Eng. Nour M Alghazo, Climate Change
Directorate
o Noura Hamad Alshra’a, Climate Change
Directorate
o Abdelrahman Talafha
Royal Scientific Society
o Dr. Ghadeer Alzghoul
Jordan University of Science and Technology
Dr. Ehab Abu-Basha, Project Lead
Mr. Bilal Al-Omari
Dr. Mohammad Borhan Al-Zghoul
Dr. Omar Al-Omari
Eng. Hani Talafha
Ayesha Al-Kofahi
Nour Alhoda
Graduate Students
o Dr. Yasmin Daradkeh
o Undergraduate Students
o Leen Al Bayari
© Abdelrahman Talafha
WHO
o Dr. Lora Al-Sawalha, One Health
Professional Officer
o Olle Mjergura
EMPHNET
o Dr. Moad Rahamneh
Private Sector Veterinarians
o Dr. Zaidoun Hijazeen
o Dr. Brittany Crook, AUS

aooo000

eooagqao0o0a0


Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

Future Subtask(s):
Subtask 4.2 Generate risk maps with human behavioral risk factors

Task 5. Strengthen local capacity for detection and reporting of MERS-CoY¥, Al, and other zoonoses
in Jordan, Iraq, and Lebanon

Subtask 5.2 train local project staff in proper techniques
o Due to an increase in COVID-19 cases in Jordan, the third date for the in-person workshop
was finalized for May 27", 2021. The workshop included training on basic laboratory safety:
biosafety and biosecurity, facilitated by Dr. Mustafa Ababneh, and cold chain implementation,
facilitated by Dr. Borhan Al-Zghoul.
o Due to the COVID-19 group limitation,
7 people attended the third in-person
workshop:
= Dr. Mustafa Ababneh (JUST)
= Dr. Borhan Al-Zghoul (JUST)
= Yasmin Issa Daradkeh
(graduate student, JUST)
® Farah AlNabulsi (graduate
student, JUST)
= Abed Elrahman Talfha
(undergraduate student, JUST)
» Leen Al-Bayari (undergraduate
student, JUST) Figure 2. in-person workshop on basic
= Dr. Omar Al-Omari, MD laboratory safety.

o March 29, 2021: Dr. Ehab Abu-Basha reviewed the diagnostic laboratories in Al-Karak and
Ma’an to determine the limitations and status of veterinary services in the south of Jordan (the
report was a follow up to previous visits in 2017 and 2019).

o Recommendations are as followed:

* Complete laboratory building infrastructure with suitable benches, sinks, and
cabinets (Karak Lab)

= Hire adequately trained veterinarians and laboratory technicians to the
laboratories

» Provide adequate training on safety, proper use and maintenance of laboratory
equipment and chemicals.

= Provide adequate amount of laboratory diagnostic chemicals, supplies and kits.

® MOA should recruit staff to Ma’an and provide incentives for relocation.

Biological Threats Reduction Program Page 4 of 7


TofS

<mailto} Jaution-

Caution! >>
by6)

<mailt ‘aution-

Cautiot >>, Billy Karesh
<karesh@ecohealthalliance.org <mailto:-karesh@ecohealthalliance.org>
Caution-mailto: @ecohealthalliance.org > < Caution-Caution-mailto:karesh@@ecohealthalliance.org <
Caution-mailto:kareshi¢ecohealthalliance.org > >>, Whitney Bagge <bagge@ecohealthalliance.org
<mailto:baggeiecohealthalliance.org> < Caution-mailto:bagge@eecohealthalliance.org > < Caution-
Caution-mailto:bagge(uecohealthalliance.org < Caution-mailto:bagge¢ecohealthalliance.org > > >, Catherine
Machalaba <machalaba@ecohealthalliance.org <mailto:machalaha@eecohealthalliance.org> <

Caution-mailto: machalaba@éecohealthalliance.org > < Caution-Caution-mailto:machalaba@ecohealthalliance.org <
Caution-mailto: machalaba@éecohealthalliance.org > > >, Joe Riccardi <riccardi@ecohealthalliance.org
<mailto:riceardi@ecohealthalliance.org> < Caution-mailto:r
Caution-mailto:

ardig@ecohealthalliance.org > <Caution-
ardii@ecohealthalliance.org < Caution-mailto:riceardi@ecohealthalliance.org > > >

Please find attached seven files:

1) FRBAA14-6-2-0471_Clarifications_FINAL-xlsx
2) EHA Budget Justification_FINAL.docx

3) EHA Documentation_FINAL.pdf

4) Human Link Budget Justification_FINAL.docx
5) Human Link Documentation_FINAL.pdf

6) JUST Budget Justification_FINAL docx

7) JUST Documentation_FINAL.pdf

There are three sets of paired files with the requested documentation (PDFs) and track-change budget
justifications (MS Word) for EcoHealth Alliance and our two subcontracts under this proposal. We have also
included the clarifications (MS Excel) with responses to the specific questions in each tab.

Please let me know. if there are additional questions or any other documentation is required.
Many thanks!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

~1.212.380.4473 < tel: 1.212.380.4469 < tel: 1.212.380.4469 > > (office)

pa ] > (mobile)

Caution-Caution-www.ecohealthalliance.org <http:/caution-caution-wwwecohealthallianceorg’> <
Caution-bttp://caution-Caution-www.ecohealthalliance.org/ > < Caution-Caution-hitp./Avww ecohealthalliance org:
< caution-Caution-http.//www.ecohealthalliance.org! > >



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

REFERENCES

1

15
16

MacDiarmid, S.C. et al, Handbook On Import Risk Analysis for animals and animal
products: quantitative risk assessment. Vol. 2 (Office International des Epizooties, 2004).

Moura, J. A., McManus, C. M., Bernal, F. E. M. & de Melo, C. B. An analysis of the
1978 African swine fever outbreak in Brazil and its eradication. Rev. Sci. Tech. 29, 549-
$63, doi:10.20506/rst.29.3.1992 (2010).

Delgado, J. et ai. U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing
Controls. Risk Anal. 37, 1768-1782, doi:10.111 L/risa.12704 (2017).

Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat.
Commun. 10, 898, doi: 10.1038/s41467-019-08616-0 (2019).

Olival, K. J. ef a. Host and viral traits predict zoonotic spillover from mammals. Nature,
doi:10.1038/nature22975 (2017).

Allen, T. ef af. Global hotspots and correlates of emerging zoonotic diseases. Nat.
Commun. 8, 1124, doi:10.1038/s41467-017-00923-8 (2017).

Mock, F., Viehweger, A., Barth, E. & Marz, M. VIDHOP, viral host prediction with
Deep Learning. Bioinformatics (2019).

Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod
vectors from evolutionary signatures in RNA virus genomes. Science 362, 577-580,
doi:10.1126/science.aap9072 (2018).

Gu, Y. COVID-19 Projections Using Machine Learning __, <https://covid19-
projections.com/about#historical-performance> (2020).

Hill, J., Linero, A. & Murray, J. Bayesian Additive Regression Trees: A Review and
Look Forward. Annu. Rev. Stat. Appl. 7, doi:10.1146/annurev-statistics-03 1219-041110
(2020).

Wood, S. N. Generalized additive models ; an introduction with R. Second edition. edn,
(CRC Press/Taylor & Francis Group, 2017).

van Buuren, S. Flexible Imputation of Missing Data, Second Edition.
doi:10.1201/9780429492259 (2018).

Lundberg, S. M., Erion, G. G. & Lee, S.-I. Consistent Individualized Feature Attribution
for Tree Ensembles. arXiv e-prints, arXiv: 1802.03888 (2018).
<https://ui.adsabs. harvard.edu/abs/201 8arXiv 180203888L>.

Jones, K. E. et af. Global trends in emerging infections diseases. Nature 451, 990-993,
doi: 10. 1038/nature06536 (2008).

Shaw, L. P. e¢ af., doi:10.1101/670315 (2020).

Dobson, A. P., Brierley, L., Pedersen, A. B. & Woolhouse, M. E. J. Tissue tropism and
transmission ecology predict virulence of haman RNA viruses. PLOS Biology 17,
doi: 10.137 1/journal.pbio.3000206 (2019).


HDTRAI-14-24-FRCWMD-BAA
CBEP = Thrust Area 6 — Cooperative Counter WMD Research with Global Partners

India’s ability to meet IHR and OIE obligations and achieve Global Health Security Agenda (GHSA)
objectives by enhancing local capacity to identify natural spillover or deliberate release of known and
novel zoonotic agents. Early detection and prevention of spillover of high-risk viral pathogens in India, one
of the mast populous and globally connected regions of the world, will be critical to preventing local
outbreaks and global pandemics.

Scientific approach & methods. We propose to test the following overarching hypothesis:
Henipaviruses, filoviruses and coronaviruses are circulating in bats in rural India where people, bats and
livestock live in close proximity, and spillover has occurred undetected. This study will answer the
following questions: 1) Which bat species carry henipaviruses, filoviruses and high consequence
coronaviruses in India; 2) are there seasonal patterns to viral infections in bat reservoirs in rural
communities; 3) have other wildlife species (e.g. macaques) and livestock been exposed to these viruses;
and 4) does having livestock increase risk of exposure? Building on our work from PREDICT in Uttar
Pradesh, we will expand our human and animal surveillance where bat, human, and livestock
populations live in close association, We will work with local university, state, and federal institutions
using established and Indian Medical Research Council (ICMR)-approved sampling protocols developed
under PREDICT. We have already established relationships with communities in Gorakhpur, a region
with high rates of undiagnosed encephalitis and other illnesses. We will add additional communities in
other parts of UP to total six field sites: three communities that rear livestock (e.g. pigs, goats, cattle)
and three that do not. We will conduct quarterly sampling of wildlife (bats and macaques), livestock,
and people at each location over 30 months (or 48 months including option years), and use
questionnaires to collect data about how people interact with animals. We will collect ecological data
such as bat species diversity and abundance, terrain and vegetation, food cultivation and dietary
practices, and potential sources of environmental exposure to animals. Samples will be screened for
henipavirus, filovirus, and SARS & MERS coronavirus viral RNA (PCR) or antibodies (Luminex) at King
George’s Medical University. PCR protocols for viral families developed under PREDICT will be
established at KGMU. A validated, multiplexed serology assay (Luminex) that uses soluble glycoproteins
from all known henipaviruses and filoviruses, as well as spike proteins from SARS and MERS CoV has.
been developed by the Broder lab at Uniformed Services University and we have used them in several
previous and current research projects, including one funded by DTRA in Malaysia. We will also transfer
these assays to other Indian lab partners (e.g. NIV, Pune) to enhance their ability detect novel and
known members of these viral groups. We will compare viral detection rates and seroprevalence in
humans and animals among study sites and measure changes over time. Serosurveillance will be
especially informative to help identify bat reservoirs as these viruses cause acute, transient infections in
bats making viral detection more difficult than antibody detection. Using a One Health approach to
biosurveillance that includes people and livestock will also allow us to characterize virus and host
diversity within these viral groups and identify past spillover events and high-risk behaviors that may
have contributed to exposure.

We will report results from this project to relevant government stakeholders in human and animal
health sectors, which will help them fulfill their reporting requirements for IHR and OIE. This project also
supports the aims of the GHSA and we will present results at India GHSA meetings. This will also
facilitate coordination with the US CDC, as they also conduct human disease surveillance in UP. We will
publish findings from this study in high-impact, peer-reviewed journals and present at scientific
meetings. We have included an optional 4" and 5 year in which we will continue targeted surveillance
in UP, and also expand this project to Kerala state where a recent outbreak of Nipah virus occurred and
where our group has already established partnerships with state wildlife, livestock and human health
agencies through the state One Health platform.


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

trained on the same pathogen. For example, for Ebola outbreak predictions, all previous Ebola
outbreaks will be held out from training data.

Model Evaluation and Validation

Models will be fit and tested against their ability predict (a) size of an outbreak (total number of
cases), (b) peak size of outbreak (number of cases at highest point), (c) time to completion of an
outbreak, (d) time to peak of an outbreak, and (e) geographic spread of outbreak (distance to
farthest case from point of origin). Prediction scores will consist of mean-square-error of each of
these prediction against true values, as well as a score of equal-weighted errors of all five
measures. All models will also be tested for fidelity of uncertainty measures the fraction of true
data values that fall within prediction confidence intervals and these metrics will be reported
along with predictive performance.

All model performance scores will be compared against meaningful, “practical naive” baselines.
While it is common practice to use a “null” or “zero-knowledge” baseline, this often amounts to
inflating the apparent power of the algorithm. We will opt for a more realistic, “practical naive”
baseline, which instead follows what an analyst would approximate in practice. For these
models, we will use a practical baseline model of expecting outbreaks to follow the average size
and timing of outbreaks of the same disease in the past 25 years. All model scores will be
reported as percentage improvements over this baseline.

Algorithm performance will be measured against holdout data to validate true predictive
performance. Repeat testing on holdout data can lead to overfitting, with predictions too specific
to the hold-out set and not indicative of future performance.’ Yet increasing holdout reduces the
availability of training data. Thus, we will use a differential privacy approach drawn from
information security to validate prediction results. The hold-out data will be obfuscated, and
validation performance will be measured against bootstrapped replicates of validation data,
essentially introducing noise to reduce overfitting against the hold-out. This will ensure accurate
estimates of predictive performance in an operational environment.!°

Data Sources

Data sources for fitting and testing models include output data of number, timing, and geography
of disease outbreaks, and predictor data include pathogen traits (disease symptoms, pathogen
traits, modes of transmission), pathogen sources (point of origin, geographic range of potential
natural hosts), environmental context (climatic conditions, ecosystem traits), and socioeconomic
context (local population densities, demographics, transportation networks, healthcare systems).
Gathering predictor data is often as large a factor in the time required to create first-pass
predictions at the early stage of an outbreak. Thus, we will create automated pipelines for
gathering, merging, and updating these data sets, and to extract key predictors from basic
location and host variables to minimize manual inputs required. For instance, by linking to high-
resolution geospatial databases, we will automatically populate data for population density,


7 ~ I. THIS CONTRACT IS A RATED ORDER RATING PAGE OF PAGES
AWARD/CONTRACT UNDER PPAS (5 CFR 700) 1 | 10

2, CONTRACT ‘Peo. tro Helens NO, 3. EFFECTIVE DATE 4, REQUISITION/PURCHASE REQULST:PROJECT NO.

HDTRA 12010029 29 Sep 2020

3. ISSUED BY CODE [HDTRA1

ls. ADMINISTERED BY CODE|Naz78

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DEFENSE THREAT REDUCTION AGENCYIAL-AC.
8725 JOHN J. KINGMAN ROAD, MSC 6201
FORT BELVOIR VA 22060-6201

JOFFICE OF NAVAL RESEARCH-BOSTON
|495 SUMMER STREET, ROOM 627
BOSTON MA 02110-2109

7, NAME AND ADDRESS OF CONTRACTOR
ECOHEALTH ALLIANCE INC.

MR. ALEKSEI CHMURA_

460 W 24TH ST 17TH FL.

NEW YORK NY 10001-2317

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FORT BELVOIR VA 22060-6201

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16. TABLE OF CONTENTS
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PART IV - REPRESENTATIONS AND INSTRUCTIONS

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Grant Training DCP Matrix

control, disease
presentation and
distribution, and impact.

transmission, clinical presentation,
mechanisms of spillover, reservoir species,
spatiotemporal and geographic distributions of
affected populations, and demographic
characteristics of affected populations
(species/age/sex/occupational status/ etc.),
including outcomes on population health.

1.1.2 Clinical Infection control and
animal health biosecurity

Understanding of basic infection prevention
and control procedures related to especially
high consequence pathogens (EHCPs) for
human and animal clinical settings in line with
GHSA and IHR milestones (when applicable),
including standard precautions {PPE use;
disinfection; sterilization); environmental
infection control; laboratory testing;
nosocomial infections, including AMR; and
animal health biosecurity principles,
procedures.)

2. Disease
identification,
detection,
confirmation, and
reporting

2.1 Positive identification of
emerging and high
consequence pathogens
(EHCP) and reporting to
appropriate authorities.

2.1.1 Sample collection

Knowledge, understanding and proficiency
with procedures for safely and securely
collecting appropriate specimens/samples for
testing of pathogens (human; animal;
environmental), especially EHCPs, and
handling and packaging of samples for
transport to diagnostic testing laboratories,
based on national and international guidelines
and best practices, while ensuring specimens
will are maintained in condition required for
testing (cold chain and or fixation, etc.)

2.1.2 Test procedures
(presumptive/confirmatory)

Knowledge, understanding and proficiency
with procedures for conducting screening,
diagnostic, and confirmatory testing and
interpreting test results for ECHPs from



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

statement of work will benefit from the significant experience of the project staff and in-kind
contribution from other EHA personnel with combined expertise in global emerging infectious
disease epidemiology, public health, veterinary medicine, machine learning, and biostatistics.
We also focus on deplovment-ready research infrastructure to minimize the need to re-engineer
models developed in the research process for applied use.

We engage multiple best practices to minimize project disruption risk. Personne/ risk entails loss
of expertise and project or institutional knowledge due to loss of core project staff. EHA
maintains project data, documentation, and code in common cross-team repositories. Version-
controlled project repositories ensure that project histories are tracked, and all team: members can
retrieve previous work by others. Through regular code review, we ensure all team members
have working knowledge of project modules and the ability to re-task. Infrastructure risk entails
loss of project infrastructure such a. EHA performs on-site high-performance computing cluster
or data storage. We keep code, data, results and documentation on-site only for active
experiments, and back up these to off-site cloud infrastructure. Our compute environment is
reproducible, being configured by pre-programmed, self-documenting methods such as
Dockerfiles, Ansible playbooks, and Bash scripts. It can be re-deployed to off-site cloud
resources or new hardware as needed.

Relevance

Our research will fundamentally improve infectious disease forecasting by identifying the
relative importance of early-stage factors in outbreak prediction. While pathogen-host-
environment linkages are fundamental in disease modeling, predictive methods developed to
assess emerging biothreats usually focus on each individually. We will also advance knowledge
of hybrid ML/mechanistic models, which have not yet been used in applied disease forecasting.
Together this research will impact C-WMD science by providing practical guidance for early-
stage data-gathering and prototyping an interactive decision support tool for biothreat impact
forecasting.

Credentials

EcoHealth Alliance (EHA) will implement this project, with Noam Ross, Principal Scientist for
Computational Research, as project lead. EHA’s mission is focused on emerging diseases and
cutting-edge research into the critical connections between human, wildlife, and ecosystem
health that drive disease emergence and spread.

PI: Dr. Ross will direct he project. He is a computational disease ecologist who provides
expertise in dynamic epidemiological processes, machine learning and biostatistics, and high-
performance computing. He has led work in ecological and epidemiological modeling and data
management for EHA’s work on USAID PREDICT and NIH NIAID viral surveillance research.
He has experience developing livestock disease outbreak and spread simulations in collaboration
with FAO as well as private industry. Dr. Ross is PI for EcoHealth’s work with DHS on machine
learning for veterinary disease outbreaks. He also a leader in open-source scientific computing.

8


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

pure ML model, excluding early time-series data. The model be structured to have high
flexibility under small number of parameters — i.e. SIR-type model with variable contact
structure and latent/infectious periods. This model will then be fit to the time series data for the
outbreak under the constraint of these prior parameters. Using this approach (see figure), we will
determine whether the structural knowledge embedded in the epidemiological model improves
outbreak predictions or hinders them due to assumptions. As with the pure ML approach, we
will test the hybrid model approach under within-disease, all-disease, and partial-pooled
approaches.

Pathogen Data: Source Data: Environmental Socioeconomic Current
symptoms, point of origin, Data: climatic Data: transport, Outbreak
transmission reservoir host conditions, health care Dynamic |”
mode, genome, ... species, ... ecosystem, .. system ... Data
l l J |
Data from . ===
previous Hierarchical Partial =—
outbreaks

Tree-based machine-learning algorithm aan

Pure ML Hybrid t '

Approach Approach
a ~

All
Approaches

Figure: Schematic of machine-learning approaches. ML algorithm ingests biothreat condition data
and makes predictions based on training from previous outbreak data. In pure ML approach, current
outbreak dynamics are used as ML predictors of outbreak impacts. In hybrid approach, ML algorithm
predicts parameters of an epidemiological model, which predicts impacts using parameters and
current outbreak dynamics.

Outbreak
Impact Pedictons: |~«--
Size, Duration

Pooling '

--1 Epidemiological
1 Model

Model variables will be analyzed at both model-wide and individual case level to determine their
contribution to performance. First, to evaluate model-wide predictive performance, we will re-fit
models successively dropping each predictor to determine which provide the greatest
improvement in performance. At the individual case level, we will calculate Shapley Additive
Explanation (SHAP) scores" for all variables. This will allow for inspection of the role of
variables across major categories (pathogen type, continent), and determine how accuracy varies
across these contexts. Clusters of similar SHAP scores will also allow us to identify clusters of
diseases or conditions that have similar behavior or performance accuracy. Such information
will ultimately support decision-making in selecting models or variables to select for prediction
based on the context of new outbreaks.

To measure the capacity of algorithms to predict new, emerging diseases, we will score using
structured cross-validation in which no disease outbreak prediction is based on an algorithm


Biological Threats Reduction Program
PROJECT QUARTERLY REPORT

PROJECT INFORMATION

HDTRA12010029 - Reducing the Threat of Middle

East Respiratory Syndrome Coronavirus and Avian

Influenza in Jordan & Strengthening Regional Disease

Surveillance Capacity

Quarter and Dates: Y1,Q1 l (9/29/20 - 12/28/20)
Prepared by: | Dr. William B. Karesh

Project and Title:

OVERALL PROJECT SUMMARY AND OBJECTIVES

Project Objective

Utilizing an international consortium of partners, this effort is a multi-disciplinary One Health
research project to identify causal factors in the transmission of MERS-CoV, AI, and other zoonoses in
Jordan, while simultaneously strengthening local capacity for prevention, detection, and reporting of
these zoonoses in Jordan, Iraq, and Lebanon. Our proposed project will rigorously test the following
initial hypotheses: 1) That livestock species and poultry in Jordan show evidence of infection with
MERS-CoV, AI, respectively and/or other zoonoses. 2) That people regularly exposed to livestock
and/or poultry or their living areas have a greater risk of infection with MERS-CoV, AI, and/or other
zoonoses compared to other community members in Jordan. 3) That some specific practices may
promote exposure to specific animal biohazards (e.g., blood, urine, feces, and/or nasal secretions) and
pose a greater risk of infection with MERS-CoV, AI, and other zoonoses compared to others. This study
involves interviews and longitudinal sampling of humans, livestock, and poultry at animal markets and
farms for MERS-CoV, Brucella spp., and Leptospira spp. (people/livestock) and for AI subtypes
(people/poultry). Partner laboratories in Jordan will screen and characterize these zoonotic pathogens,
and capacity-building cross-trainings will be conducted with visiting laboratory scientists from Irag and
Lebanon. This multifaceted One Health approach will advance scientific knowledge on the risk of
MERS-CoV, AI, and other zoonoses in Jordan, strengthen local zoonotic disease surveillance
capabilities, and enhance scientific partnerships in Jordan, Iraq and Lebanon to prevent, detect, and
teport biological threats.

Biological Threats Reduction Program Page 1 of 6


Noam Rass
EcoHealth Alliance, 460 W 34" St, New York, NY 10001
E-mail: ross@ccohcalthalliance.org

Professional Preparation

Brown University Environmental Science BS. 2006
University of California-Davis Ecology Ph.D. 2015
Appointments

Principal Scientist, Computational Research, EcoHealth Alliance 2020 - present
Senior Research Scientist Ecologist, EcoHealth Alliance 2016 - 2019
Disease Ecologist, EcoHealth Alliance 2015 - 2016
Graduate Researcher, University of California-Davis 2010 - 2015
Senior Analyst, Environmental Strategy and Markets, GreenOrder 2007 - 2009
Analyst, Environmental Strategy and Markets, GreenOrder 2006 - 2007
Contract Researcher: Energy Efficient Products Initiative, Wal-Mart 2006
Publications

Albery, G.F., Eskew, E.A., Ross, N. et al. Predicting the global mammalian viral sharing
network using phylogeography. Nature Communications 11, 2260 (2020).

Carlson CJ, Albery GF, Merow C, Trisos CH, Zipfel CM, Eskew EA, Ross Net al. Climate
change will drive novel cross-species viral transmission. bioRxiv, Cold Spring Harbor
Laboratory; (2020)

Eskew EA, White AM, Ross N, Smith KM, Smith KF, Rodriguez JP, et al. United States wildlife
and wildlife product imports from 2000 2014. Scientific Data 16;7(1).

Li, H., Mendelsohn, E., Zong, C., Zhang, W., Hagan, E., Wang, N., ... Ross N., ... Daszak, P.
(2019). Human-animal interactions and bat coronavirus spillover potential among rural
residents in Southern China. Biesafety and Health, 1(2), 84 90.

Ross, N., Eskew, E., & Ray, N. (2019). citesdb: An R package to support analysis of CITES
Trade Database shipment-level data. Journal of Open Source Software, 4(37), 1483.

Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized
additive models in ecology: an introduction with mgcv. PeerJ, 7, 6876.

Carlson CJ, Kracalik I, Ross N, Alexander K, Hugh-Jones ME, Fegan M, Elkin B, Epp T, Shury
T, Bagirova M, Getz WM, Blackburn JK. (2019). The global distribution of Bacillus
anthracis and associated anthrax risk to humans, livestock, and wildlife. Nature
Microbiology. 4(8):1337-1343 a

Rostal MK, Ross N, Machalaba C, Cordel C, Paweska JT, Karesh WB. (2018) Benefits of a One
Health approach: An example using Rift Valley fever. One Health 5:34-36.

Olival KJ, Hosseini P, Zambrana-Torrelio C, Ross N, Bogich T, Daszak P. (2017) Host and viral
traits predict zoonotic spillover from mammals. Nature 546:646 650.

Salerno J, Ross N, Ghai R, Mahero M, Travis DA, Gillespie TR, Hartter J. (2017) Self-reported
fever associated with human-wildlife interactions across park landscapes in western Uganda.
EcoHealth 14(4):675-690.

Schreiber 8, Ross N. (2016) Individual-based integral projection models: The role of size-
structure on extinction risk and establishment success. Methods in Ecology and Evolution
7(7):867-74.

Boettiger C, Ross N, Hastings A. (2013) Early warning signals: The charted and uncharted
territories. Theoretical Ecology 6(3):255-64.

1


From:

To: GY

Ce: Whitney Bagge; Joe Riccardi; Billy Karesh
Subject: Re: [Non-DoD Source] FRBA14-6-2-0471 Clarifications

Date: Thursday, September 17, 2020 11:38:05 AM

Attachments: DIRA Jordan Budaet 28 Aug 2020.xlsx

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

DearfX®)
Please find our excel file with a breakdown of EcoHealth Alliance costs and with separate tabs for our subcontracts.
Call me anytime, if you have additional questions or need other documents.

Many thanks!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 > (office)
pe } (mobile)
Caution-www.ecohealthalliance.org < Caution-http./www ecohealthalliance org! >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Sep 14, 2020, at 11:23,f616)
pa [wrote

Good morning.

Thank you for documents as they will come in handy, Would you be able to provide an excel breakdown of
EHA and subs?

Original Messag
From: Aleksei Chmura <chmura@ecohealthalliance.org <mailto:chmura(ecohealthalliance,org> >
Sent: Wednesday, September 2, 2020 12:34 PM

Tofye)



require transport to the airport $150 RT is included. In total, we are requesting $1,576 in Y1 and
$5,996 in OY 1 for domestic travel

International Travel

One international trip for two team members to present at the EEID conference held in
Montpelier, France, in 2021 requested for Y1. Flights are estimated at $1500 and the federal per
diem rates of $196 for lodging and $155 for meals in Montpelier. $25 per day is estimated in taxi
costs plus $150 RT trips to airport is included. In total we are requesting $7,088 in YL.

E. Participant/Traince Support Costs
No funds are requested for participant or trainee support costs.

F. Other Direct Costs

Materials and Supplies

PI Ross and Research Scientist Mendelsohn will both need a new computer, equipped with
Microsoft software for which we request a total of $4,000, in only Y1.

Cloud Computing and Storage Services

We will make use of cloud computer services including spot server rental for compute-intensive
jobs, data storage, continuous integration and testing of computer code. We request $2,000 per
year in Y1-OY1.

Team Communication and Collaboration Software and Services
We request $1,000 per year for subscriptions for software and services for document sharing,
chat, voice, and video communications.

Publication Costs
To facilitate the dissemination of project findings, $1,500 per year in only Y1-OYI is requested
for open access publication costs in international peer-reviewed journals.

H. Indirect Costs

We are requesting the EcoHealth Alliance federally approved indirect cost rate of 35.84% on all
applicable direct costs. The USA Department of Defense’s Department of the Navy has approved
this rate on 01 April 2020. We request S $64,986.52 in Y1 and $65,538.46 in OY1.


THER DIRECT COSTS (Note 1)

Prime 1 (Fi

Note 2) Qty Unit of Issue Unit Price Total Price
Base so
Publication costs Publication $1,500 $1,500
Cloud Services and Storage Annual $2,000 $2,000
Communication and Collaboration Services & Annual $1,000 $1,000
Option | so
Publication costs Publication $1,500 $1,500
Cloud Services and Storage Annual $2,000 $2,000
Communication and Collaboration Services & Annual $1,000 $1,000

Instructions:

Note 1:
Note 2:

+ If you have any ODC entries, be sure to fill out all columns.
+ Ensure that descriptions and vendors listed on the ODC Details works

Examples include rental fees, shipping costs, license fees

Include a separate section in the above table for the Base and eact


Peter Daszak
EcoHealth Alliance, 520 Eighth Avenue, Suite 1201, New York, NY 10018, USA
E-mail: daszak@ecohealthalliance.org

Professional Preparation

Bangor University, UK Zoology BS 1986

Univ. of East London, UK Infectious Diseases PhD 1993
Appointments

President & Chief Scientist, EcoHealth Alliance New York 2009- present
Executive Director, Consortium for Conservation Medicine 2001- 2009
Faculty Research Scientist, University of Georgia 1999- 2001
Guest Researcher, Centers for Disease Control and Prevention (CDC) 1999

Faculty Research Scientist, Kingston University, UK 1993- 1998
Research Assistant, University of East London, UK 1989- 1992
Publications

Nikolay B, Salje H, Hossain MJ, Khan AKMD, Sazzad HMS, Rahman M, Daszak P, Stréher U,
Pulliam JRC, Kilpatrick AM, Nichol ST, Klena JD, Sultana 8, Afroj $, Luby SP, Cauchemez S
& Gurley ES. (2019). Transmission of Nipah Virus - 14 Years of Investigations in Bangladesh.
New Engl J Med 380:1804-1814

Carroll D, Daszak P*, Wolfe ND, Gao GF, Morel C, Morzaria S, Pablos-Méndez A, Tomori O,
Mazet JAK (2018). The global virome project. Science 359: 872-874

Zhou P, Fan H, Lan T, Yang X-L, Shi W-F, Zhang W, Zhu Y, Zhang Y-W, Xie Q-M, Mani 8,
Zheng X-S, Li B, Li J-M, Guo H, Pei G-Q, An X-P, Chen J-W, Zhou L, Mai K-J, Wu Z-X, Li
D, Anderson DE, Zhang L-B, Li S-Y, Mi Z-Q, He T-T, Cong F, Fuo P-J, Huang R, Luo Y, Liu
X-L, Chen J, Huang Y, Sun Q, Zhang X-L-L, Wang Y-Y, Xing S-Z, Chen Y-S, Sun Y, Li J,
Daszak P*, Wang L-F*, Shi Z-L*, Tong Y-G*, Ma J-Y* (2018). Fatal Swine Acute Diarrhea
Syndrome caused by an HKU2-related Coronavirus of Bat Origin. Nature 556: 255-258

Olival KJ*, Hosseini PR, Zambrana-Torrelio C, Ross N, Bogich TL, Daszak P* (2017). Host and
viral traits predict zoonotic spillover from mammals. Nature 546, 646 650

Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, Breit N,
Olival KJ, Daszak P* (2017). Global hotspots and correlates of emerging zoonotic diseases.
Nature Comm 8: 1124

Ge X-Y, Li J-L, Yang X-L, Chmura AA, Zhu G, Epstein JH, Mazet JK, Hu B, Zhang W, Peng
C, Zhang Y-J, Luo C-M, Tan B, Wang N, Zhu Y, Crameri G, Zhang S-Y, Wang L-F, Daszak
P*, Shi Z-L* (2013). Isolation and characterization of a bat SARS-like Coronavirus that uses
the ACE2 receptor. Nature 503: 535-538

Daszak P*, Zambrana-Torellio C, Bogich TL, Fernandez M, Epstein JH, Murray KA, Hamilton
H (2013). Interdisciplinary approaches to understanding disease emergence: The past, present
and future drivers of Nipah virus emergence. PNAS 110: 3681-3688

Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, Wang H, Crameri G, Hu Z, Zhang H, Zhang J,
McEachern J, Field H, Daszak P, Eaton BT, Zhang $8, Wang L-F. (2005). Bats are natural
reservoirs of SARS-like coronaviruses. Science 310: 676-679

Daszak P*, Cunningham AA, Hyatt AD (2000). Emerging infectious diseases of wildlife -
threats to biodiversity and human health. Science 287: 443-449



Grant Training DCP Matrix

policies and procedures for disease
notification and/or bi-directional reporting of
surveillance and laboratory data through
national information-sharing systems, or to
notify regional/international authorities such
as WHO/OIE/FAO of a disease, other
significant epidemiological event, or potential
public health emergency of international
concern (as appropriate to roles and
responsibilities at each operational level).

3. Analytics,
assessment, and
research

3.1 Appropriate study
design, data analysis, and
implementation of
processes, as applies to
each discipline,

3.1.1 Study design and ethics

Knowledge and understanding of scientific
methodologies and study design principles
(hypothesis development, research plan
development, sampling design, strategy for
data collection and analysis) and application of
research ethics to responsibly address safety,
security, and ethical issues related to the
research aims and hypotheses, including
understanding of issues around dual-use
research of concern.

3.1.2 Methodologies and analysis

Knowledge, understanding and proficiency in
the use of statistical and other relevant
methodologies for scientific research analyses
including practices and approaches for data
collection, use and interpretation of statistical
data analyses, data visualization, and use of
statistical data software tools.

3.1.3 Dissemination of findings and
outreach to stakeholders

Knowledge, understanding and proficiency in
the dissemination of research findings through
peer-reviewed scientific journal publications,
conference presentations and public outreach



Cooperative Biological Engagement Program (CBEP} = Thrust Area 6 — Cooperative Counter WMD
Research with Global Partners

Biosurveillance for Spillover of Henipaviruses, Filoviruses, and Coronaviruses in Rural India

Abstract: The henipaviruses and filoviruses include Hendra virus (HeV) and Nipah virus (NiV), and
several species of Ebola (EBOV) and Marburg virus (MARV), respectively, which are highly pathogenic
viruses and select agents capable of causing public health emergencies of international concern. Severe
acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) are zoonotic
coronaviruses associated with global spread. In 2018, the WHO listed these pathogens among the top
ten threats to global health and security. In Africa, Ebola viruses have caused significant morbidity and
mortality in people and wildlife, and Saudi Arabia and the Gulf states continue to have new cases of
MERS CoV, some of which are associated with dromedary camels. In 2014, a single spillover event of
Ebola Zaire from an animal host (presumed to be a bat) caused an epidemic in West Africa that infected
more than 28,000 people, killing approximately 40%. Currently, there is an Ebola outbreak in an
insecure region of DRC which threatens to spread to Uganda. In Bangladesh and India, Nipah virus has
caused recurrent, seasonal outbreaks of acute encephalitis in people and has an associated mean
mortality rate of 70%, including the recent outbreak in May 2018 in Kerala, India. Hemorrhagic fever and
acute encephalitis both occur in India, but surveillance is limited and most cases remain undiagnosed.
Bats are reservoirs for henipa- corona-, and filoviruses and zoonotic transmission of these viruses from
bats to humans either directly or via domestic animals has previously occurred in Southeast Asia.
Despite their importance as threats to public health and security, the full diversity of these viruses in
bats and their potential to infect livestock and people remains poorly understood. The recent Nipah
virus outbreak in southern India and previous findings of Nipah in bats in northern India illustrates that
Nipah virus circulates in bats throughout the country and spillover is possible where bats and people
interact. In line with Thrust Area 6 — Cooperative Weapons of Mass Destruction Research with Global
Partners, this project will reduce threat from these select agents and viruses of high concern by
enhancing early detection and surveillance capacity in India by: 1) transferring Luminex technology with
validated reagents to detect IgG antibodies against all henipaviruses, filoviruses, and MERS and SARS
CoVs to key Kerala state and government of India partner labs in animal and human health sectors; 2)
training laboratory personnel to develop and utilize Luminex assays to identify human and animal
exposure to henipa- filo- and CoVs; 3) conduct biological surveillance in bats, nonhuman primates,
livestock and people in rural communities in Kerala and UP, where there are high levels of contact
among people and animals. This project will test the hypothesis that henipaviruses filoviruses, and
coronaviruses are circulating in bats in southern India and have spilled over into animal and human
populations undetected, which presents a threat to human health. Activities will be coordinated with
the Global Health Security Agenda and surveillance data will be shared with the government partners in
Kerala and the Government of India (Gol). The proposed project is closely aligned with the aims of the
Cooperative Biological Engagement Program in that it will support threat reduction through
biosurveillance and capacity building, engagement with and implementation by partner-country
scientists, and use of a One-Health approach.

Point of Contact & Principal Investigator: Dr. Jonathan Epstein. Epstein@ecohealthalliance.org

Ce: Ms. Emma Lane. lane@ecohealthalliance.org




Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

Figure 12. Group photo of the participants from Figure 13. Certificates given out at
the August training workshop. the end of day two after the completion
of the workshop.
Subtask 5.6 Data Munugement
o On August 8", the project staff met at the Princess Haya Biotechnology Center at Jordan
University of Science and Technology to review the data management system AirTable
and discuss any concerns prior to starting sampling.

o The team decided to use a general
QR code and have team members
select their name from a list in the
administration section of the
survey.

o The team created validation
reports for the field data and lab
data. The data validation reports
will help the field team check for
extreme values or any data entry
errors when the project team
inputs data into AirTable or ODK

surveys. eo . .
Figure 14. On August 8, 2021, the team
Subtask 5.7 Conduct prexentationsémectings ut gathered at JUST University, Jordan to
times and places specified discuss AirTable and data management.

o On September 9, 2021, Dr. Ehab Abu-
Basha conducted a meeting with both field and lab teams and the following activities
were undertaken:

o Responsibilities and tasks such as sample collection, transportation, sample
identification and labeling, receiving, and processing of samples in the lab and
questionnaire administration were described and identified for each member of
each team.

o SOPs previously prepared were reviewed and the human and animal team were
instructed to strictly follow the SOPs. A workshop to train team members how to
execute each task according to SOP is set to take place in the coming weeks.

Biological Threats Reduction Program 9


RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Senior/Key Person 4

Pretix:[>e.

Position’Title: [::

* Street: [é2

“state: [wy: New Yors

] * First Name:[itty ] | Middle Name: |
* Last Name: [xar ]  Sutfix:[ ]
ative VP fer Healtz and Policy Depariment:|
Organization Name: [Ecolicall: Rlliance ] Division: |
2 34h Bt ]

Street2: [sz 17¢1 ]

“City: fnew ors ] County’ Parish: [ ]
] Province: | l

* Country: [11

UNITED STATES

* Zip / Postal Code:

* Phone Number:[12134 86

] Fax Number: [12325324465 ]

*E-M

[kavesh@econeal thal lance org ]

Credential, e.g., agency login: |wkares ]

* Project Role: [ocher profe

onal ] Other Project Role Category:

Senicr Adviser

Degree Type:

Degree Year:

Attach Biographical Sketch
Attach Current & Pending Support [

l[ ] [Delete Attachment | [ View Attachment |
I

] [Delete Attachment | [ View Attachment |

Tracking Number:GRANT13118386

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT



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Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: William Karesh

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:
Crimean-Congo hemorrhagic fever: Reducing an emerging health threat in Tanzania

Source of Support: DTRA

Total Award Amount: $4,848,483 Total Award Period Covered: 10/01/2019 - 07/31/2024

Location of Project: USA, Tanzania

Person-Months Per Year Committed to the Cal: 1.0 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Reducing the threat of Rift Valley Fever through Ecology, Epidemiology and Sacio-Economics

Source of Support: DTRA

Total Award Amount: $4,988,526 Total Award Period Covered: 06/01/2019 - 05/31/2024

Location of Project: USA, South Africa

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: C1] Current XX] Pending C1 Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Understanding filovirus diversity in bat reservoirs and implications for human outbreaks in West Africa

Source of Support: NIH

Total Award Amount: $7,307,869 Total Award Period Covered: 03/01/2020 - 02/28/2025

Location of Project: Global

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: C1] Current XX] Pending C1 Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Reducing the Threat from high-risk pathogens causing febrile illness in Liberia

Source of Support: DTRA

Total Award Amount: $4,912,818.06 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Liberia

Person-Months Per Year Committed to the Cal: 1.5 Acad: Sumr:

Support: LJ Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY

3


Section B - Supplies or Services and Prices

ITEM NO
001

ITEM NO
OOOLOL

SUPPLIES/SERVICES = QUANTITY
1

Years 1-3: FRBAA14-6-2-0471

FFP

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian
Influenza in Jordan & Strengthening Regional Di:
accordance with the following atlachements: SOW at atlachment | dated 3/18/2020

UNIT

Lot

UNIT PRICE,
$2,957,164.19

¢ Surveillance Capacity. In

and DTRA Terms and Conditions for Grant Awards dated 09/14/2020 at

atlachment 2
FOB: Destination
u009

SUPPLIES/SERVICES QUANTITY

Funding to support CLIN 0001
FFP

ACRN AA
CIN: HDTRA10361120001

UNIT

NET AMT

UNIT PRICE

NET AMT

HDTRAI2010029

Page 2 of 10

AMOUNT
$2,957, 164.19

$2.957.164.19

AMOUNT
$0.00

$0.00

$2,956,308.78


Quarterly Report: HDTRA 12010029 - Year 1, Quarter 1 (9/29/20 - 12/28/20)

o A date and preliminary schedule for the kick-off meeting was set.

o Coordinated with the Jordan Armed Forces health official, including organizing an upcoming
briefing meeting on the project. We anticipate this will be a positive collaboration for the
project, expanding key stakeholders to promote synergies with existing biodefense/health
security activities and provide a forum to share One Health approaches that could potentially
be sustained by Ministry of Health.

Subtask 5.6 Data Management oe
o Software, systems, and workflow was collection tleaning

determined for data management (see Figure een < Ropers yan, tures

3). These will be tailored to outputs for 7

Jordanian partners (e.g., reports that can be
shared at the monthly national One Health
platform meeting convened by Ministry of
Health, as relevant). Figure 3. Summary of data management
software and data flow for this project.

Future Tasks

Subtask 5.2 Train local project staff in proper techniques

o All team members will be equipped with CITI training and the team will continue planning
future training to understand research and data security, and survey tool development

Subtask 3.3 Host annual partners and stakeholders meeting

Subtask 3.4 Complete annual report and share with partners and stakeholders

Subtask Conduct locul-regional training workshops

Subtask 3.7 Conduct presentations/mectings af times and places specified

Tasks to be initiated after Year 1:

Task 6. Identify specific modifiable risk factors for human infection with these zoonoses

Presentations and Publications

1, Presentations and Other Conlerence Submissions: All Institutes

* Developed slide set to present project concept for wider awareness of an applied One
Health approach for threat reduction.

2. Publications: All In es

No project scientific publications to report as of yet. However, the project team developed a project
summary sheet for outreach to stakeholders in Jordan (as shown below).

Biological Threats Reduction Program Page 4 of 6


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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

He is a founding editorial board member of rOpenSci, an organization that defines standards for
reproducibility and runs peer review for scientific software, and is a core contributor to the
Rocker project, which provides open-source, standardized computing containers for data science.
Dr. Ross holds a Ph.D. in computational ecology from the University of California-Davis.

Organization: EHA has a proven track record of large project management with U.S.
government agencies. We lead the largest global country portfolio, and serve as lead for
Modeling and Analytics and Behavioral Risk Analysis teams, for the USAID Emerging
Pandemic Threats PREDICT program, a $118 million program focused on predicting and
preventing pandemic diseases. The PREDICT project is building a global early warning system
to detect and reduce the impacts of emerging diseases that move between wildlife and people
(zoonotic diseases). EHA also leads multiple field epidemiological contracts funded by DTRA,
NIH, and NSF.

EHA has proven expertise in surveillance-oriented technology development. We managed a five-
year contract to develop applications for DTRA's Chemical Biological Integration Platform
(CBIP, formerly the Biosurveillance Ecosystem BSVE), as well as the development of the
Inbound Bioevent Information System (IBIS), and Rapid Evaluation to Prevent Epidemics in
Livestock (REPEL) project under DHS’s Analytics for Prevention of Biothreat Incidents (APBI)
program and Science and Technology Directorate. Through this work, EHA scientists have
gained substantial expertise in both basic research and working with government project
managers and analysts to develop technologies that are in line with the goals of the funding. The
expertise resulting from this effort serves as the foundation for the current proposal.

Team: Robert Young, Senior Research Software Engineer, will lead project infrastructure
development and the creation ML pipelines. Mr. Young has a background in artificial
intelligence and bioinformatics. He has developed and managed laboratory information systems
in neuroscience, genomics and performed research in neural-network design for image-
recognition in astronomy. Mr. Young currently leads cyperinfrastructure work for EHA’s
veterinary disease forecasting under DHS as well as the DTRA-Funded Western Asia Bat
Research Network.

Emma Mendelsohn, M.Sc., Research Scientist is a data scientist with extensive expertise in
dynamic systems modeling, biostatistics, web application development and data engineering.
Emma is lead analyst for EHA’s work on antimicrobial resistance emergence work as well as
projects linking emerging infectious diseases with nutrition and non-communicable diseases.
Emma will lead data engineering and contribute expertise to algorithm development and
application design.

Peter Daszak, Ph.D., President of EcoHealth Alliance will provide oversight and act as project
advisor on infectious disease spread. Dr. Daszak is a leader in the field of emerging diseases and
disease ecology.


Grant Training DCP Matrix

(e.g., personal protective equipment,
safety practices and equipment,
facility design, and administrative
controls).

with development and implementation of
international best practices and procedures for
EHCP biosafety and biohazard control
measures (substitution, elimination,
mitigation) for personnel, equipment, facilities
(laboratories, hospitals, and other biomedical
and clinical facilities) and the community.

7.1.3 Biohazard Incident
Management

Knowledge, understanding and proficiency
with development and implementation of
policies, procedures, and operational
guidelines, including emergency operation
communications systems, for Biohazard
Incident Management for EHCP outbreaks,
releases or exposures,

8. Laboratory
biosecurity

8.1 Prevent theft, misuse,
loss, or deliberate release of
emerging and high
consequence pathogens
(EHCP).

8.1.1 Biosecurity Risk Identification

Knowledge, understanding and proficiency
with principles and procedures for Biosecurity
Risk identification, risk assessments,
characterization and compliance with
recommended international standards and
best practices to prevent the loss, theft, or
misuse of biological organisms and
laboratory/research technologies.

8.1.2 Biosecurity Risk Mitigation

Knowledge, understanding and proficiency
with international best practices for
Biosecurity Risk Mitigation, and related risk
reduction approaches to enhance physical
security, personnel reliability, material control
and accountability, transport security, and
information security/cybersecurity, and the
development and implementation of
Biosecurity Risk Mitigation strategies for
laboratories, facilities, and personnel to



EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.


Caution-http:/www.ecohealthalliance.org/ < caution-Caution-Caution-http://www.ecohealthalliance. org/ > > > >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation

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Date: August 18, 2020 at 08:50:01 EDT

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>>

Cc: "Dr. Jon Epstein" <epstein@ecohealthalliance.org <

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(6)



From: Aleksei Chmura
bye)

To: Kexe

Ce: Dr_Jon Epstein; Avg Sullivan ;kbi(6) ] Joe Biccara; (EXO) _]
fre) Alison Andre:

Subject: Re: [Non-DoD Source] FRBAA14-6-2-0:

Date: Thursday, September 17, 2020 10:17:58 AM

bled. Please verify the identity of the sender, and confirm the
ge prior to copying and pasting the address to a Web browser.

All active links contained in this email were di:
authenticity of all links contained within the mess

Deaifb)(6)

Apologies for missing your call just now.

I confirm that our India project budget is $4,956,064.36. This is less than our originally proposed budget. Our
justified and verified budget costs were slightly reduced compared to what was originally submitted via Grants.gov

< Caution-hitp://Grants.gov >.
Many thanks!

-Aleksei

Aleksei Chi
Chief of Staff

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 > (office)

Po } cmobile

Caution-www ecohealthalliance.org < Caution-http:/www.ecohealthalliance.org >
EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

[ do have a question, while reviewing your budget I see a total budget of $4,956,064.36 and S4,964.011.70,
which has orignal. For clarification, what is the total proposed amount?

On Sep 16, 2020, at 10-04f16)
Caution-mailtofX®) > wrote:

Original Message.
From; Aleksei Chmura <chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org > >
Sent: Wednesday, September 2, 2020 11:52 AM

To: Lyles, Latrice D CTR DTRA AL (USA) {0)6)
Caution-mailtf@)



RESEARCH & RELATED BUDGET - Cumulative Budget

Totals ($)

Section A, Senior/Key Person

Section B, Other Personnel

Total Number Other Personnel

Total Salary, Wages and Fringe Benefits (A+B)
Section C, Equipment

Section D, Travel

1. Domestic
2. Foreign
Section E, Pa

ant/Trainee Support Costs

1. Tuition/Fees/Health Insurance
Stipends

Travel

Subsistence

Other

7 Aa wp

Number of Participants/Trainees

Section F, Other Direct Costs [ace]
Materials and Supplies La, 000.00]

Consultant Services
ADP/Computer Services
Subawards/Consortium/Contractual Costs
Equipment or Facility Rental/User Fees
Alterations and Renovations

Other 1

Other 2

10. Other 3

oe NP ae ww B

Section G, Direct Costs (A thru F)

Section H, Indirect Costs

Section |, Total Direct and Indirect Costs (G + H)
Section J, Fee

Section K, Total Costs and Fee (I+ J)

Tracking Number:GRANT13118386 Funding Opportunity Number:HDTRA1-14-24-F RCWMD-BAA Received Date. May 28, 2020 05:49:37 PM EDT.


Grant Training DCP Matrix

5. Quality
management
systems

5.1 Knowledge, skills, and
abilities required for
developing a culture of
quality. Operations,
services, and infrastructure
integrated in a system that
meets applicable standards,
professional guidelines, and
customer requirements for
ensuring and maintaining
quality and continually
improving services,

5.1.1 Physical environment and
equipment

Knowledge, understanding and proficiency
with facility and equipment operations and
maintenance, and facility management policies
and procedures, in support of establishing a
culture regarding physical environment and
equipment necessary for EHCP detection,
management, and control, or
management/oversight of these operations, in
a system that meets applicable standards,
professional guidelines, and customer
requirements for ensuring and maintaining
quality and continually improving services.

5.1.2 Qualified, well-trained, and
competent workforce

Knowledge, understanding and proficiency
with the principles, processes, and
methodologies for workforce development
and sustainment in relation to EHCP detection,
management, and control, including workforce
planning, training, refresher training,
continuing education, and/or professional
development, in a system that meets
applicable standards, professional guidelines,
and national licensing requirements where
applicable.

5.1.3 Materiel resource management

Knowledge, understanding and proficiency
with procedures and policies for procurement
and inventory management of materiel
resources, including supply chain systems,
operations, and infrastructure, or
management/oversight of these processes.



William B. Karesh
EcoHealth Alliance, 460 W. 34th St, New York City, NY 10001, USA
E-mail: karesh@ccohcalthalliance.org

Professional Preparation

Clemson University Biology BS 1977

Univ. of Georgia Veterinary Medicine DVM 1982

Zool. Society of San Diego Residency Wildlife Health 1982 - 1984
Appointments

Life Member, Council on Foreign Relations 2016 -
present

Emerging Pandemic Threats Partner Liaison, USAID EPT PREDICT-2 2014 - present
Advisor, WHO Expert Panel on MERS-CoV 2013 - present
Expert, WHO International Health Regulation Roster of Experts 2013 - present
Executive Vice President for Health & Policy, EcoHealth Alliance 2010 - present
President, Working Group on Wildlife Diseases, OIE, France 2008 - present
Co-Chair, Wildlife Health Specialist Group, IUCN, Switzerland 2001 - present
Chief Technical Officer, USAID EPT PREDICT 2009 - 2014

Chief of Party, USAID Global Avian Influenza Network for Surveillance 2006 - 2009
Vice President & Director, Global Health Programs, WildlifeCons. Society 2001 - 2010

Publications

Kandeil A, Gomaa M, Shehata M, El Taweel AN, Mahmoud SH, Bagato O, Moatasim Y, Kutkat
O, Kayed AS, Dawson P, Oui X, Bahl J, Webby RJ, Karesh WB, Kayali G. (2019) Isolation
and characterization of a distinct influenza A virus from Egyptian bats. Journal of Virology
93(2).

Anthony SJ, Johnson CK, Grieg D, Kramer 8, Che X, Wells HL, Hicks AL, Joly D, Wolfe ND,
Daszak P, Karesh WB, Lipkin WI, Morse SS, Predict Consortium, Mazet J, Goldstein T.
(2019) Global patterns in coronavirus diversity. Virus Evolution 3(1).

Machalaba CC, Elwood SE, Forcella S, Karesh WB. Global avian influenza surveillance in wild
birds: A strategy to capture viral diversity. (2015) Emerging Infectious Diseases 21(4):¢1-7.

Alagaili AN, Briese T, Mishra N, Kapoor V, Sameroff SC, de Wit E, Munster VJ, Hensley LE,
Zalmout IS, Kapoor A, Epstein JH, Karesh WB, Daszak P, Mohammed OB, Lipkin WI. (2014)
Middle East Respiratory Syndrome Coronavirus infection in dromedary camels in Saudi
Arabia. mBio 5(2):e00884-14.

Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, Aldrich 8,
Harrington T, Formenty P, Loh EH, Machalaba CC, Thomas MJ, Heymann DL. (2012)
Ecology of zoonoses: Natural and unnatural histories. The Lancet 380(9857):1936-45.

Gaidet N, Caron A, Cappelle J, Cumming GS, Balanca G, Hammoumi §, Cattoli G, Abolnik C,
De Almeida RS, Gil P, Fereidouni SR, Grosbois V, Tran A, Mundava J, Fofana B, El Mamy
ABO, Ndlovu M, Mondain-Monval JY, Triplet P, Hagemeijer W, Karesh WB, Newman SH,
Dodman T. (2012) Understanding the ecological drivers of avian influenza virus infection in
wildfowl: A continental-scale study across Africa. Proceedings of the Royal Society B:
Biological Sciences 279(1731):1131-41.

Morse SS, Mazet JAK, Woolhouse M, Parrish CR, Carroll D, Karesh WB, Zambrana-Torrelio C,
Lipkin WI, Daszak P. (2012) Prediction and prevention of the next pandemic zoonosis. The
Lancet 380: 1956-65.


Grant Training DCP Matrix

4.1.3 Lead culturally diverse teams to
complete a project or program

Knowledge, understanding and proficiency in
developing and leading culturally diverse
teams, ensuring collaboration among team
members, and mentorship of future team
leaders.

5. Quality
management
systems

5.1 Knowledge, skills, and
abilities required for
developing a culture of
quality. Operations,
services, and infrastructure
integrated in a system that
meets applicable standards,
professional guidelines, and
customer requirements for
ensuring and maintaining
quality and continually
improving services.

5.1.1 Physical environment and
equipment

Knowledge, understanding and proficiency
with facility and equipment operations and
maintenance, and facility management policies
and procedures, in support of establishing a
culture regarding physical environment and
equipment necessary for EHCP detection,
management, and control, or
management/oversight of these operations, in
a system that meets applicable standards,
professional guidelines, and customer
requirements for ensuring and maintaining
quality and continually improving services.

5.1.2 Qualified, well-trained, and
competent workforce

Knowledge, understanding and proficiency
with the principles, processes, and
methodologies for workforce development
and sustainment in relation to EHCP detection,
management, and control, including workforce
planning, training, refresher training,
continuing education, and/or professional
development, in a system that meets
applicable standards, professional guidelines,
and national licensing requirements where
applicable.

5.1.3 Materiel resource management

Knowledge, understanding and proficiency
with procedures and policies for procurement



Enter all patents, invention disclosures, and licenses resulting from this DTRA award that were awarded or applied for during the reporting period
Reporting here does NOT replace the reporting requirements for patents and invention disclosures in the terms and conditions of the award,

Selec the inellest Foner thesa-nty

patent MEE FUMES ner tge the REMAN on icciom dats in date 9 naweisio# she patent or
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ARetwimg te Brace" tere SBOE tnofist note, PRIMBEMOE oe isemtorts). the deo: ewe

Intellectual Property
Tee

Award # Number Country Application Date ate Issued Abstract or Description Inventors Status

HOIRAL-ROTRS Note oar
[mowa-s0-1-0029
HOTAL-20-L0023
(foTRAL20-1-0029
HOTRES-20-L.029
[jorRa-20-10029
PTRAL 20 1129
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(foTeat30-1-0029
HOTRAL-20-L0029
(foTRAL20-1-0020
TRAN 20 112
[Hornas-20-1-0028

Proceed to Transitions



authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

[ just wanted to confirm that you received the email below earlier this week.

Thanks!!

William B. Karesh, D.V.M
Executive Vice President for Health and Policy

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018 USA

+1.212.380.4463 (direct)

+1.212.380.4465 (fax)

Caution-Caution-www-ecohealthalliance.org <hitp.‘/caution-caution-www ecohealthalliance.org’> <
Caution-bttp://caution-Caution-www ecohealthalliance.org/ > < Caution-
Caution-mailto:karesh@ecohealthalliance.org < Caution-mailto:kareshi@ecohealthalliance.org > >

President, OIE Working Group on Wildlife
Co-chair, IUCN Species Survival Commission - Wildlife Health Specialist Group

EPT Partners Liaison, USAID Emerging Pandemic Threats - PREDICT-2 Program

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

Begin forwarded message:

From:Aleksei Chmura <chmura@ecohealthalliance.org <mailto:chmura@ecohealthalliance.org> <
Caution-mailto:chmuraiéecohealthalliance.org > < Caution-Caution-mailto:chmura@wecohealthalliance org <

ecohealthalliance.org > > >

Caution-mailto:chnurai

Subject:Re: [Non-DoD Source] FRBA14-6-2-0471 Clarifications

Date: August 30, 2020 at 8:14:36 PM EDT


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Masks N35 so $ 75 23425 75 3425 75 33025 53 Be os
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1s 55 1900.00 3 $ 3 5
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Eye wats station 1s 15 2300 3 $ 3 5
Card reader system: 5 151,500.00 8 5 se 5
TOTALS $35,933.56) $18,70877 $7686.57 $10,795.19 §
Twine LA rceralcryav ae PERS J awabsfanimal ar person
Servin subes 10 re, 1 oer hus, 2 per cumal serolugy
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Bcc er lack sere 1 ec cx ceen serolony
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Cotta Seas PCRS. 2 wabs/sruml ur porsor


DEFENSE THREAT REDUCTION AGENCY
8725 JOHN J. KINGMAN ROAD, STOP 6201
Fort BELvoiR, VA 22060-6201

February 17, 2022

Shannon Murray
USS. Right to Know

Re: FOIA Case No.: 22-008
Dear Ms. Murray:

This is our final response to your Freedom of Information Act (FOIA) request perfected
on October 6, 2021, and assigned FOIA case number 22-008 by the Defense Threat Reduction
Agency (DTRA). You requested a copy of grant proposals, scientific and technical interim
reports, reviewer response documents, policy-related documents, and email communications
between DTRA staff and EcoHealth Alliance staff (email to or from the domain
@ecohealthalliance.org, including attachments) about the following grants awarded by DTRA to
EcoHealth Alliance, since January 2019: HDTRA12010026, HDTRA12010029, and
HDTRA12110023.

Enclosed is a copy of the documents totaling 311 pages. These records are being released
to you in part. Some information is being withheld under FOIA Exemption 6. Exemption 6
applies to information, which, if released, would constitute a clearly unwarranted invasion of the
personal privacy of an individual. No fees are due as the assessable cost total $25.00 or less.

Determinations for this interim release were made by the Initial Denial Authority (IDA),
Mr. Earl Washington, Chief, Records Management, FOIA, and Privacy Act Division / DTRA
Records Officer, Information Management and Technology Directorate, on behalf of DTRA. If
you consider this decision to be an adverse determination, you may file a written appeal that is
postmarked no later than 90 calendar days after the date of this letter to the Deputy Director,
Defense Threat Reduction Agency, Information Management and Technology Directorate,
ATTN: FOIA/PA Office, 8725 John J. Kingman Road, MSC 6201, Fort Belvoir, Virginia
22060. The appeal should reference the FOIA/Privacy Act case number, contain a concise
statement of the grounds upon which the appeal is brought, and a description of the relief sought.
A copy of this letter should also accompany your appeal. Both the envelope and your letter
should clearly identify that a Freedom of Information Act and/or a Privacy Act Appeal is being
made.


Section F - Deliveries or Performance

DELIVERY INFORMATION

CLIN DELIVERY DATE QUANTITY

0001 POP 29-SEP-2020 TO N/A

28-SEP-2023
000101 N/A N/A
0002 «1 yr. AOE 1
0003 1 yr. AOE 1

HDTRAI2010029

Page 6 of 10
SHIP TO ADDRESS DODAAC/
CAGE
DEFENSE THREAT REDUCTION HDTRAL
A ie
N ROAD MSC 6201
FORT BELVOIR | VA 3080 6201
FOB: Destination
N/A N/A
DEFENSE THREAT REDUCTION HDTRAL

AGENCY ICT- I
DYE

8725 JOHN J. KINGMAN ROAD MSC 6201

FORT BELVOIR VA 22060-6201

FOB: Destination

(SAME AS PREVIOUS LOCATION)
FOB: Destination

HDTRAL


Biological Threats Reduction Program
PROJECT QUARTERLY REPORT

PROJECT INFORMATION

HDTRA12010029 - Reducing the Threat of Middle

East Respiratory Syndrome Coronavirus and Avian

Influenza in Jordan & Strengthening Regional

Disease Surveillance Capacity

Quarter and Dates: Y1, Q4 | (06/30/21- 9/29/21)
Prepared by: | Dr. William B. Karesh

Project and Title:

OVERALL PROJECT SUMMARY AND OBJECTIVES

Project Objective

Utilizing an international consortium of partners, this effort is a multi-disciplinary One Health
research project to identify causal factors in the transmission of Middle East Respiratory Syndrome
Coronavirus (MERS-CoV), Avian Influenza (AI), and other zoonoses in Jordan, while simultaneously
strengthening local capacity for the prevention, detection, and reporting of these zoonoses in Jordan, Iraq,
and Lebanon. Our proposed project will rigorously test the following initial hypotheses: 1) That livestock
species and poultry in Jordan show evidence of infection with MERS-CoV or AI, and/or other zoonoses.
2) That people regularly exposed to livestock and/or poultry or their living areas have a greater risk of
infection with MERS-CoV, AI, and/or other zoonoses compared to other community members in Jordan.
3) That some specific practices may promote exposure to specific animal biohazards (e.g., blood, urine,
feces, and/or nasal secretions) and pose a greater risk of infection with MERS-CoV, AI, and other
zoonoses compared to others. This study involves interviews and longitudinal sampling of humans,
livestock, and poultry at animal markets and farms for MERS-CoV, Brucella spp., Leptospira spp.
(people/livestock), and for AI subtypes (people/poultry). Partner laboratories in Jordan will screen and
characterize these zoonotic pathogens, and capacity-building cross-trainings will be conducted with
visiting laboratory scientists from Iraq and Lebanon. This multifaceted One Health approach will advance
scientific knowledge on the risk of MERS-CoV, AI, and other zoonoses in Jordan, strengthen local
zoonotic disease surveillance capabilities, and enhance scientific partnerships in Jordan, Iraq, and Lebanon
to prevent, detect, and report biological threats.

Biological Threats Reduction Program


a Oe a |

Reducing the Threat of Middle East Respiratory Syndrome
Coronavirus and Avian Influenza in Jordan & Strengthening
Regional Disease Surveillance Capacity

Annual Report Year 1

Principal Investigator: Dr. William Karesh, DVM
EcoHealth Alliance

Biological Threats Reduction Program


Frter all presentations (poster or oral} given during the reporting period that resulted from this DTRA project or contained significant results from this DTRA project.

Select the
presentation type
from the drop down

Instructions tt. sett” sathing

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presentations

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HOTRAT-20-1-0028

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HDIRAL 20 1 BE

MOTRAL 20.1 0029

HOTRAT-20-1-0028

HOTRA-20-1-0025

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HDIRAL 20 1 BE

[HOTRAT-20-1-0029

HOTRAT-20-1-0028

HOTRAT-20-1-0025

HOTRAI-20-1-0028

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HDIRAL 20 1 BE

[HOTRAT-20-1-0029

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HOTRAT-20-1-0025

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HDIRAL 20 1 BE

HOTRAT-20-1-0029

Proceed to Impacts



po ]

<EHA India SOW.pdf»<Terms and Conditions (DTRA-specific)_14Sep2020 FINAL.pdf><HDTRA 1-20-1-
0026 Award.pdt>


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

o September 14, 2021, the field and lab teams had a meeting to discuss the logistics of cold
chain transport of samples from different project sites to JUST. The field team will draw a
chart to summarize this process according to the agreed SOPs.

o September 16, 2021, Dr. Whitney Bagge conducted a regional planning meeting to discuss
the upcoming training at Human Link Lab at the National Research Centre in Egypt.

© Those in attendance:
= EcoHealth: Dr. William Karesh Dr. Whitney Bagge, Jamie Cooksey,
Amanda Andre, Darya Ivanova
= JUST: Dr. Ehab Abu-Basha, Dr. Zaidoun Hijazeen, Dr. Bilal Al Omari,
Yasmin Daradkeh, Farah Al. Nabulsi
= Human Link: Ghazi Kayali
o The meeting concluded that the training would take place for 5 days in the second
half of November. Security clearance for participants to enter the lab will need to
be cleared before travel to Egypt since the lab is a governmental facility.

Future Subtask(s) ‘ ‘Aitabla Lab Data Validation Report MEREGV Asian
Subtask 3.1 Conduct project kick-
off meeting in Amman with local
stakeholders Gow teas” ‘naan
Subtask 3.3 Host annual partners
and stakeholders meeting
Subtask 5.4 Complete annual eHARTCOOO NA vA Na NULL RealTime FCR ORF 1A Negative

report and share with partners and EMArICOOT Ne
stakeholders

EHADTCODS NA Swabinoss — Mere: RealTime PCR Reel Time PCR ORF 1A Positive

Figure 15. An updated version of the validation reports
that will be sent to the team members that will inform of
any extreme or missing values.

Tasks to be initiated after Year 1:

Task 6. Identity specitic modifiable risk factors for human infection with these zoonoses

Biological Threats Reduction Program 10


RESEARCH & RELATED BUDGET - Budget Period 1 OMB Number: 4040-0001
Expiration Date: 12/31/2022

ORGANIZATIONAL DUNS:

Enter name of Organization: = |..-;:ea1-h Alliance

Budget Type: Project [_] Subaward/Consortium Budget Period: 1 _—_‘Start Date: End Date:

A. Senior/Key Person

Months

Requested Fringe Funds
Prefix First Middle Last Suftix Base Salary (3) Cal. Acad. Sum. Salary ($) Benefits ($) Requested ($)
Ee on TOL a a oe

Project Role: [pr;/r1 ]

lone 1 Taal TI

1s 220.05]

Project Role: [senicr Engineer
[ Jemma l [veadersona | l L | a2, 800.9] 15, 640.03] ae.03]
Project Role: [Research Setentist
Total Funds requested for all Senior
Additional Senior Key Persons: Add Attachment l[ | Key Persons in the attached file LF
Total Senior/Key Person
B. Other Personnel
Number of Months Requested Fringe Funds
Personnel Project Role Cal. Acad. Sum. Salary (8) Benefits ($) Requested (8)

Post Doctoral Associates

Graduate Students

LE] Undergraduate Students [ ]
L__] L_]
[|
[

Secretarial/Clerical

Total Number Other Personnel Total other Personnel [

Total Salary, Wages and Fringe Benefits (A+B)

II |
II |
|| |
|| |
| |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


HDTRAI2010029
Page 7 of 10

Section G - Contract Administration Data

ACCOUNTING AND APPROPRIATION DATA

AA: 044315 097 0134 000 N 20182020 D 34HQ 0901515SBR_KD_BP_OT_18 1820_0134_34HQ_SCNCT DTRA 410
AMOUNT: $2.956,308.78

ACRN — CLIN/SLIN CIN AMOUNT

AA 000101 HDTRA 10361 120001 $2.956,308.78,
CLAUSES INCORPORATED BY FULL TEXT
252.601-9000 GRANT POINTS OF CONTACT (MAY 2009)

a Grant Specialist:

Name: fX®)
Defense Threat Reduction Agency/AL-ACC

8725 John J. Kingman Road, MS 6201
Fort Belvoir, VA 22060-6201

Telephonefby6y
a

b. Grantee Business Office:
Name: Dr. Aleksei Chmura
Title: Executive Organizational Representative
Phone: (212) 380 - 4473
E-mail: chmura@ccohcalthallianee.org

c. Grantee Principal Investigator (PI):
Name: Dr. William Karesh
Title: Executive Vice President of Health and Policy
Phone: (212) 380 - 4463
E-mail: karesh(@ecohealthalliance.org

(End of Clause)

252.601-9001 | GRANTS OFFICER'S REPRESENTATIVE (GOR) (MAY 2009)

d. Grants Officer's Representative (GOR) for this Grant is:

Defense-Threat Reduction Agency/BTRP

8725 John J. Kingman Road
Fort Belvoir, VA 22060-6201
telephonefb}6)

email address:{}O)



HDTRAI-14-24-FRCWMD-BAA
CBEP = Thrust Area 6 — Cooperative Counter WMD Research with Global Partners

HDTRA1-14-24-FRCWMD-BAA
CTRP = Thrust Area 6 — Cooperative Counter WMD Research with Global Partners
Biosurveillance for Spillover of Zoonotic Viruses in Rural Communities in India

Abstract: Zoonotic viruses including certain henipaviruses, filoviruses, and coronaviruses constitute a
significant threat to global health. Nipah virus (NiV), Ebola virus (EBOV), and SARS coronavirus are highly
pathogenic viruses and select agents capable of causing public health emergencies of international
concern. In 2018, the WHO listed these pathogens among the top ten threats to global health and
security. In 2014, a single spillover event of Ebola Zaire from an animal host (presumed to be a bat)
caused an epidemic in West Africa that infected more than 28,000 people, killing approximately 40%. In
Bangladesh and India, Nipah virus causes outbreaks of acute encephalitis in people and has an
associated mean mortality rate of 70%. Saudi Arabia and the Gulf states continue to have new cases of
MERS CoV, a zoonotic coronavirus related to SARS CoV, Bats are reservoirs for these and related viruses,
and zoonotic transmission fram bats to humans either directly or via domestic animals has previously
occurred in Southeast Asia. Despite their importance as threats to public health and security, the full
diversity of these viruses in bats and their potential to infect livestock and people remains poorly
understood. This project will test the hypothesis that henipaviruses, filoviruses, and coronaviruses are
circulating in bats in India and have spilled over into animal and human populations undetected, which
presents a threat to human health. In line with Thrust Area 6 —- Cooperative Weapons of Mass
Destruction Research with Global Partners, this project will reduce threat from these select agents and
viruses of high concern by enhancing early detection and surveillance capacity in India by: 1) conducting
biological surveillance in bats, macaques, livestock and people in rural communities in Uttar Pradesh; 2)
transferring PCR protocols and a multiplexed serology platform that can detect RNA from and IgG
antibodies against all known henipaviruses, filoviruses, and SARS & MERS coronaviruses, to Indian
partner labs; and 3) training laboratory personnel to develop and safely utilize these assays to identify
human and animal infection and exposure to these viruses. This project will contribute to the Global
Health Security Agenda, India and we will share results with key Indian government partners. The
proposed project is closely aligned with the aims of the Cooperative Threat Reduction Program in that it
will support threat reduction through biosurveillance and capability building, engagement with partner-
country scientists, and use of a One-Health approach.

Background: India has been identified as a hotspot for emerging infectious diseases (EIDs). The majority
of EIDs are zoonotic and caused by pathogens that circulate in wild animal populations. People across
South Asia, in both rural and urban settings, live in close association with domestic and wild animals,
including bats, which have been identified as important reservoirs for zoonotic viruses. Zoonoses such as
SARS CoV, Influenza and HIV, have already caused significant global pandemics, and there is potential for
other zoonotic viruses (known and unknown) to cause widespread epidemics if given opportunities to
jump into human populations (e.g. Ebola virus in West & Central Africa). The henipavirus, filovirus, and
coronavirus viral groups include important zoonotic agents such as Nipah virus (NiV), Ebola virus (EBOV),
and SARS CoV. Bats are natural reservoirs for all three viral groups. Nipah virus and Ebola virus are
listed as select agents by CDC and USDA, and the WHO has identified them and SARS coronavirus as
three of the ten most important global infectious disease threats. Enhancing surveillance for these
significant zoonotic viruses in animal and human populations to improve detection and reporting in
areas highly vulnerable to disease emergence is critical for reducing the risk of epidemics.

There is a need for enhanced surveillance for zoonotic viruses in India. Acute encephalitis syndrome
(AES) accounts for more than 10,000 deaths each year in India. Uttar Pradesh (UP), in northern India, is a
hot spot for AES, and more than 50% of encephalitides India-wide remain undiagnosed. Nipah virus is a


F. Other Direct Costs

Funds Requested ($)

Materials and Supplies
Publication Costs
Consultant Services
ADP/Computer Services

Subawards/Consortium/Contractual Costs

Alterations and Renovations

7,000.00}

1,500.00)

J |

1.

2.

3.

4.

5.

6. Equipment or Facility Rental/User Fees
7.

8.

9.
10.

G. Direct Costs

Total Other Direct Costs

Funds Requested ($)

H. Indirect Costs

Total Direct Costs (A thru F) 181,32

Indirect Cost Type

Indirect Cost Rate (%) Indirect Cost Base (S) Funds Requested ($)

wealth Alliance's NICA rate

a2, 324.00] [

Cognizant Federal Agency

Total Indirect Costs

(Agency Name, POC Name, and
POC Phone Number)

IDepasimens of Defense, Deparvmen: of ihe Navy, Skavon Gales, 1.

|. Total Direct and Indirect Costs

Funds Requested ($)

Total Direct and Indirect Institutional Costs (G + H) 246, 320.52

J. Fee Funds Requested ($)

K. Total Costs and Fee Funds Requested ($)
Total Costs and Fee (I + J) 246, 2.0.82

L. Budget Justification

(Only attach one file.) [i232 DTRA ML_Budge. Juez ificarion. pal Delete Attachment View Attachment ]

Tracking Number:GRANT13118386

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Revised 14 September 2020 EXHIBIT B

DEFENSE THREAT REDUCTION AGENCY (DTRA)
GENERAL TERMS AND CONDITIONS FOR GRANT AWARDS

Table of Contents

Table of Content ........sccsscssessseasessacossseasessncossssacsssncnssssasessncessncnesseacsssncseaeacsesusneacencnenene sees 1
1. Terms and Conditions Incorporated by Reference. ........ccccccsscsscssesseeseecceesees 2
2. Acceptance of Grant. ........eseseereeeee

3. Recipient Responsibilities............cesscessrersversversversnersseessnesveesveesveesseesseesseesseesneeseersees 2
4. Standards for Financial Management Systems........cccsccssssesesscssessesasesaseraseeasees 2
5. Modification of the Grant. ......cssccssssscsesessasessseasessacsssaseesssacsssacneaseassssncneaceeneaceneseen 2
6. Payments.

7. Funding Increments and/or Options. ...........srssersersserssersserseersserssenssensseneesnessneanees 3
8. Patent Rights. ............csscsssessscssssesnessseesssesnessseesssesnessecesnessssesecessessseesscessassseesneesceseeeee 4
9. Technical Reporting Requirements...........cccccccsescesscsscssessesasesasesasesasesaseeaseeseeasees 4
10. Financial Reporting Requirement. .........cccscccsescesscsscssesscsasesasesasesasesaseraseecseeasees 7
11. Delegation of Administration Duties. .............cssssssccssscssssescsssesssseseeossessssesnesscessesseee 7
12. Security. ...

13.

14.

15. Publications and Acknowledgement of Sponsorship. ........sssssssssesssesseeseevversveres 13
16. Authorization to Perform Activities ADr0ad.........sssssssssssesnersseersessersseeseeennensseess 13
17. Inconsistency between English Version and Translation of Grant... 14
18. Value Added Tax (VAT) and Other Taxes

19. Prohibition on the Use of Cooperative Threat Reduction Funds in the Russian
Federation

Page 1 of 14


CONSULTANTS Prime 1 (
Description of effort to be performed by the Consultant | Number of | Hourly Supporting Supporting
Name (Note 1) Total | Documentation (Note| Rationale
or attach Consultant Statement of Work Hours Rate
2) (Note 3}
Base
$0.00
$0.00
$0.00
[Option |
$0.00
$0.00
$0.00
$0.00
Instructions: ~ For Prime - fil out all columns f applicable. Indicate "Not Applicable” in BS when there is no information to include.
Note 1: Include a separate section in the above table for the Base and each Optian.
Note 2: Pravide the location (section, page) in the proposal's Cost Narrative/Supparting Documentation (BAA para 3.5.2.2.1) used to support your price basis (e.g. copy of quate or agreement),

Note 3: Provide the location (section, page) in the proposal's Technical Basis of Estimate (BAA para 3.5.2.1.2) of the rationale for the necessity of the proposed consultant(s).


<mailte@@)

Subject: Re: [Non-DoD Source] FRBA14-6-2-0471 Clarifications

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Much appreciated{OX)

I cannot imagine how you get through all these documents. We only have a few proposals/awards to justify and
it seems like an enormous task for us!

If T may help you quickly locate certain documents in our forms and files, please call or text my handphone
anytime day or night.

Many thanks again!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

a) [> «mobile)
SOTO TOTE-=Dittp.//caution-www ecohealthallianceorg/> <

Caution-http:/www.ecohealthalliance.org >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

pa

On Sep 2, 2020, at 12:17,
Kex6)

<mailto} > wrote:

Yes . they were received. Thank you

a Original Message----
From: William B. Karesh <karesh@ecohealthalliance.org <mailto:karesh(@ecohealthalliance.org> <

Caution-mailto:karesh(@ecohealthalliance.org > >
Sent: Wednesday. September 2, 2020 11:16 AM
Tatb)(6)

<mailto
Ce: Aleksei Chmura <chmura@ecohealthalliance.org <mailto:chmura@ecohealthalliance.org> <

Caution-mailto:chmura@ecohealthalliance.org > >
Subject: Fwd: [Non-DoD Source| FRBA14-6-2-0471 Clarifications

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EcoHealth Alliance develops science-based solutions to prevent pandemics and promote
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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Background

Situational awareness and risk assessment are critical at early stages of disease outbreaks, when
interventions can best mitigate adverse outcomes. Yet decision-makers often face a paucity of
data at these stages. Traditional modeling methods to forecast disease impact severity rely
heavily on prior knowledge or assumptions about mechanisms of disease spread progression, and
they frequently do not anticipate the actual mechanisms.'* They also take considerable time and
effort to develop.

Recent theory suggests disease dynamics are fundamentally predictable+, and ML methods have
been successfully applied in emerging disease science. ML refers to a group of methods, ranging
from simple nearest-neighbor approaches to function-approximation methods such as boosted
trees and penalized splines, to complex deep-learning neural networks. ML has been used to
predict the likelihood of disease emergence and the zoonotic potential of various hosts and
viruses, and deep-learning approaches have been used to infer viral hosts from the
comparatively richer data of genetic sequences of viruses.’*

ML methods are generally unstructured, flexible, and offer greater predictive power over
traditional statistical approaches. However, these methods are data-intensive, and a key
challenge with ML development is data paucity and/or a low signal-to-noise ratio. Surveillance
data for even existing, well-studied diseases is rarely rich enough for the data scale of many
modern ML techniques. For instance, a ML approach to predict impacts from a new outbreak of
Ebola would draw training data from fewer than twenty previous events, each with varying
levels of data coverage.

Transfer learning, or data fusion, provides a solution to data paucity issues by using data from
other domains to train parts of models that are general, thus limiting new data requirements for
more specific components. Hierarchical modeling, or partial pooling, is a transfer learning
approach for partitioning out which data can be shared between models and which are only
applicable to a specific case. For instance, Ebola forecasts for countries outside the historic
range of the disease may be improved by calibrating against Cholera outbreaks, which have
occurred both within and outside countries with Ebola outbreaks. The degree to which the
disease outcomes are correlated is learned, rather than assumed.

An alternative recent development in disease forecasting is the development of hybrid models
that incorporate simple mechanistic structures and predictive ML methods to learn model
parameters. These approaches have demonstrated high predictive accuracy for near-term
forecasting of epidemic growth and impact, particularly during the COVID-19 pandemic’.
However, these approaches require adequate time-series of data to project forward from current
conditions. Their utility in early stages and long-term forecasting accuracy has yet to be
determined. In opting to apply these models, it is critical to understand these data requirements
and at what point in an outbreak hybrid approaches can overtake pure ML early-stage predictions
in accuracy. This project seeks to develop, apply, and compare pure and hybrid-ML techniques
to enable the development of robust, rapidly usable predictions at early stages of outbreaks.

Kev references (additional references can be found in the Technical Proposal):

1 MacDiarmid, S. C. et al. Handbook On Import Risk Analysis for animals and animal
products: quantitative risk assessment. Vol. 2 (Office International des Epizooties, 2004).


Enter transitions of the research or technology from this DTRA project.

Instructions

Award #

Example

Enter a text description of the transitioned Enter the year _Enter the organization, company, or
research or technology. Enter “nothing to. that the transition government agency to the which this
report" if there were no transitions. started. work has {is) transitioning.

Year

The accelerator technology for detection of

WMDs transitioned to funding from other

US government agency for further

development 2020 USGOV Contract # 12-34-5678

HDTRA1-20-1-0026

Nothing to report

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026

HDTRA1-20-1-0026



Quarterly Report: HDTRA12010029 - Year 1, Quarter 2 (12/29/20 - 03/28/21)

YEAR 1, QUARTER 1 PROGRESS UPDATES FOR EACH TASK

Task L: Determine the presence of MERS-CoV. AL and other zoonoses among livestock and
poultry in Jordan,

Subtask 1.1 Tdentily sites for projeet study

Activities and results this quarter:

o Project staff reviewed site selection criteria
and collaborated with the team to produce a
solution for spatial sampling using a “Shiny
App.” This novel app will be used to
randomly select sampling sites in Jordan.

© The Site Characterization form has been
drafted and coded with Open Data Kit
(ODK). ODK is software that allows for the
creation of survey tools and the collection,
management, and usage of data in low-
bandwidth environments.

Subtash 1.2 Obtain local permissions and

EPPO ‘ Figure 1, An example of the Shiny App

Activities and results this quarter: randomly selecting sites (the red dots).

o The animal owner consent forms were finalized
with input from local subject matter experts (SMEs)
familiar with sampling communities. kb ‘F

o Jordan University Science and Technology (JUST)
IACUC for research with animal subjects was
submitted and full approval was obtained.

o JUST approval for use of the animal owner consent
forms was obtained.

o ACURO approved the animal IACUC and
determined the projects protocol was a surveillance
project. No further action is required.

o. The first draft of the animal sampling protocol has ie
been completed and is currently being reviewed by
partners.

vely to werk with animals

Future Tasks

Subtash 13 Conduct hivlogical sampling of animals in
the vtudy areas

Subtask 14 Conduct lab testing for Al, MERS-Col, Figure 2. Approval letter from
hrucellosis and leptospirosis Jordan’s IRB committee.

Biological Threats Reduction Program Page 2 of 5


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: William Karesh

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus (MERS) and Avian Influenza (Al) in Jordan &
Strengthening Regional Disease Surveillance Capacity (current proposal)

Source of Support: DTRA

Total Award Amount: $4,975,681.29 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Jordan

Person-Months Per Year Committed to the Cal: 2 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Understanding the Risk of Bat-Bourne Zoonotic Disease Emergence in Western Asia

Source of Support: DTRA

Total Award Amount: $4,391 ,443.65 Total Award Period Covered: 10/02/2017 - 10/01/2022

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Ground Truth Network

Source of Support: DHS

Total Award Amount: $2,231,114.13 Total Award Period Covered: 09/30/2016 - 09/29/2021

Location of Project: USA

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

READY: Augmenting Capacity for Humanitarian Emergencies of Infectious Diseases with Epidemic or Pandemic
Potential

Source of Support: USAID

Total Award Amount: $143,605 Total Award Period Covered: 09/25/2018 - 09/30/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Emerging Pandemic Threats PREDICT-2

Source of Support: USAID
Total Award Amount: $100,000,000.00 Total Award Period Covered: 10/01/2014 - 09/30/2019
Location of Project: Global
Person-Months Per Year Committed to the Cal: 4.56 Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY



Revised 14 September 2020 EXHIBIT B

judgment of the PI or the recipient, should be classified, the Grants Officer shall be notified
immediately.

13. Representations and Assurances.

By accepting funds under this Grant, the recipient assures that it will comply with applicable
provisions of the national policies and statutory/regulatory/executive-based requirements
detailed below.

LIVE ORGANISMS. By signing this agreement or accepting funds under this agreement,
the recipient assures that it will comply with applicable provisions of the following national
policies concerning live organisms:

1) For human subjects:

a) Adhere to the requirements for protection of human subjects per the DoD level terms
and conditions as well as the following DTRA requirements:

b) The recipient shall adhere to DTRA local clause 252.223-9002 — Protection of
Human Subjects (Aug 2010). The full text of this clause is as follows:

All research under this grant involving human subjects must be conducted in
accordance with 32 CFR 219, 10 U.S.C 980, and DoDD 3216.02, as well as other
applicable federal and state regulations. Grantees must be cognizant of and abide by
the additional restrictions and limitations imposed on the DoD regarding research
involving human subjects, specifically as regards vulnerable populations (32 CFR
219 modifications to subparts B-D of 45 CFR 46), recruitment of military research
subjects (32 CFR 219), and surrogate consent (10 U.S.C. 980).

DTRA Directive 3216.01 of June 9, 2010 establishes the DTRA Human Subjects
Protection Program, sets forth the policies, defines the applicable terms, and
delineates the procedures necessary to ensure DTRA compliance with federal and
DoD regulations and legislation governing human subject research. The regulations
mandate that al] DoD activities, components, and agencies protect the rights and
welfare of human subjects of study in DoD-supported research, development, test and
evaluation, and related activities hereafter referred to as “research”. The requirement
to comply with the regulations applies to new starts and to continuing research.

The DTRA directive requires that research using human subjects may not begin or
continue unti] the Defense Threat Reduction Agency’s Research Oversight Board
(ROB) has reviewed and approved the proposed protocol. Grantees and
subcontractors are required to submit a valid federal assurance for their organization
(institution, laboratory, facility) that has been issued by either DoD or the Department
of Health and Human Services, and documentation of review of proposed protocols
by the local Institutional Review Board (IRB) to include consent forms for any
planned research using human subjects to the DTRA ROB for its review through the
Grants Officer’s representative (if assigned) or the Grants Officer. The ROB review
is separate from, and in addition to, local IRB review.

A study is considered to involve human research subjects if: 1} there is interaction
with the subject (simply talking to the subject qualifies; no needles are required); and

Page 8 of 14


OMB Number: 4040-0001
Expiration Date: 12/31/2022

RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Project Director/Principal Investigator

Prefix:[or. | * First Name: ] | Middle Name:{ ]
* Last Name: [Ress | Suttix:[ ]
PositionTitle: [Principal Scientist, Computational Research | Department:[ ]
] Division: |
Street2 [seize 11 ]
“City: fnew York ] County? Parish: [wy ]
*State: [Ny: New Yors ] Province: [ ]
UNITED STATES | * Zip / Postal Code 2517 ]

* Phone Number:[212 3884468 Fax Number: [21238

* E-Mail: [poss @ecohea ial lance .org ]

Credential, e.g., agency login ]

* Project Role: [po/pr ] Other Project Role Category: | ]

Degree Type: [pg

*Attach Biographical Sketch [iz35-Kos

] [Delete attachment | | View Attachment |

Attach Current & Pending Support [12 26-Ko< | [Delete Attachment | [View Attachment |

PROFILE - Senior/Key Person 1

Prefix: [ | * First Name:[Rebext ] Middle Name: [

* Last Name: [Yoana ]  Sutfix:[ ]

Position/Title: Software sng. Depariment:| ]

Organization Name: [Ez ] Division: |

* Street! ]

Street2 [seize 1c ]

“City: fnew ors ] County’ Parish: [ ]

“state: [ys 0 ] Province: | ]
“Country: [usa: UNITED STATES * Zip / Postal Code: ]
* Phone Number:[21238644 50 ] Fax Number: [2123294465 ]

* E-Mail: [poung@ecchea=than.lance.opg l

Credential, e.g., agency login: ]

* Project Role: [ocher Professional ] Other Project Role Category: |senicr Engineer l

Degree Type: fess

Attach Biographical Sketch [ies3

] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support [12 ] [Delete Attachment | [ View Attachment |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

Figure 6. (L-R) Zaidoun Lijazeen, Jamie
Cooksey, Emily Hagan, Dr. Ehab Abu-Basha,
Dr. Mustafa Ababneh, Dr. Whitney Bagge, Dr.
William Karesh, Dr. Borhan Al-Zghoul, and
Mohammad Hussien Alboom in the molecular
biology lab at JUST.

Figure 5. Mohammad Lussien Alboom
demonstrating how to analyse genetic
sequencing results.

Future Subtask(s)
Subtask 2.2 Obtain local permissions and approvals to work with human subjects

o The team is still awaiting approval from the Office for Human Research Protections
(HRPO).

Task 3: Characterize causal factors in animal-to-human transmission of these zoonoses.

Subtask 3.4 Obtutin local permissions and approvals to work with human subjects
Activities and results this quarter:
o The team is still awaiting approval from the Office for Human Research Protections
(HRPO).

Future Subtask(s)
Subtesk
Subtesk

3.2 Conduct behavioral visk factor survers in people

3.3 dnalyce epi causal inference to identify modifiable risk factors

Lask 4: Produce geospatial maps of zoonoses in livestock and poultry and of human zoonotic
transmission risk.

Biological Threats Reduction Program 4


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross

Thrust Area 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Performance Schedule

Description

Task 1: Develop and deploy project infrastructure

]¥1 01-2 [¥1.93-4 [ov a1-2 [ov a3-4

1.1.1 Layout a provision project network

1.1.2 Set up continious integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation

1.1.6 Code review, testing and validation

2.1.5 Write and maintain codebase documentation

2.1.6 Perform code review, testing and validation

Task 2: Engineer Project Database

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.4 Cross-link data sources via common ontologies

1.2.4 Write QA/QC tests for data and perform data cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors

1.2.6 Update data from live sources and clean and maintain data continuously

2.2.1 Extend database schema te include genetic data

2.2.2 Write pipelines to ingest genetic data

2.2.3 Cross-link data sources via common ontologies

2.2.4 Write QA/QC tests for data and perform data cleaning

2.2.5 Update data from live sources and clean and maintain data continuously

Task 3: ML Model Development and Testing
1.3.1 Implement “practical naive” model baseline

1.3.2 Design BART model structure

1.3.3 Design alternative GAM model structure

1.3.4 Implement within-disease, all-disease, and partial-pooled disease models versions

1.3.8 Measure variable contribution by re-fitting

1.3.6 Analyze prediction-level variable contributions and model performance

1.3.7 lteratively update models to improve performance

2.3.1 Develop genetic predictor variable set via neural-network autoencoding

2.3.2 Modify BART and GAM structures to incorporate genetic variables

2.3.3 Implement within-disease, all-disease, and partial-pooled disease models

2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 4: Hybrid Model Development and Testing

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Madify ML model to predict mechanistic parameters

2.4.4 Implement within-disease, all-disease, and partial-pooled models

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze predictionevel variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 lteratively update models to improve performance

ee Pa

13


¢C. Equipment Description

List items and dollar amount for each item exceeding $5,000
Equipment item

Funds Requested ($)

Additional Equipment:

l| Add Attachment lI ll ]

Total funds requested for all equipment listed in the attached file |

TotalEouipment [|

D, Travel Funds Requested ($)
1. Domestic Travel Costs ( Incl. Canada, Mexico and U.S. Possessions)

2.

E. Participant/Trainee Support Costs

Foreign Travel Costs.

Total Travel Cost

Funds Requested ($)

1

ap wen

Tuition/Fees/Health Insurance

Travel |
Subsistence |
Other [ ] [ ]

[___] number of Participants/Trainees

Tracking Number:GRANT13118386

panifirainee Support Coste [

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Biological Threats Reduction Program
PROJECT QUARTERLY REPORT

PROJECT INFORMATION

HDTRA1 2010029 - Reducing the Threat of Middle

East Respiratory Syndrome Coronavirus and Avian

Influenza in Jordan & Strengthening Regional Disease

Surveillance Capacit

Quarter and Dates: Y1, Q2 l (12/29/20 - 03/28/21)
Prepared by: | Dr. William B. Karesh

Project and Title:

OVERALL PROJECT SUMMARY AND OBJECTIVES

Project Objective

Utilizing an international consortium of partners, this effort is a multi-disciplinary One Health
research project to identify causal factors in the transmission of MERS-CoV, AI, and other zoonoses in
Jordan, while simultaneously strengthening local capacity for prevention, detection, and reporting of
these zoonoses in Jordan, Iraq, and Lebanon. Our proposed project will rigorously test the following
initial hypotheses: 1) That livestock species and poultry in Jordan show evidence of infection with
MERS-CoV or AI, respectively and/or other zoonoses. 2) That people regularly exposed to livestock
and/or poultry or their living areas have a greater risk of infection with MERS-CoV, AI, and/or other
zoonoses compared to other community members in Jordan. 3) That some specific practices may
promote exposure to specific animal biohazards (e.g., blood, urine, feces, and/or nasal secretions) and
pose a greater risk of infection with MERS-CoV, AI, and other zoonoses compared to others. This study
involves interviews and longitudinal sampling of humans, livestock, and poultry at animal markets and
farms for MERS-CoV, Brucella spp., and Leptospira spp. (people/livestock) and for AI subtypes
(people/poultry). Partner laboratories in Jordan will screen and characterize these zoonotic pathogens,
and capacity-building cross-trainings will be conducted with visiting laboratory scientists from Irag and
Lebanon. This multifaceted One Health approach will advance scientific knowledge on the risk of
MERS-CoV, AI, and other zoonoses in Jordan, strengthen local zoonotic disease surveillance
capabilities, and enhance scientific partnerships in Jordan, Iraq and Lebanon to prevent, detect, and
teport biological threats.

Biological Threats Reduction Program Page 1 of 5


Principal Investigator 1

Prefix Cr

Name Hoar Rass

Title Principal Scientist Computational
Research

Address 1 460 Vest 34th Street

Address 2 Suite 1704

City Claw York

County/Province —[lY

State ny

Country USA

Zip 10001 - 2317
Phone 2123804460 Ext
Fax 2123004455
Email cetpecccs

Business Official 1

Prefix Dr
Name Aleksei Chmura
Title Authorized Organizational

Reoresentative

Address 1 460 West 34th Street
Address 2 Suite 1701
City Hew York

County/Province THY

State Hy

Country USA

Zip 10001 - 2317
Phone 2123604460 Ext

Fax 2123804465


EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

4 Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat.
Commun. 10, 898, doi: 10.1038/s41467-019-08616-0 (2019).

5 Olival, K. J. ef a. Host and viral traits predict zoonotic spillover from mammals. Nature,
doi: 10.1038/nature22975 (2017).
6 Allen, T. e¢ a/. Global hotspots and correlates of emerging zoonotic diseases. Nat.

Commun. 8, 1124, doi:10.1038/s41467-017-00923-8 (2017).

7 Mock, F., Viehweger, A., Barth, E. & Marz, M. VIDHOP, viral host prediction with
Deep Learning. Bioinformatics (2019).

8 Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod
vectors from evolutionary signatures in RNA virus genomes. Science 362, 577-580,
doi: 10.1126/science.aap9072 (2018).

9 Gu, Y. COVID-19 Projections Using Machine Learning, <https://covid19-
projections.com/about#historical-performance> (2020).

Data Sources

Fitting and testing models will draw from disease outbreak data (counts, timing, and geography)
and predictor data including pathogen traits (disease symptoms, pathogen traits, modes of
transmission), pathogen sources (point of origin, geographic range of potential natural hosts),
environmental context (climatic conditions, ecosystem traits), and socioeconomic context (local
population densities, demographics, transportation networks, healthcare systems). Please see the
Technical Proposal for available data sources.

Tasks/Scientific Goals (Format: Year. Task.Subtask)
YEAR 1
Task 1.1 — Develop and deploy project infrastructure
The awardee shall develop a computational environment that enables continuous model
development and testing and allows for research and models to be reproduced and extended by
external teams. The computational environment will include continuous maintenance and
documentation of project codebase.

Subtasks

1.1.1 Layout a provision project network

1.1.2 Set up continuous integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation

1.1.6 Perform code review, testing and validation

Task 1.2 — Engineer project database
The awardee shall curate, integrate, cross-link, and validate relevant datasets from various
sources into a project database, accounting for differing space and time scales, data formats, and
data completeness.

Subtasks

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.3 Cross-link data sources via common ontologies


BUDGET JUSTIFICATION FOR ECOHEALTH ALLIANCE

EcoHealth Alliance requests a total of $4,975,681 over all-years of the proposed project to
support personnel, travel, equipment, consortium agreements, and applicable indirect costs.

A. Key Personnel

Noam Ross, Ph D., Principal Investigator (3.0 calendar months for all years). Dr. Ross will
oversee the project and be responsible for project management, research, development, design,
analysis, and manuscript writing and conference presentations, as well as communication with
DTRA. We request $25,000 in Y1 due to the high cost of living in New York City and a 5% cost
of living increase in OY L.

Emma Mendelsohn, M.Sc, Research Scientist. (6.0 calendar months for all years). Ms.
Mendelsohn will lead data engineering and contribute expertise to algorithm development. She
will contribute to manuscript writing and conference presentations. We request $42,500 in Y1
with a 5% cost of living increase in OY 1

Robert Young M.Sc, Senior Research Software Engineer. (6.0 calendar months for all years).
Mr. Young will lead infrastructure engineering and contribute expertise to algorithm
development. He will contribute to manuscript writing and conference presentations. We request
$52,500 in Y1 with a 5% cost of living increase in OY 1.

Fringe Benefits

Fringe benefits are calculated as 36.80% of base salary p.a. with $44,160 requested in Y1
calculated from the base salary for all personnel. In OY 1, we budget for a 5% per year cost of
living allowance increase in all salaries.

B. Other Personnel
No funds are requested for other personnel.

C. Equipment
No funds are requested for equipment.

D. Travel
Per recommendations in the BAA, we request travel funds to Nationally Recognized
Conferences and the Annual Technical Review.

Domestic Travel

Domestic travel is requested for one trip per year from New York City per year to Northern
Virginia the Annual Technical Review. Transportation is estimated at $350 per trip along with
the government GSA per diem rates for Northern Virginia ($161 for accommodation and $71 for
meals and incidentals). Additional domestic travel support will facilitate two EcoHealth Alliance
team members presenting at one conference. In OY1, this will be the EEID conference held in
Bozeman, MT in 2022, with transportation costs of $500, $225 for accommodation and $55 for
meals and incidentals. For each trip, $25 per day is estimated in taxi costs and for those that


Grant Training DCP Matrix

DOMAINS

Domain 1

Domain 2

l Domain 3

l Domain 4

COMPETENCIES

Training Task

11.1

1.1.2

2.1.1

2.1.2

2.1.3

3.1.1

3.1.2

3.1.3

4.11

4.1.2

Ex. Task 1.3.2:
Conduct
laboratory-
based
diagnostic
trainings

x

1.2.2. hire and
train field and
lab teams

1.2.5-OY5.2.5.
Train students
&team
through field &
lab activities.

1.3.3-2.3.3.
Train
technicians
from KGMU to
develop
reagents & run
pseudovirus
neutralization
assay.

1.6,1-OY4.6.1
Hospital staff
identified and
trained on
study protocols



Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

Presentations and Publications

1. Presentations and Other Con ¢ Submissions: All Institutes

«© June 7, 2021: Dr. Whitney Bagge presented to the American Association of University Women
(AAUW) on “Preventing the Next Pandemic: Science and Capacity Building.

« June 24, 2021: Dr. William Karesh presented a webinar for the Walter Reed Army Institute of
Research (WRAIR) One Health Branch, including AFRIMS and other overseas labs on “One
Health in the Pandemic Era” and discussed DTRA BTRP projects including this one in Jordan.

2. Publications: All Institutes

No project scientific publications to report as of yet.

Biological Threats Reduction Program Page 7 of 7


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Phase II Technical Proposal
ABSTRACT

Accurately forecasting the impacts of novel biological agents in the early stages of their
emergence is critical to planning control measures for warfighters and civilians. However, at the
beginning of outbreaks, data about pathogen properties and dynamics are often scarce. Machine
learning (ML) techniques generally require large data sets, thus hampering their utility in these
contexts. Our proposed work addresses this challenge by using cross-disease prediction via
transfer learning — utilizing data from previous emergence events to improve early-stage
predictions for future events. We will use partial-pooling to determine the degree to which
information can be shared across diseases according to hierarchical similarities in pathogen and
host properties, and in environmental and population conditions. We will also determine which
properties contribute most to predictive accuracy, providing guidance for prioritizing data-
collection in early outbreak stages. We will test and compare two approaches — pure ML
predictions and hybrid ML/mechanistic models — to determine which provides better predictions
of outbreak size and impacts. We will determine at what point in an outbreak current data
overtakes historical data as a better predictor of outbreak dynamics. Making use of data sets from
multiple host species as well as efficient model-validation techniques, we will expand the size of
available training data and thus models’ predictive capacity.

SCOPE

Objective

Our primary research objectives are to determine the efficacy of ML techniques in predicting
epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats both
intentional (e.g. novel genetically-modified pathogens) and natural (e.g. zoonotic). Specifically,

we will answer three questions that will enable rapid decision-making based on ML models of
epidemic impacts:

Q1: How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

Q2: What variables are most predictive of epidemic impacts at early stages of an outbreak?
Q3: At what point in epidemic growth do hybrid models that incorporate mechanistic

epidemiological components become more accurate than pure ML models?

This work will contribute to fundamental knowledge of properties and variables that contribute
most to predictive accuracy at early-stage stages in emerging outbreak prediction and
forecasting. It will enable the development of robust and deployable predictive models at early
stages of outbreaks, and support informed choices for data collection and model selection.

1


Report Submitted to
Grant/Award #
Organization/Institution
Project Title

Name of Submitting Official
Title of Submitting Official
Email Address

Phone Number
Submission Date
Reporting Period End Date
Reporting Period

Country 1
Country 2
Country 3

SHADED FIELDS ARE REQUIRED

Defense Threat Reduction Agency

HDTRA1-20-1-0026

EcoHealth Alliance

Biosurveillance for Spillover of Henipaviruses and Filoviruses in Rural India
Dr. Jonathan Epstein

Principal Investigator

epstein@ecchealthalliance.org

1.212.380.4471

7/1/2021
June 30th, 2021
Annual
Foreign Spending Country Foreign Spending ($)
55891.3
India

Proceed to Personnel



Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD
members and will be covered under a regional workshop to promote broader uptake of geospatial
mapping as a low-cost tool to enhance disease monitoring programs in the region (Task 5).
Y¥ 1.4.1-02.4.1 Generate maps of laboratory results
¥2.4.2-02.4.2 Generate risk maps of human behavioral risk data

TASK 5; Y1.5-OY2.5: Strengthen local capacity for detection and reporting of MERS-
CoV, AT, and other zoonoses in Jordan, Iraq, and Lebanon through mectings and
workshops.
Strengthening disease detection and reporting capacity is a key contributor to health security and
the ability to target risk factors for threat reduction, The project team is committed to improving
this capacity in Jordan as well as more widely in the region through involvement of scientists and
government officials in Iraq and Lebanon. We will train local staff in proper techniques for all
project activities (Year | and Option Year 1). Each year we will support four students from
medical and veterinary disciplines in Jordan to participate in training, field activities, project
analyses and workshops to provide scientific mentorship and promote the One Health concept.
Jordanian scientists and students will also be trained in laboratory techniques to be deployed in
Jordan by the Human Link laboratory consultant that has led capacity development for MERS-
Co¥ diagnostics in other countries, The awardee shall organize workshops in Jordan open to
scientists from the Ministries of Health and Agriculture from Jordan, Iraq, and Lebanon (Years
1-2, Option Years 1-2). A secure project database will be developed for use in the
epidemiological analyses. The awardee shall provide submission of annual sample repository
information using a DIRA-specified format and shall grant access to all samples collected and
data generated during the course of the project, up to and including at least 12 months after the
project end date. The awardee shall conduct presentations/meetings at times and places specified
in the grant schedule (Year 1 Task 5), including the DTRA Annual Technical Review. In Year |
the awardee shall hold a kick-off meeting to introduce the project objectives and promote buy-in
in study findings and capacity sustainment. Annual reports and stakeholder meetings and
scientific presentations and publications will be used to disseminate results. The project will
reinforce Jordan’s national One Health platform, promoting the One Health concept for multiple
diseases of concern for public and animal health and advancing capacity for hypothesis-driven
research with direct application for policy decisions for threat reduction.

Y¥1,5.1 Conduct project kick-off meeting in Amman with local stakeholders.

¥ 1.5.2 & O1.5.2 Train local project staff in proper techniques.

¥1,5.3-02.5.3 Host annual partners and stakeholders meeting.

Y1.5.4-02.5.4 Complete annual report and share with project partners and local

stakeholders.

¥1.5.5-Y2.5.5 & O1.5.5-02.5.5 Conduct local/regional traming workshops.

¥ 1.5.6- 02.5.6 Data management.

Y1.5.7- 02.5.7 Conduct presentations/meetings at times and places specified in the grant

schedule, including DTRA Annual Technical Review.

TASK 6: Y3.6-OY2.6: Identify specific modifiable risk factors for human infection with
these zoonoses.

The awardee shall integrate the findings of the biological testing (animal and human) and
behavioral surveys in Tasks 1-3, assessing specific animal-related behaviors and practices that
involve exposure to specific animal biohazards (e.g.. blood, urine, feces, and/or nasal secretions)



Enter all patents, invention disclosures, and licenses resulting from this DTRA award that were awarded or applied for during the reporting period
Reporting here does NOT replace the reporting requirements for patents and invention disclosures in the terms and conditions of the award,

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Intellectual Property
Tee

Award # Number Country Application Date ate Issued Abstract or Description Inventors Status

HOIRAL-RETAEE Note oar
[mowa-z0-1-0036
HOTRAL-20-LO02S
(foTRAL 20-0036
HOTRES-20-L026
[jorRa-20-10026
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HOTRAL-20-L0025
(foTRAL 20-0026
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Proceed to Transitions



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Attachment 1 - Statement of Werk (SOW)
Project Title: Predictin Biothreat Impacts from Early-Stage Data via Transfer Learning
Document Date: February 26, 2021

Objective

The project’s objectives are to determine the efficacy of machine learning (ML) techniques in
predicting epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats,
both intentional (e.g. novel genetically-modified pathogens) and natural (e.g. zoonotic). We
define “early stage” as the initial period in an outbreak when only short time-series data are
available, prior to rapid growth and geographic spread, and epidemiological parameters are not
well-established. Specifically, we will answer three questions that will enable rapid decision-
making based on ML models of epidemic impacts:

Q1: How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

Q2: What variables are most predictive of epidemic impacts at early stages of an outbreak?
Q3: At what point in epidemic growth do hybrid models that incorporate mechanistic
epidemiological components become more accurate than pure ML models?

This work will contribute to fundamental knowledge of properties and variables that contribute
most to predictive accuracy at early-stage stages in emerging outbreak prediction and
forecasting. It will enable the development of robust and deployable predictive models at early
stages of outbreaks, and support informed choices for data collection and model selection.

Scope

The awardee proposes a two-year study to determine the efficacy of ML techniques in predicting
epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats. The
awardee team shall focus on the following major goals and milestones:

@ Year 1, Milestone 1 (0-6 months): Create and curate predictor database, project
infrastructure, continuous integration/validation, establish data-imputation and automatic
predictor workflow (Tasks 1.1-1.2).

® Year 1, Milestone 2 (6-12 months): Bayesian additive regression tree (BART) model
fitting and tuning, determination of variable contribution to prediction accuracy (Task
1.3). Document and validate project code and infrastructure (Task 1.4)

®@ Option Year 1, Milestone 3 (12-18 months): Extend the database to include additional
predictors, extend the BART model to ingest genetic data (Tasks 2.1-2.3)

®@ Option Year, Milestone 4 (18-24 months): Construction of hybrid ML/mechanistic model
structure, comparative testing of pure and hybrid version, determination of timing when
current data overtakes transfer data (Task 2.4). Document and validate project code and
infrastructure (Task 2.5)

®@ Option Task 1, (12-18 months); Develop a graphical user interface (GUI) for the model,
allowing users to input of early-stage outbreak data and generate predictions, uncertainty
bounds, and interpretable explanations of influential variables. Document GUI and
validate code.


Subtask 5.2 Train local project staff in proper techniques

All research team members are equipped with CITI training in human subjects research,
and the global team will continue to provide capacity-building through trainings and
workshops on subjects integral to this project and to future public health work, including
best practices regarding research and data security, survey tool development, and risk
communication,

Figure 6. First and second
days of virtual first annual
project training workshop

o. The first annual project training workshop took place over the course of three days,
and was attended by team members, including university students. The first and
second day of the workshop was held virtually, and the third day of the workshop was
held in person.

o The sessions held during the two-day virtual workshop included training on:

= Data management use and storage - facilitated by Jamie Cooksey
and Dr. Whitney Bagge

« Ethical human subject research - facilitated by Emily Hagan and Stephanie
Martinez

= Ethical animal subject research - facilitated by Hani Talafha

= Questionnaire administration - facilitated by Emily Hagan and Stephanie
Martinez

= Safe human and animal sampling - facilitated by Dr. Basil Amarneh and Dr.
Zaidoun Hijazeen


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¢C. Equipment Description

List items and dollar amount for each item exceeding $5,000
Equipment item

Funds Requested ($)

Additional Equipment:

l| Add Attachment lI ll ]

Total funds requested for all equipment listed in the attached file |

TotalEouipment [|

D, Travel Funds Requested ($)
1. Domestic Travel Costs ( Incl. Canada, Mexico and U.S. Possessions)

2.

E. Participant/Trainee Support Costs

Foreign Travel Costs.

Total Travel Cost

Funds Requested ($)

1

ap wen

Tuition/Fees/Health Insurance

Travel |
Subsistence |
Other [ ] [ ]

[___] number of Participants/Trainees

Tracking Number:GRANT13118386

panifirainee Support Coste [

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Robert J. Young
520 Eighth Avenue, Ste. 1201, New York, NY 10018
E-mail: young@ecohealthalliance.org

Professional Preparation

University of Minnesota Computer Science BS 1999
University of New Mexico Computer Science MS 2007
Appointments

Senior Research Software Engineer, EcoHealth Alliance, New York, NY 2020 - present
Bioinformatics Programmer, Institute for Genomic Medicine, CUMC 2016 - 2020
Computer Analyst, Laboratory for Computational Vision, HHMI 2007 - 2016
Research Assistant, CTI-II/NESSI Project, University of New Mexico 2005 - 2007
Research Assistant, University of New Mexico 2004 - 2005
Bioinformatics Specialist, Visual Neuroscience Laboratory, HHMI 2000 - 2002

Publications and Presentations

Healy, M.J., Olinger, R. D., Young, R.J., Taylor, $.E., Caudell, T., Larson, K.W., (2009) Applying
Category Theory to Improve the Performance of a Neural Architecture, Neurocomputing,
72(13-15):3158-3173.

Young, R.J., Ritthaler, M., Zimmer, P., McGraw, J., Healy, M.J., Caudell, T.P., (2007) Comparison
of Adaptive Resonance Theory Neural Networks for Astronomical Region of Interest Detection
and Noise Characterization, International Joint Conference on Neural Networks, 2123-2128.

Young, R.J., Ritthaler, M., Healy, M., Caudell, T.P., Zimmer, P. and McGraw, J. (2006) Adaptive
Resonance Theory Neural Networks for Astronomical Region of Interest Detection and Noise
Characterization, Astronomical Data Analysis Software and Systems (ADASS) XVI, ASP
Conference Series, Vol. XXX, Phoenix, AZ (Poster).

Ritthaler, M., Luger, G. F., Young, R., McGraw, J. and Zimmer, P. (2006) Bayesian Belief
Networks for Astronomical Object Recognition and Classification in CTI-II, Astrenemical Data
Analysis Software and Systems XVI, ASP Conference Series, Vol XXX, Phoenix, AZ

(Poster).

Young, R.J. & Williams, L.R., Psychophysical Demonstration of (An)isotropy in Illusory
Surface Computation, Second Annual CS UNM Student Conference (CSUSC 2006),
Albuquerque, NM (Poster).

Healy, M.J., Olinger, R., Young, R.J., Caudell, T.P., and Larson, K., (2005) Modification of the
ART-1 Architecture Based on Category Theoretic Design Principles, Special Session in the
INNS/IEEE International Joint Conference on Neural Networks, 457-462.

Young, R.J. & Williams, L.R., (2005) Psychophysical Measurement of Illusory Contour Shape,
First Annual CS UNM Student Conference (CSUSC 2005), Albuquerque, NM (Poster).


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Deliverables
e Reports and Documents
e Monthly reports on task status
¢ Monthly cost statement
e Periodic program reviews (virtual and/or in person)
¢ Annual technical report, which shall include
+ Executive Summary
* I. Introduction/Background
+ IL Methodology
+ III. Findings
¢ IV. Discussion/Future Recommendations
* V. Conclusion
+ VI. Appendices
+ a, Snapshots of GUI/how to use
+ b. Data worksheet (summarizing list of data sources used, metadata abstracted,
links, etc.)
* Source and executable software code
* Project databases
« Technical papers describing algorithm design and performance
* Conference presentations

Code Modules and Database Archives
Code and databases will be made available for download and provided electronically via DoD
SAFE and/or on-disk to DTRA at the end of the period of performance.

Note: The data and information collected in this effort is funded on the condition that it will not be
released to another nation or international organization without the specific authority from the
Defense Threat Reduction Agency. Individual or corporate rights originating in the information,
whether patented or not, will be respected; that the recipient government will report promptly to
the Defense Threat Reduction Agency any known or suspected compromise and that the
information will be provided substantially the same degree of security afforded it by the
Department of Defense of the United States. No U.S. Government commitment to sell, lend,
lease, co-develop, or co-produce defense articles is implied or intended. Also, regardless of any
other markings on the document, it will not be downgraded or declassified without written
approval of the originating U.S. agency.


F, Other Direct Costs Funds Requested ($)
Materials and Supplies 3, 000.00

1.

2. Publication Costs
3. Consultant Services |
4. ADP/Computer Services |
5. Subawards/Consortium/Contractual Costs Eo
6. Equipment or Facility Rental/User Fees |
7.
8.
9.
10.

Alterations and Renovations |
| J |
| J |
| | |

Total Other Direct Costs

Funds Requested ($)
Total Direct Costs (A thru F) 182,26

G. Direct Costs

H. Indirect Costs

Indirect Cost Type Indirect Cost Rate (%) Indirect Cost Base (S) Funds Requested ($)

call: Alliance's NICA rate | bs

oo) |

Total Indirect Costs

Cognizant Federal Agency
(Agency Name, POC Name, and

|. Total Direct and Indirect Costs Funds Requested ($)
Total Direct and Indirect Institutional Costs (G + H) 248,402.46

J. Fee Funds Requested (5)

K. Total Costs and Fee Funds Requested ($)
Total Costs and Fee (I + J) 248, 107.46

L. Budget Justification

(Onlyatach one file) yee pTkA M_Budgex sos.itisusion. pat] | | II

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


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William B. Karesh
EcoHealth Alliance, 460 W. 34th St, New York City, NY 10001, USA
E-mail: karesh@ccohcalthalliance.org

Professional Preparation

Clemson University Biology BS 1977

Univ. of Georgia Veterinary Medicine DVM 1982

Zool. Society of San Diego Residency Wildlife Health 1982 - 1984
Appointments

Life Member, Council on Foreign Relations 2016 -
present

Emerging Pandemic Threats Partner Liaison, USAID EPT PREDICT-2 2014 - present
Advisor, WHO Expert Panel on MERS-CoV 2013 - present
Expert, WHO International Health Regulation Roster of Experts 2013 - present
Executive Vice President for Health & Policy, EcoHealth Alliance 2010 - present
President, Working Group on Wildlife Diseases, OIE, France 2008 - present
Co-Chair, Wildlife Health Specialist Group, IUCN, Switzerland 2001 - present
Chief Technical Officer, USAID EPT PREDICT 2009 - 2014

Chief of Party, USAID Global Avian Influenza Network for Surveillance 2006 - 2009
Vice President & Director, Global Health Programs, WildlifeCons. Society 2001 - 2010

Publications

Kandeil A, Gomaa M, Shehata M, El Taweel AN, Mahmoud SH, Bagato O, Moatasim Y, Kutkat
O, Kayed AS, Dawson P, Oui X, Bahl J, Webby RJ, Karesh WB, Kayali G. (2019) Isolation
and characterization of a distinct influenza A virus from Egyptian bats. Journal of Virology
93(2).

Anthony SJ, Johnson CK, Grieg D, Kramer 8, Che X, Wells HL, Hicks AL, Joly D, Wolfe ND,
Daszak P, Karesh WB, Lipkin WI, Morse SS, Predict Consortium, Mazet J, Goldstein T.
(2019) Global patterns in coronavirus diversity. Virus Evolution 3(1).

Machalaba CC, Elwood SE, Forcella S, Karesh WB. Global avian influenza surveillance in wild
birds: A strategy to capture viral diversity. (2015) Emerging Infectious Diseases 21(4):¢1-7.

Alagaili AN, Briese T, Mishra N, Kapoor V, Sameroff SC, de Wit E, Munster VJ, Hensley LE,
Zalmout IS, Kapoor A, Epstein JH, Karesh WB, Daszak P, Mohammed OB, Lipkin WI. (2014)
Middle East Respiratory Syndrome Coronavirus infection in dromedary camels in Saudi
Arabia. mBio 5(2):e00884-14.

Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, Aldrich 8,
Harrington T, Formenty P, Loh EH, Machalaba CC, Thomas MJ, Heymann DL. (2012)
Ecology of zoonoses: Natural and unnatural histories. The Lancet 380(9857):1936-45.

Gaidet N, Caron A, Cappelle J, Cumming GS, Balanca G, Hammoumi §, Cattoli G, Abolnik C,
De Almeida RS, Gil P, Fereidouni SR, Grosbois V, Tran A, Mundava J, Fofana B, El Mamy
ABO, Ndlovu M, Mondain-Monval JY, Triplet P, Hagemeijer W, Karesh WB, Newman SH,
Dodman T. (2012) Understanding the ecological drivers of avian influenza virus infection in
wildfowl: A continental-scale study across Africa. Proceedings of the Royal Society B:
Biological Sciences 279(1731):1131-41.

Morse SS, Mazet JAK, Woolhouse M, Parrish CR, Carroll D, Karesh WB, Zambrana-Torrelio C,
Lipkin WI, Daszak P. (2012) Prediction and prevention of the next pandemic zoonosis. The
Lancet 380: 1956-65.


From: by6)
To: “William B. Karesh"; “Aleksei Chmura’
Ce: by6)

(USA); DTRA Ft Belvoir SI Mailbox SI-FMKA SPS Contracts; "onr_boston@navy.mil"()(6)
b}6}

Subject: Jordan Award
Date: Tuesday, September 29, 2020 4:22:00 PM
Attachments: Terms and Conditions {(DTRA-specific) 14Sep2020 FINAL.pdf

HDTRA1-20-1-0029 Award.pdt
EHA Jordan SOW. pdf

Attached you will find the award documentation for grant HDTRA 1-20-1-0029: Reducing the Threat of Middle Last
Respiratory Syndrome Coronavirus and Avian Influenza in Jordan & Strengthening Regional Disease Surveillance
Capacity. Please confirm once you have received this email, thank you.

Vir

a)

Contract Specialist * Broadleaf

Defense Threat Reduction Agency (DTRA)

Office PhonefPXO) |


Grant Training DCP Matrix

and inventory management of materiel
resources, including supply chain systems,
operations, and infrastructure, or
management/oversight of these processes.

5.1.4 Quality review practices and
procedures

Knowledge, understanding and proficiency
with quality assurance, quality control, and/or
quality management review practices and
procedures, based on national or international
best practices.

5.1.5 Document management

Knowledge, understanding and proficiency
with document and information management
policies and procedures, and program
documentation and records/archival
management processes.

6. Outbreak
preparedness and
surveillance

6,1 Use of operational
guidelines, identification
and analysis of outbreak
situations, and effective
communication with
stakeholders.

6.1.1 Operational guidelines

Knowledge, understanding and proficiency
with development and implementation of
outbreak preparedness, surveillance,
investigation, laboratory practices, and
response planning and operations guidelines,
including case definitions, and demonstrated
ability to use related operational tools such as
emergency preparedness and response plans,
contingency plans, SOPs, and job aids when
available. Special note and care should be



Methodology: We propose to conduct a prospective cohort
A study with repeated surveys at various human-
livestock/poultry interfaces across Northern, Middle, and
Southern Jordan. Each interface will be visited thrice a year
for 3 years (9 total visits per site) or 4 and 12 respectively if
\ option years are exercised.

a 7 During each human-livestock interface visit, livestock (e.g.,
: camels, goats, sheep, cattle) will be opportunistically tested
Hl for evidence of active infection with MERS using nasal
swabs. During the first, fifth, and final visits, livestock will be
opportunistically tested for past infection with MERS,
Brucella spp., and Leptospira spp. using sera. Swab samples
will be pooled by species + site + visit date and tested via
real-time PCR. Serum samples will be tested via Luminex.
During each human-poultry interface visit, poultry will be
sampled for AI using oropharyngeal swabs. During the first,
Figure 1: Regional map oflivecamel __ fifth, and final visits, poultry will be opportunistically tested
(A) and poultry (B) production, 2015. for past infection with AI using sera.

Individuals at each site and its surrounding community will be approached for enrollment at the
first visit. Those providing informed consent will complete a standardized questionnaire (the
previously Jordan-approved and tested PREDICT-2 questionnaire augmented with study-specific
items) during the first visit and complete a shorter questionnaire at each subsequent visit to
account for time-varying factors. Participants will provide oropharyngeal and nasopharyngeal
swabs at each visit and sera at the first, fifth, and final visits. These samples will be tested using
PCR for the swab samples (MERS and AI) and Luminex serology for the serum samples (all 4
zoonoses).

Data will be analyzed to generate geospatial maps of livestock and poultry zoonoses, geospatial
maps of human zoonotic disease risk (including static and seasonal versions), exposure risk
models using causal inference techniques, and prediction models for transmission that can
account for seasonal variations and wide-scale livestock/poultry practice trends.
Implementation of Luminex Serological Technology: Luminex technology for serology and
associated equipment will be acquired for participating laboratories in Jordan. Training
workshops will be held in Jordan for regional scientists, with refresher trainings held annually.

Regional Capacity Building: Iraqi and Lebanese scientists from their Ministries of Health and
Agriculture will be invited to participate in capacity-building workshops in Jordan. Workshops
will cover topics including implementing/using Luminex technology, animal and human
sampling fieldwork, ethical human subjects research, biosafety and biosecurity best practices,
data management and storage, and reporting procedures pursuant to OIE, WHO, IHR, and
GHSA obligations. Advance technical training for key individuals is proposed for option years.

Our research project will serve multiple goals and objectives of the CBEP mission, by: |)

engaging partner country scientists in hypothesis-driven research; 2) supporting biosurveillance
capacity building by enhancing partner capability to detect and report select agents; 3) enhancing

EcoHealth Alliance 3


Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD

Statement of Work

Project Title: Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and
Avian Influenza in Jordan & Strengthening Regional Disease Surveillance Capacity.

Document Date: 18 March 2020.

Objective: The objective of this grant is to reduce the threat of high-consequence zoonotic
pathogens in Jordan and improve regional disease surveillance capacity. Jordan faces risk of
several zoonoses of concern to human and animal health, including Middle East Respiratory
Syndrome (MERS-CoV) and Avian Influenza (AI), with fundamental knowledge and capacity
gaps around the distribution and determinants of zoonoses. Recent work by our team detected
MERS-CoV in camels and people in Jordan, suggesting ongoing and unmitigated transm:
risk of a priority pathogen, The proposed study will generate critical advances in determining the
presence of zoonotic pathogens in the country and opportunities for public health intervention.
Through a prospective human cohort study coordinated with animal sampling, we will conduct
biological and behavioral surveillance in five regions of Jordan with livestock production
interfaces to determine the presence and risk factors for MERS-CoV, AT, brucellosis, and
leptospirosis to identify modifiable risk factors. By testing three core policy-relevant hypotheses
and providing multi-disciplinary training opportunities, the awardee shall enhance scientific
capacity in Jordan and support disease detection and reporting in Jordan, Iraq and Lebanon.
Taking a coordinated, multi-hazard approach to threat reduction, the proposed study will add
critical understanding of presence and risk factors for zoonotic diseases in Jordan and advance
scientific capacity and application of the One Health concept to counter biothreats in the region.

Scope: The awardee proposes a five-year One Health study of zoonotic diseases in Jordan. The
awardee team shall focus on the following major goals and milestones:
1) Determine the presence of MERS-CoV, AI, and other zoonoses among livestock and
poultry in Jordan: Implement animal study (Y1-OY1); Conduct PCR and serology testing
(YT-OY2)
2) Determine whether (and if so, the extent to which) humans at human-livestock/poultry
interfaces display evidence of infection with these zoonoses: Implement human cohort
study (¥1-OY1): Conduct PCR and serology testing (YI-OY2)
3) Characterize causal factors in animal-to-human transmission of these zoonoses:
Implement behavioral risk survey (Y1-OY 1); Conduct epidemiological analyses (Y1-OY2)
4) Produce geospatial maps of zoonoses in livestock and poultry and of human zoonotic
transmission risk: Generate geospatial distribution and risk maps (Y1-OY2)
5) Strengthen local capacity for detection and reporting of MERS-CoV, AT, and other
zoonoses in Jordan, Iraq, and Lebanon: Host workshops (Y1-2, OY1-2); Submit reports
and publications (Y1-OY2); Attend presentations/meetings (Y1-OY2)
6) Identify specific modifiable risk factors for human infection with these zoonoses:
Conduct causal interference analyses (Y3-OY2); Policy recommendations on modifiable
risk factors (¥3-OY2)
This research will generate critical advances in detecting the presence of and exposure risk to
high-consequence zoonotic viruses and bacteria across geographic regions and interfaces in
Jordan, contributing to enhanced understanding of modifiable risk factors to inform government
authorities on prevention activities to reduce disease threats.



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data
cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location
information

1.2.6 Update data from live sources and clean and maintain data continuously

Task 1.3 —- Machine Learning (ML) model development and testing
The awardee shall design, implement, test, and analyze performance of ML model structures and
iteratively improvw them.
Subtasks
1.3.1 Implement “practical naive” model baseline
1.3.2 Design Bayesian additive regression tree (BART) model structure
1.3.3 Design alternative generalized additive model (GAM) structure
1.3.4 Implement within-disease, all-disease, and partial-pooled disease model versions
1.3.5 Measure variable contribution by re-fitting
1.3.6 Analyze prediction-level variable contributions and model performance
1.3.7 Iteratively update models to improve performance

OPTION YEAR:
Task 2.1 — Maintain and document project infrastructure
In the option year, the awardee shall continue to perform maintenance and documentation of
project codebase to ensure reproducibility and reusability of additional components
Subtasks
2.1.1 Write and maintain codebase documentation
2.1.2 Perform code review, testing and validation

Task 2.2 — Extend project database
In the option year, the awardee shall extend the database to include additional predictors and
continuously maintain and update to accumulate more data from upstream sources.
Subtasks
2.2.1 Extend database schema to include genetic data and auto-encoded variables
2.2.2 Write pipelines to ingest genetic data
2.2.3 Cross-link data sources via common ontologies
2.2.4 Write QA/QC tests for data and perform data cleaning
2.2.5 Update data from live sources and clean and maintain data continuously

Task 2.3 — Extend BART model
In the option year, the awardee shall extend the BART model to ingest genetic data and test the
contribution of this data in enhancing or recapitulating pathogen trait data.
Subtasks
2.3.1 Develop genetic predictor variable set via neural-network autoencoding
2.3.2 Modify BART and GAM structures to incorporate genetic variables
2.3.3 Implement within-disease, all-disease, and partial-pooled disease model versions
2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 2.4 — Hybrid ML-mechanistic model development and testing




Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross

Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Gantt Chart

Description

Year 1, Milestone 1

]¥1at.2 [¥1.034 | ¥2 01-2 |y3 a34

1.1.1 Layout a provision project network

1.1.2 Set up continious integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

1.2.1 Design database schema

1.2.2 Write pipelines to ingest data

1.2.3 Cross-link data sources via common ontology

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location information

1.2.6 Update data from live sources and clean and maintain data continuously

1.3.1 Establish quantitative and qualitative metrics of madel performance

1.3.2 Implement ‘practical naive” model baseline

1.3.3 Design model structure

1.3.4 Design altemative structure

1.3.5 Implement within-disease, all-disease, and partial-pooled disease model versions

1.3.6 Measure variable contribution by refitting

1.3.7 Analyze prediction-level variable contributions and model performance

1.3.8 lteratively update models to improve performance

1.4.1 Write and maintain initial codebase documentation

1.4.2 Perform code review, testing, and validation

Option Year 4 (Option 1)

2.1.4 Extend database schema to include genetic data

2.1.2 Write pipelines to ingest genetic data

2.1.3 Cross-link data sources via common ontologies

2.1.4 Write QA/QC tests for data and perform data clearing

2.1.5 Update data from live sources and clean and maintain data continuously

2.2.1 Develop genetic predictor variable set via neural-network autoencoding

2.2.2 Modify BART and GAM structures to incorporate genetic variables

2.2.3 Implement within-disease, all-disease, and partial-pooled disease models

2.2.4 Compare performance of genetic, pathogen-trait, and combined models

2.4.1 Establish quantitative and qualitative metrics of model performance

2.4.2 Design mechanistic model structure

2.4.3 Determine mechanistic model prior forms

2.4.4 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.5 \mplement within-disease, all-disease, and partia-pooled disease model versions

2.4.6 Measure variable contribulion by re-fitling

2.4.7 Analyze prediction-level variable contributions and model performance

2.4.8 Compare Hybrid and ML performance along outbreak time-series progression

2.4.9 lteratively update models to improve performance

2.5.1 Write and maintain initial codebase documentation

2.5.2 Perform code review, testing, and validation

Option Task (Option Il)

3.1.1 Port model into portable framework usable for standalone or web-based interaction

3.1.2 Optimize for rapid response time and application size

3.1.3 Design input interface

3.1.4 Design visualizations for predictions, uncertainty, and variable importance

3.2.1 Create GU! documentation

3.2.2 Perform code review, testing, and validation



HDTRA1-14-24-FRCWMD-BAA
CBEP = Thrust Area 6 - Cooperative Counter WMD Research
with Global Partners FRCWMD

“Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza
in Jordan & Strengthening Regional Disease Surveillance Capacity”

Abstract: In line with Thrust Area 6 - Cooperative Counter WMD Research with Global
Partners FRCWMD, we propose a multi-disciplinary One Health research project to identify
causal factors in the transmission of Middle East Respiratory Syndrome Coronavirus (MERS),
avian influenza (AI), and other zoonoses from animals to humans in Jordan, while
simultaneously strengthening local capacity for prevention, detection, and reporting of these
zoonoses in Jordan, Iraq, and Lebanon. Little is known about the transmission routes of MERS,
Al, and other key zoonoses at human-livestock/poultry interfaces in Jordan. To fill this gap and
contribute to biological threat reduction, we propose a hypothesis-driven One Health research
project to characterize exposure risks for MERS and AI in Jordan using epidemiological causal
inference techniques and improving serological technology. This prospective cohort study
involves interviews and longitudinal sampling of humans and opportunistic non-lethal sampling
of livestock and poultry at animal markets, farms, and abattoirs for MERS, Brucella spp., and
Leptospira spp. (humans/livestock) and for AI subtypes (humans/poultry). Partner laboratories in
Jordan will screen and characterize these zoonotic pathogens, and capacity-building cross-
trainings will be conducted with visiting laboratory scientists from Iraq and Lebanon. This
multifaceted One Health approach will advance scientific knowledge on the risk of MERS, AI,
and other zoonoses in Jordan, strengthen local zoonotic disease surveillance capabilities, and
enhance scientific partnerships in Jordan, Iraq and Lebanon to prevent, detect, and report MERS,
Al, and other zoonotic threats.

Background: There have been over 2,200 cases and 790 deaths across 27 countries from MERS
since 2012. Although most cases occurred in the Kingdom of Saudi Arabia (KSA), the first
known human infections occurred in Jordan. Most KSA cases have been healthcare-associated,
stemming from secondary generations of transmission. Identification of factors involved in
primary transmission may inform interventions to prevent future primary cases and therefore
prevent them from seeding healthcare or community outbreaks. Camels are the presumptive
source of primary human MERS infections; however, the exact mechanisms of transmission and
the possible role of other livestock species are unclear. These uncertainties highlight the need to
better understand the causal factors of primary human infection with MERS at human-livestock
interfaces. Understanding modifiable risk factors may lead to development of public health
interventions that can prevent animal-te-human transmission of zoonoses.

Avian influenza viruses circulate worldwide among migratory bird populations and occasionally
spill into poultry at mass production sites, farms, or backyard coops. Although not every AI
subtype is capable of infecting humans or causing severe illness, there have been worrisome
outbreaks. AI A/HS5N1 has been detected in humans in 16 countries since 2003, resulting in 860
cases and 454 deaths by July 2018. Of these, nearly 42% were in neighboring Egypt. Due to
antigenic shifts typical of the influenza viral genome, there is always a possibility of new Al
subtypes emerging capable of infecting humans. In Jordan, the rapidly expanding poultry
industry driven by ever-increasing demand poses a distinct global health security risk.

EcoHealth Alliance 1


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

REFERENCES

1

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MacDiarmid, S.C. et al, Handbook On Import Risk Analysis for animals and animal
products: quantitative risk assessment. Vol. 2 (Office International des Epizooties, 2004).

Moura, J. A., McManus, C. M., Bernal, F. E. M. & de Melo, C. B. An analysis of the
1978 African swine fever outbreak in Brazil and its eradication. Rev. Sci. Tech. 29, 549-
$63, doi:10.20506/rst.29.3.1992 (2010).

Delgado, J. et ai. U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing
Controls. Risk Anal. 37, 1768-1782, doi:10.111 L/risa.12704 (2017).

Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat.
Commun. 10, 898, doi: 10.1038/s41467-019-08616-0 (2019).

Olival, K. J. ef a. Host and viral traits predict zoonotic spillover from mammals. Nature,
doi:10.1038/nature22975 (2017).

Allen, T. ef af. Global hotspots and correlates of emerging zoonotic diseases. Nat.
Commun. 8, 1124, doi:10.1038/s41467-017-00923-8 (2017).

Mock, F., Viehweger, A., Barth, E. & Marz, M. VIDHOP, viral host prediction with
Deep Learning. Bioinformatics (2019).

Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod
vectors from evolutionary signatures in RNA virus genomes. Science 362, 577-580,
doi:10.1126/science.aap9072 (2018).

Gu, Y. COVID-19 Projections Using Machine Learning __, <https://covid19-
projections.com/about#historical-performance> (2020).

Hill, J., Linero, A. & Murray, J. Bayesian Additive Regression Trees: A Review and
Look Forward. Annu. Rev. Stat. Appl. 7, doi:10.1146/annurev-statistics-03 1219-041110
(2020).

Wood, S. N. Generalized additive models ; an introduction with R. Second edition. edn,
(CRC Press/Taylor & Francis Group, 2017).

van Buuren, S. Flexible Imputation of Missing Data, Second Edition.
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Lundberg, S. M., Erion, G. G. & Lee, S.-I. Consistent Individualized Feature Attribution
for Tree Ensembles. arXiv e-prints, arXiv: 1802.03888 (2018).
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Jones, K. E. et af. Global trends in emerging infections diseases. Nature 451, 990-993,
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Shaw, L. P. e¢ af., doi:10.1101/670315 (2020).

Dobson, A. P., Brierley, L., Pedersen, A. B. & Woolhouse, M. E. J. Tissue tropism and
transmission ecology predict virulence of haman RNA viruses. PLOS Biology 17,
doi: 10.137 1/journal.pbio.3000206 (2019).


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

We will develop algorithms te predict the dynamics and impacts of outbreaks of new
infectious diseases, based on data available at carly stages of outbreaks. In doing so, we will
answer the following key questions.

e How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

e What variables are most predictive of epidemic impacts at early stages of an outbreak?

e At what point in epidemic growth do hybrid models that incorporate mechanistic
epidemiological components become more accurate than pure ML models?

Algorithmic Methods:

We will test the ability to ML methods incorporating partial pooling to predict impacts from a
new outbreak of disease. Our primary model structure will be built around Bayesian additive
regression trees (BARTs)'°. BARTs provide three features appropriate for this application: First,
they can provide both high-quality predictions as well as robustness in relatively data-poor
scenarios. Secondly, they have explicit uncertainty structures is better able to characterize error
than other regression tree methods that do not. Thirdly, they are able to incorporate hierarchical
partial pooling. In some scenarios where data are smooth rather than categorical, generalized
additive models (GAMs) can offer similar performance and similar features'!, and we will test
these as an alternate formulation.

We will parametrize the models using data on pathogen traits (disease symptoms, pathogen
traits, modes of transmission), pathogen sources (point of origin, geographic range of potential
natural hosts), environmental context (climatic conditions, ecosystem traits), and socioeconomic
context (local population densities, demographics, transportation networks, healthcare systems),
as well as time-series data of infections from the earliest phase of the outbreaks. Not all these
data are typically available at the early stages of a new bioagent outbreak, and are in many cases
incomplete even in the case of historical outbreaks. Thus, we will use multiple imputation” in all
models to allow for model fitting, prediction and assessment of uncertainty under incomplete
data.

We will explore multiple model structures, but all formulations will be fit and tested under three
conditions: within-disease forecasting where models are fit only on data from a single disease
and tested at ability to predict impacts from that disease, a//-disease forecasting where models
are fit on data from a// diseases and tested against ability to predict each disease, and partial-
pooled where models are fit all models from all diseases but parameter sharing across diseases
are partially pooled. (See ‘Model Evaluation and Validation’ below)

While ML approaches are best for predictive power, they do not have the flexibility to be used
for alternate scenarios or intervention evaluation that traditional compartmental models do. We
will compare "pure" ML algorithms to a hybrid approach using ML to generate parameters for
mechanistic epidemiological models. In this hybrid case, we will use the ML model to predict
prior parameters for compartmental epidemiological model using the same data as used in the

3


Grant Training DCP Matrix

5.1.4 Quality review practices and
procedures

Knowledge, understanding and proficiency
with quality assurance, quality control, and/or
quality management review practices and
procedures, based on national or international
best practices.

5.1.5 Document management

Knowledge, understanding and proficiency
with document and information management
policies and procedures, and program
documentation and records/archival
management processes.

6. Outbreak
preparedness and
surveillance

6,1 Use of operational
guidelines, identification
and analysis of outbreak
situations, and effective
communication with
stakeholders.

6.1.1 Operational guidelines

Knowledge, understanding and proficiency
with development and implementation of
outbreak preparedness, surveillance,
investigation, laboratory practices, and
response planning and operations guidelines,
including case definitions, and demonstrated
ability to use related operational tools such as
emergency preparedness and response plans,
contingency plans, SOPs, and job aids when
available. Special note and care should be
given to documents designed for health care
workers directly combatting an outbreak.

6.1.2 Identification and analysis

Knowledge and proficiency with principles and
processes for developing and maintaining
disease outbreak situational awareness
capabilities, including coordination and



Please fill out the requested information on all tabs (click on each bottom colored tab or
sheet} labeled Cover Page, Personnel, Publications, Presentations, Impacts, Intellectual
Property, and Transitions. For annual reports, please only describe DTRA funded work for

the reporting period (July 1, 2019 - June 30, 2020}.
Tab Description

Providing information about the project participants and collaborating organizations allows an assessment of

performance in promoting partnerships and collaborations.

The products from an effort demonstrate the excellence of the research and efficacy with which the results are being

communicated to colleagues, potential users, and the public. Publications are the characteristic product of research.
blicatio If there is nothing to report during this reporting period, the awardee shall state "Nothing to Report".

The products from an effort demonstrate the excellence of the research and efficacy with which the results are being

communicated to colleagues, potential users, and the public. Many projects (thought not all) may develop products

other than publications. In all cases, if there is nothing to report during this reporting period, the awardee shall state
Presentations "Nothing to Report”.

knowledge, enlarges the pool of people trained to develop that knowledge and techniques or put it to use, and

improves the physical, institutional, and information resources that enable those people to get their training and

perform their functions. In all cases, if there is nothing significant to report during this reporting period, the awardee

shall state "Nothing to Report”.
Information on any intellectual property demonstrates the value of protectable innovative ideas resulting from

sponsored research and includes patents, inventions, and licenses. Submission of this information is not a substitute
for other invention reporting requirements under the awards terms and conditions. In all cases, if there is nothing
ee era ET es) clas ge Significant to report during this reporting period, the awardee shall state "Nothing to Report".

Refers to cases where knowledge resulting from your DTRA sponsored effort will be further developed for, or will be
Transitions used in, a technology application. Transition sponsors can be entities in DoD, other federal agencies, or industry.



From: Aleksei Chmura

To: a)

ot
b)(6) [DIRA Ft Belvoir SI Mailbox S-FMKA SPS
Contracts; ont bostonainavy. ml, Joe Ricard

Subject: [Non-DoD Source] Re: HDTRA1-20-1-0026 Award

Date: Friday, September 25, 2020 8:59:49 AM

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticily of all links contained within the message prior to copying and pasting the address to. a Web browser.

I confirm receipt
Many thanks most,
Sincerely,

-Aleksci

Aleksei Chmura, PhD
Chief of Staff

Ecollealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

1 1,212.380.4473 « tel:1.212.380.4469 > (office)
Pe } (mobile)
Caution-www.ccohealihalliance.org < Caution-htp:/Avww ecoheulthalliance.org >

Ecollealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Sep at 08:04 Kee)
Caution-mailt > Whole:

Allached you will find award documentation for grant IIDTRA1 -20-1-0026. Please confirm once you have
received this email. Thank you!

Vir

bx) ontractor

Contract Specialist + Broadleaf
Defense Threat Reduction Agency (DTRA)
Office Phone: (571) 616-4277


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

e Pathogen Trait Data consists of information about pathogen taxa, transmission mode,
structural, and biological traits. Relevant traits for pathogens, and have been largely
aggregated for viruses by our Host-Pathogen Phylogeny Project® and parallel projects for
bacterial pathogens'* and non-zoonotic human viruses!®.

e Pathogen Genetic Data consist of genetic sequence of representative samples of
individual pathogen species. Representative sequences of nearly all relevant pathogens
are available via genetic repositories GenBank, ICTV, or GISAID. We expect pathogen
data at the scale of pathogen species to largely predict similar information as pathogen
traits, but genomic data may be faster to use in cases of rapid sequencing of novel
pathogens. We will test whether incorporating genetic data into model features via
neural-network autoencoders'’ provides improved performance over separate
representation of pathogen traits (e.g., taxonomic classification, transmission mode), or
other representations of genetic data such as phylogeny. Whether this performance is
additive, or whether these two types of information are redundant. Even if redundant,
genetic data may improve predictive capacity in the case of new pathogens where
pathogen traits have not been fully delineated. Rich inputs from genetic data will be
integrated using neural-network encoders.

¢ Source Data refers to intelligence about the origin of the disease, which includes location
and mode of origin (environmental, zoonotic host species, laboratory or accidental
release). In many cases source data is unknown, particularly in the early stages of a new
outbreak event. For zoonotic-origin diseases, the pool of known host species and their
geographic provide probabilistic information on the source. Our own Host-Pathogen
Phylogeny Project aggregates host traits for all known mammalian diseases hosts.>

«© Environmental Data refers to information describing the conditions in which the disease
may spread. These will include local climate variables from the BIOCLIM database,
land-use coverage (HYDE), and human and livestock population densities (World Bank,
FAO).

© Socioeconomic Data these include economic activity, governance, medical and veterinary
capacity (World Bank, FAQ), and transportation network connectivity in the region of
disease spread.

Programmatics
All work will be undertaken by the project prime, EcoHealth Alliance (EHA). EHA has a

proven track record of project management with U.S. government agencies including USAID,
DTRA, DHS, NIH, and NSF, ranging from global epidemiological research and surveillance
programs to small-to-medium scale technology development.

Our management approach is to bring production-quality software development practices to data
science for research and development. We use agile development methodology, with weekly
development meetings, monthly project status reviews, and regular organization-wide review
seminars to gather feedback from our broader interdisciplinary expertise. All tasks in the

q


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Task 1.2 — Engineer Project Database: Data engineering consists of curating, integrating and
cross-linking and validating relevant data sets from various sources into a project database. This
allows data with different sources, space and time scales, formats, and completeness to be used
in common models establishes a database that can be used as a common resource.

Subtasks

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.3 Cross-link data sources via common ontologies

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data
cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location
information

1.2.6 Update data from live sources and clean and maintain data continuously

Task 1.3 -ML Medel Development and Testing: The core ML development task consists of
designing, implementing, testing, and analyzing performance of ML model structures and
iteratively improving them.

Subtasks

1.3.1 Implement “practical naive” model baseline

1.3.2 Design Bayesian additive regression tree (BART) model structure

1.3.3 Design alternative generalized additive model (GAM) structure

1.3.4 Implement within-disease, all-disease, and partial-pooled disease model versions
1.3.5 Measure variable contribution by re-fitting

1.3.6 Analyze prediction-level variable contributions and model performance

1.3.7 Iteratively update models to improve performance

OPTION YEAR:

Task 2.1 — Develop and deploy project infrastructure: In the option year we will continue to
perform maintenance and documentation of project codebase to ensure reproducibility and
reusability of additional components

Subtasks
2.1.1 Write and maintain codebase documentation
2.1.2 Perform code review, testing and validation

Task 2.2 — Engineer Project Database: In the option year we will extend the database to
include additional predictors and continuously maintain and update to accumulate more data
from upstream sources.

Subtasks

2.2.1 Extend database schema to include genetic data and auto-encoded variables

2.2.2 Write pipelines to ingest genetic data

2.2.3 Cross-link data sources via common ontologies

2.2.4 Write QA/QC tests for data and perform data cleaning


RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Senior/Key Person 2

Prefix: [ ] 7 First Name: [Enns ] Middle Name: [

* Last Name: [x | Suffix:[ ]

Position/Title: gientist Department: | ]
Organization Name: [Ecolicall: Rlliance ] Division: |

* sweet: [é3 Lh 8 ]

Street2 [seize 1c ]

“City: fnew ors ] County’ Parish: [ ]

“State: [wy: New Yoos ] Province: | ]

* Country: [11

UNITED STATES

* Zip / Postal Code:

* Phone Number:[21238644 50 ] Fax Number: [2123294465 ]

+ E-Mail: [nende_solingecohea: Chal Lance ang ]

Credential, e.g., agency login: ]

roh Scientist

* Project Role: [ocher Professional ] Other Project Role Category: [7

Degree Type: |

Attach Biographical Sketch [iese

] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support | ] [Delete Attachment | [ View Attachment |

PROFILE - Senior/Key Person 3

* First Name:[Peter ] Middle Name: | ]
] Suttix:[ ]
Position/Title: u Department: [ ]
Organization Name Health Alliance ] Division: [
*Streett: |46¢ Wess 240h Surees ]
Street2: |s.e 1761 ]
“City, rew yore County/ Parish: [ ]
*State: [yi new yore ] Province: | ]

* Country: [usa: UNTTRD STATRS

* Phone Number:[1212 2204466 Fax Number: [12123594445 ]

* E-Mail: [saszakSecohea_tha.-iance.org ]

Credential, e.g., agency login

szak ]

* Project Role:

] Other Project Role Category:

Senicr Adviser

Degree Type: [rit ]

Attach Biographical Sketch [1241 pascak_= l[ ] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support [1242 pascak_c6?.pd | | [Delete attachment | | View Attachment |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Grant Training DCP Matrix

DOMAINS
Domain 1 l Domain 2 l Domain 3 l Domain 4
COMPETENCIES
Training Task [111 [112[211 212 [223 [3411 [312 [323 [314 [411 [412 [413
Task 5.5.1 x x x x x
Conduct local-
regional
training
workshops
DOMAINS
Domain 5 Domain 6 Domain 7 Domain 8
COMPETENCIES

Training Task 5.1.1 | 5.1.2 | 5.1.3 5.1.4 5.15 6.1.1 6.1.2 6.1.3 7.11 7.1.2 7.1.3 8.1.1 8.1.2 8.1.3
Task 5.5.1
Conduct local-
regional
training
workshops



HDTRAI2010029

Page 4 of 10

Section C - Descriptions and Specifications

CLAUSES INCORPORATED BY FULL TEXT

252.601-9002. GRANT REFERENCE INFORMATION (MAY 2009)

a. This grant is awarded as a result of Broad Agency Announcement (BAA) HDTRAI-11-16-BRCWMD-
BAA, Research and Development Enterprise, Basic and Applied Sciences Directorate, Basic Research for
Combating Weapons of Mass Destruction (C-W MD).

b. CFDA #: 12.351

c. Authority: 10 U.S.C 2358 as amended.

(End of Clause)


OMB Number: 4040-0001
Expiration Date: 12/31/2022

APPLICATION FOR FEDERAL ASSISTANCE 3. DATE RECEIVED BY STATE | State Application Identifier
SF 424 (R&R) | ll
1. TYPE OF SUBMISSION 4. a. Federal Identifier [

[_]Pre-application [XJApplication [“]Changed/Corrected Application | b. Agency Routing Identifier |

2, DATE SUBMITTED Applicant Identifier

[ ] [ Sacre eo

5, APPLICANT INFORMATION Organizational DUNS: [077 J
Legal Name: [ucolieal=h Alliance ]
Department: [ ] Division ]

Street: [4 34th Street |

Street2: [suite 2702

City [ fork ] County Parish ]

State: | NY: Mew York ] Province: | ]
Country: | USA: UNITED STATES ] ZIP / Postal Code: [19991-2317 ]

Person to be contacted on matters involving this application

Prefix (be, First Name: [cam | Midste Name: [—S—*dY

LastName: Rows ]  Suttix: |

Position/Title: [princi pa at, Camputationa: Research |

Street: [z

Steet2: [size 1701

City: [New vor ] County / Parish: | ]

State: [ NY: New < | Province: [ ]
Country: | USA: UNITED STATES ] ZIP‘ Postal Code: Znnn= 232% |
Phone Number: [2123594469 ] Fax Number: [z ]

Email: [ressfecchealthalliance erg ]
6. EMPLOYER IDENTIFICATION (Ef) or (TIN)
7. TYPE OF APPLICANT: | M: Nonpeolis with S012 IRS Suacus
Other (Specify): { ]
Small Business Organization Type ["] Women Owned _[[] Socially and Economically Disadvantaged

21172849

> than Ingiatucuon of Fig

> Educasionl

8. TYPE OF APPLICATION: If Revision. mark appropriate box(es).
DJ New  [_] Resubmission (DJA. Increase Award [] B. Decrease Award[_]C. Increase Duration [_]D. Decrease Duration
[_] Renewal [7] Continuation [_] Revision CIE. Other (specity):[ ]
's this application being subrvitied to other agencies? Yes[] No[<J_ What other Agencies? |
9, NAME OF FEDERAL AGENCY: 10, CATALOG OF FEDERAL DOMESTIC ASSISTANCE NUMBER: |1
Defense Threal. Reduclion Agency J] TILE: foziencific Reseavcl: Combacing Weapons of Mas

11. DESCRIPTIVE TITLE OF APPLICANT'S PROJECT:

Inredicning Biothreat Tmpas

From Rarly

Stage Dara via Transfer Learning

12, PROPOSED PROJECT: 13. CONGRESSIONAL DISTRICT OF APPLICANT
Start Date Ending Date
] [ ef 30/ 2022 ] [errata

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has: none
Investigator: Robert Young

Support: 2 Current (1 Pending LJ Submission Planned in Near Future (_] *Transfer of Support
Project/Proposal Title:

REPEL: Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DTRA

Total Award Amount: Total Award Period Covered: 04/09/20 19-03/09/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 6 Acad: Sumr:

Support: LJ Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:

Source of Support: DHS

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: L] Current L] Pending LJ Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: C1] Current L] Pending LJ Submission Planned in Near Future (_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS

NECESSARY
AAI



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Enter awards, honors, promotions, or graduations that occurred during the reporting period for the personnel contributing to this DTRA project.

Instructions

Award #

Exomaie

Select the impact
(hono#] type fram the
rap dawn list, Select

Nothing to Report’ if
there were na impacts.

Impact Type

Best Poster

Impact Description
Best Geaduate Stadent Poster at the
American Physical Society sinter
conference

Select the personnel

Enter the FULL

type for the individual narne of the

wha received the

wardor honor,

Personnel Type

Groduote Student

individual who
received the award,

Awardee Name

John Savth

Enter the date the
erard or honar
was received in
manth/day/year
format,

Impact Date

9/20/2019

Impact Details/Website

MDTRAL-20-1-0026

Nothing to Report

HDTRAI-20-1-0026

HOTRAL 20 1 0026

HDTRAI-20-1-0026

HOTRAL-20-1-0026

MDTRAI-20-1-0026

HOTRAL-20-1-0026

HOTRAI-20-1.0026

MD IRAL-20-1-0026

[HOTRAI-20-1.0026

MDTRAI-20-1-0026

HOTRAI-20-1-0026

MDTRAL-20-1-0026

HDTRAI-20-1-0026

HOTRAL 20 1 0026

HDTRAI-20-1-0026

HOTRAL-20-1-0026

MDTRAI-20-1-0026

HOTRAL-20-1-0026

HOTRAI-20-1.0026

MD IRAL-20-1-0026

[HOTRAI-20-1.0026

MDTRAI-20-1-0026

HOTRAI-20-1-0026

Proceed to Intellectual Property



OMB Number: 4040-0001
Expiration Date: 12/31/2022

RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Project Director/Principal Investigator

Prefix:[or. | * First Name: ] | Middle Name:{ ]
* Last Name: [Ress | Suttix:[ ]
PositionTitle: [Principal Scientist, Computational Research | Department:[ ]
] Division: |
Street2 [seize 11 ]
“City: fnew York ] County? Parish: [wy ]
*State: [Ny: New Yors ] Province: [ ]
UNITED STATES | * Zip / Postal Code 2517 ]

* Phone Number:[212 3884468 Fax Number: [21238

* E-Mail: [poss @ecohea ial lance .org ]

Credential, e.g., agency login ]

* Project Role: [po/pr ] Other Project Role Category: | ]

Degree Type: [pg

*Attach Biographical Sketch [iz35-Kos

] [Delete attachment | | View Attachment |

Attach Current & Pending Support [12 26-Ko< | [Delete Attachment | [View Attachment |

PROFILE - Senior/Key Person 1

Prefix: [ | * First Name:[Rebext ] Middle Name: [

* Last Name: [Yoana ]  Sutfix:[ ]

Position/Title: Software sng. Depariment:| ]

Organization Name: [Ez ] Division: |

* Street! ]

Street2 [seize 1c ]

“City: fnew ors ] County’ Parish: [ ]

“state: [ys 0 ] Province: | ]
“Country: [usa: UNITED STATES * Zip / Postal Code: ]
* Phone Number:[21238644 50 ] Fax Number: [2123294465 ]

* E-Mail: [poung@ecchea=than.lance.opg l

Credential, e.g., agency login: ]

* Project Role: [ocher Professional ] Other Project Role Category: |senicr Engineer l

Degree Type: fess

Attach Biographical Sketch [ies3

] [Delete Attachment | [ View Attachment |

Attach Current & Pending Support [12 ] [Delete Attachment | [ View Attachment |

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Quarterly Report: HDTRA12010029 - Year 1, Quarter 2 (12/29/20 - 03/28/21)

Task 4: Produce geospatial maps of zoonoses in livestock and poultry and of human zoonotic
transmission risk.

Future Subtask(s):
Subtask 4.1 Generate maps of laboratory results
Subtask 4.2 Generate visk maps with human behavioral risk factors

Task 5. Strengthen local capacity for detection and reporting of MIERS-CoV, Al, and other
zoonoses in Jordan, Iraq, and Lebanon

Subtask 3.4 Conduct project kick-off mecting in Amman with local stakeholders

Activities and results this quarter:

o The Jordan MERS/AI project launch meeting was held on January 27", 2021. In order to
adhere to COVID-19 safety guidelines, the meeting was held virtually using a password-
protected system. The launch meeting was a success and highlighted the centrality and
investment in collaboration for this project. The launch was also an opportunity to engage
additional key stakeholders to promote synergies with existing biodefense/health security
activities, and provided a forum to share One Health approaches that could potentially be
sustained by the Ministry of Health.

Subtask 3.2 Train local project staff in proper techniques
o All research team members are equipped with

CITI training in human subjects research, the le | de |
team will continue to provide capacity-building

rex if wa - ad
A!

through trainings and workshops on subjects
integral to this project and to future public health
research, including best practices regarding
research and data security, and survey tool

ny

development
» The first annual project training workshop
took place over the course of three days, Figure 3. First day of the virtual first annual

and was attended by all team members, Project training workshop.

including senior scientist and university students. The first and second day of the
workshop was held virtually and the third day of the workshop will be held in person.
The date of the third workshop will be determined soon, depending on local
regulations and will include safety measures in place for COVID-19.

= The third day of the workshop will include training on basic laboratory safety:
biosafety and biosecurity, facilitated by Dr. Mustafa Ababneh, and cold chain
implementation, facilitated by Dr. Borhan Al-Zghoul.

co The sessions held during the two-day virtual workshop included training on:

= Data management use and storage - facilitated by Jamie Cooksey and Dr. Whitney
Bagge

» Ethical human subject research - Emily Hagan and Stephanie Martinez

= Ethical animal subject research- Hani Talafha

= Questionnaire administration - facilitated by Emily Hagan and Stephanie Martinez

Biological Threats Reduction Program Page 4 of 5


Revised 14 September 2020 EXHIBIT B

3) The prohibition in paragraph 1) of this award provision does not contravene requirements
applicable to Standard Form 312, Form 4414, or any other form issued by a Federal
department or agency governing the nondisclosure of classified information.

4) Ifthe Government determines that the recipient is not in compliance with this award
provision, it:

a. Will prohibit the recipient's use of funds under this award, in accordance with
section 743 of Division E of the Consolidated and Further Continuing Resolution
Appropriations Act, 2015, (Pub. L. 113-235) or any successor provision of law;
and

b. May pursue other remedies available for the recipient's material failure to comply
with award terms and conditions.

14. Data Collection.

Data collection activities, if any, performed under this Grant are the responsibility of the
recipient. Awarding agency support of the project does not constitute approval of the survey
design, questionnaire content, or data collection procedures. The recipient shal] not represent
to respondents that such data are being collected for or in association with the awarding
agency without the specific written approval of the cognizant awarding agency official.
However, this requirement is not intended to preclude mention of the awarding agency
support of the project in response to an inquiry or acknowledgment of such support in any
publication of this data.

15. Publications and Acknowledgement of Sponsorship.

Publication of results of the research project in an appropriate professional journal is
encouraged as an important method of recording and reporting scientific information. .

The recipient agrees that in the release of information relating to the grant, such release shall
include the following statement, “The project or effort depicted was or is sponsored by the
Department of the Defense, Defense Threat Reduction Agency. The content of the
information does not necessarily reflect the position or the policy of the federal government,
and no official endorsement should be inferred.” For purposes of this provision, information
includes news releases, articles, manuscripts, brochures, advertisements, still and motion
pictures, speeches, trade association proceedings, symposia, etc.

When issuing statements, press releases, requests for proposals, bid solicitations, and other
documents describing projects or programs funded in whole or in part with federal money, all
recipients receiving federal funds, shall clearly state: (i) the percentage of total costs of the
program or project which will be financed with federal money, and (ii) the dollar amount of
federal funds for the project or program.

16. Authorization to Perform Activities Abroad.

If the award recipient is a foreign institution, the recipient assures that it has been duly
authorized to operate and do business in the country or countries in which the grant is to be
performed; that it has obtained all appropriate licenses, permits, and approvals required in
connection with the grant's proposed activities; and that it will fully comply with all the laws,

Page 13 of 14


¢C. Equipment Description

List items and dollar amount for each item exceeding $5,000
Equipment item Funds Requested {$)

Additional Equipment: | [Add attachment | | l[

Total funds requested for all equipment listed in the attached file |
Total Equipment

D, Travel Funds Requested ($)

1. Domestic Travel Costs ( Incl. Canada, Mexico and U.S. Possessions)
2. Foreign Travel Costs
Total Travel Cost

E. Participant/Trainee Support Costs Funds Requested {$)

1. Tuition/Fees/Health Insurance |
Subsistence |

. Other [ ] [ ]

panifirainee Support Coste [

ap wen

[___] number of Participants/Trainees

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


FREON Ba [ere — cH a
a, wea ns ys sascuan |S sesasan Jesezcvan [ssn nto For va aa TET SE [TTS STR TT TE]



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

He is a founding editorial board member of rOpenSci, an organization that defines standards for
reproducibility and runs peer review for scientific software, and is a core contributor to the
Rocker project, which provides open-source, standardized computing containers for data science.
Dr. Ross holds a Ph.D. in computational ecology from the University of California-Davis.

Organization: EHA has a proven track record of large project management with U.S.
government agencies. We lead the largest global country portfolio, and serve as lead for
Modeling and Analytics and Behavioral Risk Analysis teams, for the USAID Emerging
Pandemic Threats PREDICT program, a $118 million program focused on predicting and
preventing pandemic diseases. The PREDICT project is building a global early warning system
to detect and reduce the impacts of emerging diseases that move between wildlife and people
(zoonotic diseases). EHA also leads multiple field epidemiological contracts funded by DTRA,
NIH, and NSF.

EHA has proven expertise in surveillance-oriented technology development. We managed a five-
year contract to develop applications for DTRA's Chemical Biological Integration Platform
(CBIP, formerly the Biosurveillance Ecosystem BSVE), as well as the development of the
Inbound Bioevent Information System (IBIS), and Rapid Evaluation to Prevent Epidemics in
Livestock (REPEL) project under DHS’s Analytics for Prevention of Biothreat Incidents (APBI)
program and Science and Technology Directorate. Through this work, EHA scientists have
gained substantial expertise in both basic research and working with government project
managers and analysts to develop technologies that are in line with the goals of the funding. The
expertise resulting from this effort serves as the foundation for the current proposal.

Team: Robert Young, Senior Research Software Engineer, will lead project infrastructure
development and the creation ML pipelines. Mr. Young has a background in artificial
intelligence and bioinformatics. He has developed and managed laboratory information systems
in neuroscience, genomics and performed research in neural-network design for image-
recognition in astronomy. Mr. Young currently leads cyperinfrastructure work for EHA’s
veterinary disease forecasting under DHS as well as the DTRA-Funded Western Asia Bat
Research Network.

Emma Mendelsohn, M.Sc., Research Scientist is a data scientist with extensive expertise in
dynamic systems modeling, biostatistics, web application development and data engineering.
Emma is lead analyst for EHA’s work on antimicrobial resistance emergence work as well as
projects linking emerging infectious diseases with nutrition and non-communicable diseases.
Emma will lead data engineering and contribute expertise to algorithm development and
application design.

Peter Daszak, Ph.D., President of EcoHealth Alliance will provide oversight and act as project
advisor on infectious disease spread. Dr. Daszak is a leader in the field of emerging diseases and
disease ecology.


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Recent theory suggests disease dynamics are fundamentally predictable*, and ML methods have
been successfully applied in emerging disease science. ML refers to a group of methods, ranging
from simple nearest-neighbor approaches to function-approximation methods such as boosted
trees and penalized splines, to complex deep-learning neural networks. ML has been used to
predict the likelihood of disease emergence and the zoonotic potential of various hosts and
viruses*-°, and deep-learning approaches have been used to infer viral hosts from the
comparatively richer data of genetic sequences of viruses.’*

ML methods are generally unstructured, flexible, and offer greater predictive power over
traditional statistical approaches. However, these methods are data-intensive, and a key
challenge with ML development is data paucity and/or a low signal-to-noise ratio. Surveillance
data for even existing, well-studied diseases is rarely rich enough for the data scale of many
modem ML techniques. For instance, a ML approach to predict impacts from a new outbreak of
Ebola would draw training data from fewer than twenty previous events, each with varying
levels of data coverage.

Transfer learning, or data fusion, provides a solution to data paucity issues by using data from
other domains to train parts of models that are general, thus limiting new data requirements for
more specific components. Hierarchical modeling, or partial pooling, is a transfer learning
approach for partitioning out which data can be shared between models and which are only
applicable to a specific case. For instance, Ebola fore for countries outside the historic
range of the disease may be improved by calibrating against Cholera outbreaks, which have
occurred both within and outside countries with Ebola outbreaks. The degree to which the
disease outcomes are correlated is learned, rather than assumed.

An alternative recent development in disease forecasting is the development of hybrid models
that incorporate simple mechanistic structures and predictive ML methods to learn model
parameters. These approaches have demonstrated high predictive accuracy for near-term
forecasting of epidemic growth and impact, particularly during the COVID-19 pandemic’.
However, these approaches require adequate time-series of data to project forward from current
conditions. Their utility in early stages and long-term forecasting accuracy has yet to be
determined. In opting to apply these models, it is critical to understand these data requirements
and at what point in an outbreak hybrid approaches can overtake pure ML early-stage predictions
in accuracy. This project seeks to develop, apply, and compare pure and hybrid-ML techniques
to enable the development of robust, rapidly usable predictions at early stages of outbreaks.

Key references (additional references can be found in the Technical Proposal):

L MacDiarmid, S. C. et al. Handbook On Import Risk Analysis for animals and animal
products: quantitative risk assessment. Vol. 2 (Office International des Epizooties, 2004).

2 Moura, J. A., McManus, C. M., Bernal, F. E. M. & de Melo, C. B. An analysis of the
1978 African swine fever outbreak in Brazil and its eradication. Rev. Sci. Tech. 29, 549-
$63, doi:10.20506/rst.29.3, 1992 (2010).

3 Delgado, J. e¢ af, U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing
Controls. Risk Anal. 37, 1768-1782, doi:10.111 L/risa.12704 (2017).


Caution-mailtq)©) I> > > > >>
wrote:

Greetings Dr. Epstein and Dr. Chmura,

CONGRATULATIONS! Your proposal, submitted under DTRA Broad Agency Announcement
HDTRAI-14-24-FRCWMD, Proposal Number: FRBAA 14-6-2-0454, has been selected as one that the Defense
Threat Reduction Agency (DTRA) expects to award. PLEASE DO NOT BEGIN WORK as any award is subject to
(1) successful negotiations between our contracting division and your organization and (2) the availability of funds.
This notification must not be construed as an obligation on the part of the Government; only duly authorized
procurement personnel may commit resources. Also, this notification must not be used as a basis for accruing costs
to the Government prior to award.

Myself and®)6) ill be your POCs until the award has been made. We will work
directly with your office to ications required to support your proposal. Below are some initial
clarifications for EHA’s review and response.

1 Does EHA have its proposed budget available in MS Excel format and if so, would EHA be
willing to share an executable file with DTRA for ease of review?

2. Does EHA have quotations available to support any of the proposed costs (ex. Proposed
equipment or materials, sub-award)? If so, please provide that information to DTRA.
3. The proposal contains the procurement of a Luminex 200 Machine in Year | for use

throughout the project. Please provide additional details as to how EHA proposes to mitigate the expected foreign
tax burden for the local purchase, or importation of this piece of equipment.

4. The proposal contains the procurement of three laptops for this project, however it was noted
in the budget justification that only one will be used to perform Luminex reads. Please provide additional
justification to support the procurement of the additional two laptops.

5. The proposal contains a sub-award to HJF, which includes international travel. Please provide
additional details which addresses the specific nature and frequency of international travel and how it relates to the
requirements for project execution.

6. The proposal narrative states that Dr. Linfa Wang of Duke-NUS will serve as a Technical
Advisor for this project, yet his role is not adequately characterized. There is no sub-award associated with Duke-
NUS, nor are Dr. Wang’s responsibilities, projected time, or salary for his role captured in the technical proposal,
Statement of Work, or budget proposal. DTRA requests clarification on the role of Duke-NUS in the project, and
subsequent submission of any relevant sub-award documentation, if appropriate.

Please let us know if you have any questions. Thank you and we look forward to working with you.

Very Respectfully,
eye)

Contract Specialist

Defense Threat Reduction Agency
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ITEM NO

0002
OPTION

ITEM NO

0003
OPTION

SUPPLIES/SERVICES

Year 4: FRBAB14-6-2-0471
FFP

Reducing the Threat of Middle East Respiratory
Influenza in Jordan & Strengthening Regional Di:
accordance with the following attachements: SOW at attachment | dated 3/18/2020

QUANTITY
1

UNIT
Lot

UNIT PRICE
$999,970.32

Syndrome Coronavirus and Avian
¢ Surveillance Capacity. In

and DTRA Terms and Conditions for Grant Awards dated 09/14/2020 at

attachment 2
FOB: Destination
u009

SUPPLIES/SERVICES

Year 5: FRBAAL4-6-2-0471
FEP

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian
Influenza in Jordan & Strengthening Regional Disease Surveillance Capacity. In
accordance with the following attachements: SOW at attachment | dated 3/18/2020

QUANTITY
l

UNIT
Lot

NET AMT

UNIT PRICE
$942,857.34.

and DTRA Terms and Conditions for Grant Awards dated 09/14/2020 at

attachment 2
FOB: Destination
U009

NET AMT

HDTRAI2010029
Page 3 of 10

AMOUNT
$999,970.32

$999,970.32

AMOUNT
$942,857.34

$942,857.34


Enter transitions of the research or technology from this DTRA project.

Instructions

Award #

Example

Enter a text description of the transitioned Enter the year Enter the organization, company, or
research or technology. Enter “nothing to that the transition government agency to the which this
report" if there were no transitions. started. work has (is) transitioning.

Year

The accelerator technology for detection of

WMDs transitioned to funding from other

US government agency for further

development 2020 USGOV Contract # 12-34-5678

HDTRA1-20-1-0029

Nothing to report

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029

HDTRA1-20-1-0029



Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.)

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: Peter Daszak

Support: J Current KX Pending LJ Submission Planned in Near Future —_] *Transfer of Support
Project/Proposal Title:

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus (MERS) and Avian Influenza (Al) in Jordan &
Strengthening Regional Disease Surveillance Capacity (current proposal)

Source of Support) DTRA

Total Award Amount: $4,975,681.29 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Jordan

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: LJ Current X Pending LJ Submission Planned in Near Future — L] “Transfer of Support

Project/Proposal Title:
CNHz2: Land-use change and microbial spillover as a coupled human-Resilience

Source of Support: NSF

Total Award Amount: $1,599,738 Total Award Period Covered:

Location of Project: EcoHealth Alliance and Malaysia

Person-Months Per Year Committed to the Cal: 0.50 Acad: Sumr:

Support: (J Current Kl Pending (J Submission Planned in Near Future —(_] *Transfer of Support

Project/Proposal Title:
Understanding Risk of Zoonotic Virus Emergence in EID Hotspots of South East Asia

Source of Support: NIH

Total Award Amount: $7,573,721.35 Total Award Period Covered: 3/1/2020 — 2/28/2025

Location of Project: EcoHealth Alliance and International Field and Lab Locations

Person-Months Per Year Committed to the Cal: 3.0 Acad: Sumr:

Support: W& Current UL Pending LJ Submission Planned in Near Future — LJ “Transfer of Support

Project/Proposal Title:
One Health Workforce — Next Generation

Source of Support: USAID

Total Award Amount: $5,000,000 Total Award Period Covered: 10/1/2019 - 9/30/2020

Location of Project: EcoHealth Alliance and International Field and Lab Locations

Person-Months Per Year Committed to the Cal: 1.44 Acad: Sumr:

Support: LJ Current LJ Pending ] Submission Planned in Near Future — LJ “Transfer of Support

Project/Proposal Title:
Strategies to Prevent (STOP) Spillover

Source of Support: USAID

Total Award Amount: $100,000,000 Total Award Period Covered: 8/31/2020 — 8/30/2025
Location of Project: EcoHealth Alliance and International Field and Lab Locations
Person-Months Per Year Committed to the Cal: 3.0 Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY


Quarterly Report: HDTRA 12010029 - Year 1, Quarter 1 (9/29/20 - 12/28/20)

YEAR 1, QUARTER 1 PROGRESS UPDATES FOR EACH TASK

Task L: Determine the presence of MERS-CoV, AL, and other zoonoses among livestock and
poultry in Jordan,

Subtask 1.1 Tdentily sites for projeet study

Activities and results this quarter:

© Project staff reviewed site selection criteria within the 5
project sites; final site selection to occur prior to ofr Ramatn
commencing sampling in Q3. .

an

Subtash 1.2 Obtain local permissions and approvaly to work

with animes FlKonah

Activities and results this quarter:

o JUST IACUC for research with animal subjects was
submitted and full approval was obtained.

o IACUC for research with animal subjects was submitted
for pre-review, comments were addressed from pre-
review, and the subsequent amended application was
submitted for full review to Tufts University.

© Drafted animal owner consent form. Figure I. Map of the 5 sites for project
study in Jordan.

fabs

Future Tasks

Subtash 13 Conduct biological sanipling of animals in the
vtudy areas

Subtask bf Conduct dab testing for Al, MERS-Col,
bruccHosis and leptospirosis

Task 2: Determine whether (and if so, the extent to which) humans at human-livestock/poultry
interfaces display evidence of infection.

Subtask 2.1 fdeatify sites and conduct enrollment

for faiman sendy COMPOHCHE

Activities and results this quarter:

o Project staff reviewed site selection criteria
within the 5 project sites; final site selection
to occur prior to commencing sampling in

Q3.

Subtask 2.2 Obtain local permissions and
approvals to work with human subjects
Activities and results this quarter:

o Compiled materials for local JUST and

HML IRBs, finalized human questionnaire. Figure 2. Virtual meeting to discuss the project
questionnaire

Biological Threats Reduction Program Page 2 of 6


Quarterly Report: HDTRA 12010029 - Year 1, Quarter 1 (9/29/20 - 12/28/20)

Utilizing an international consortium of partners, we are conducting a multi-disciplinary
One Health research project to identify causal factors in the transmission of MERS-CoV, AI. and
other zoonoses in Jordan, while simultaneously
strengthening local capacity for prevention,
detection, and reporting of these zoonoses in
Jordan, Iraq, and Lebanon. Our proposed project
will ngorously test the following initial
” hypotheses:
1) That livestock species and poultry in Jordan

show evidence of infection with MERS-CoV, AL.
respectively and-or other zoonoses.

- 2) That people regularly expased to livestock
andor poultry or their living areas have a greater
risk of infection with MERS. CoV, AI, and-or other zoonoses compared to other
community members in Jordan.

3) That some specific practices may promote exposure to specific animal biohazards
(e.g., blood, urine, feces, and/or nasal secretions) and pose a greater risk of infection with
MERS-CoV, AI, and other zoonoses compared to athers.

This study involves interviews and longitudinal sampling of humans, livestock, and
poultry at animal markets and farms for MERS-CoV. Brucelia spp., and Leprospira spp.
(people livestock) and for AI subtypes (people-poultry). Partner laboratories in Jordan will
screen and characterize these zoonotic pathogens, and capacity-building cross-trainings will be
conducted with visiting laboratory scientists from Iraq
and Lebanon. This multifaceted One Health approach
will advance scientific knowledge on the risk of
MERS-CoV, Al, and other zoonoses in Jordan,
strengthen local zoonotic disease surveillance
capabilities, and enhance scientific partnerships in
Jordan, Iraq and Lebanon to prevent, detect, and report
biological threats.

The project is supported by the U8. DoD
Defense Threat Reduction Agency's Biological
Threats Reduction Program.

Biological Threats Reduction Program Page 6 of 6


Cooperative Biological Engagement Program (CBEP) = Thrust Area 6 — Cooperative Counter WMD
Research with Global Partners

Biosurveillance for Spillover of Henipaviruses and Filoviruses in Rural India

Abstract: The henipaviruses and filoviruses include Hendra virus (HeV) and Nipah virus (NiV)}, and
several species of Ebola (EBOV) and Marburg virus (MARV). respectively, which are highly pathogenic
viruses and select agents capable of causing public health emergencies of international concern. In 2018
the WHO listed Nipah virus as one of the top ten threats to global health and security. In Africa, Ebola
viruses have caused significant morbidity and mortality in people and wildlife. In 2014, Ebola Zaire
caused an epidemic in West Alrica that infected more than 28,000 people. killing approximately 40%.
The outbreak began with a single spillover event, presumably from contact with an infected bat. Preropus
medius is a reservoir for Nipah virus and occurs throughout India and Bangladesh. In Bangladesh and
India, including a recent outbreak in May 2018 in Kerala, India, Nipah virus has caused recurrent,
seasonal outbreaks of acute encephalitis in people and has an associated mean mortality rate of 70%,
Hemorrhagic fever has also recently been reported in India. but surveillance is limiled and to date, no
Ebola-related cases have been identified. Bats are reservoirs for both henipa- and filoviruses, and zoonotic
transmission of these viruses from bats to humans either directly or via domestic animals has previously
occurred in Southeast Asia. Despite their importance as threats to public health and security. the full range
of henipa- and filoviruses in bats and their potential to infect livestock and people remains poorly
understood. The recent outbreak in Kerala illustrates that Nipah virus circulates in bats throughout the
country and spillover is possible wherever bats and people interact. In India, reducing spillover risk is
particularly important due to the high population density in the region and high global connectivity via
international airports, creating opportunity for local outbreaks to become pandemics. In line with Thrust
Area 6 — Cooperative Weapons of Mass Destruction Research with Global Partners. this project will
reduce threat from these select agents through enhancing early detection and surveillance capacity in
India by: 1} transferring Luminex technology with validated reagents to detect IgG antibodies against all
henipaviruses and filoviruses to key partner labs in animal and human health sectors; 2) training
laboratory personne] to develop and utilize Luminex assays to identify exposure to henipa-and filoviruses;
3) conduct biological surveillance in bats, nonhuman primates, livestock and people in rural communities
in Kerala where there are high levels of contact among people and animals, and with optional expansion
into high risk zones in Uttar Pradesh and West Bengal. This project will test the hypothesis that both
henipaviruses and filoviruses are circulating in [ruil bats in India and have spilled over into animal and
human populations. which presents a threat to human health. Activities will be coordinated with the
Global Health Security Agenda, and surveillance data will be shared with the government partners at
local, provincial and national levels. The proposed project is closely aligned with the aims of the
Cooperative Biological Engagement Program in that i will support threat reduction through
biosurveillance and capability building, engagement with partner-country scientists, will engage with the
CBEP Bat One Health threat reduction network (BOHRN) and promotes a One-Health approach.

Point of Contact & Principal Investigator: Dr. Jonathan Epstein. Epstcin@ecohealthalliance.org

Ce: Ms. Emma Lane. lane@eeohealthalliance.org


Biological Threats Reduction Program
PROJECT QUARTERLY REPORT

PROJECT INFORMATION

HDTRA1 2010029 - Reducing the Threat of Middle

East Respiratory Syndrome Coronavirus and Avian

Influenza in Jordan & Strengthening Regional Disease

Surveillance Capacity

Quarter and Dates: Y1,Q3 l {03/29/21- 6/29/21)
Prepared by: | Dr. William B. Karesh

Project and Title:

OVERALL PROJECT SUMMARY AND OBJECTIVES

Project Objective

Utilizing an international consortium of partners, this effort is a multi-disciplinary One Health
research project to identify causal factors in the transmission of Middle East Respiratory Syndrome
Coronavirus (MERS-CoV), Avian Influenza (AI), and other zoonoses in Jordan, while simultaneously
strengthening local capacity for prevention, detection, and reporting of these zoonoses in Jordan, Iraq, and
Lebanon. Our proposed project will rigorously test the following initial hypotheses: 1) That livestock
species and poultry in Jordan show evidence of infection with MERS-CoV or AI, and/or other zoonoses.
2) That people regularly exposed to livestock and/or poultry or their living areas have a greater risk of
infection with MERS-CoV, AI, and/or other zoonoses compared to other community members in Jordan.
3) That some specific practices may promote exposure to specific animal biohazards (e.g., blood, urine,
feces, and/or nasal secretions) and pose a greater risk of infection with MERS-CoV, AI, and other
zoonoses compared to others. This study involves interviews and longitudinal sampling of humans,
livestock, and poultry at animal markets and farms for MERS-CoV, Brucella spp., Leptospira spp.
(people/livestock), and for AI subtypes (people/poultry). Partner laboratories in Jordan will screen and
characterize these zoonotic pathogens, and capacity-building cross-trainings will be conducted with
visiting laboratory scientists from Iraq and Lebanon. This multifaceted One Health approach will advance
scientific knowledge on the risk of MERS-CoV, AI, and other zoonoses in Jordan, strengthen local
zoonotic disease surveillance capabilities, and enhance scientific partnerships in Jordan, Iraq, and Lebanon
to prevent, detect, and report biological threats.

Biological Threats Reduction Program Page 1 of 7


RESEARCH & RELATED Other Project Information OMB Number: 4040-0001
Expiration Date: 12/31/2022

1. Are Human Subjects Involved? (Yes No
1a. If YES to Human Subjects
Is the Project Exempt trom Federal regulations? [] Yes [No
If yes, check appropriate exemption number. (11 (211304015 De C17L1e
If no, is the IRB review Pending? [Yes  []No
IRB Approval Date: |
Human Subject Assurance Number: |

2. Are Vertebrate Animals Used? [Jes XJNo
2.a. If YES to Vertebrate Animals
Is the IACUC review Pending? [_]¥es  [_]No

IACUC Approval Date:| |
Animal Welfare Assurance Number:[|

3. Is proprietary/privileged information included in the application? —_ [XX] Yes [No

4.a. Does this Project Have an Actual or Potential Impact - positive or negative - on the environment? (Yes [IX] No

4b. If yes, please explain: [

4.. If this project has an actual or potential impact on the environment, has an exemption been authorized or an environmental assessment (EA) or
environmental impact statement (EIS) been performed? Clves [[]No

4.d. IFyes, please explain: |

5. ls the research performance site designated, or eligible to be designated, as a historic place? Oo Yes 4 No

5.a. If yes, please explain: [

6. Does this project involve activities outside of the United States or partnerships with international collaborators? Oo Yes Xx No

6.a. If yes, identify countries: [

6.b. Optional Explanation: |

7. Project SummaryiAbstract [_245-3:na-mL_project—Sunmary_2020-054 | ] [Delete Attachment | [ View Attachment |
8, Project Narrative = [1246-DTRA-MT.-Tashnical Propoaat-20204 [ ] [ Delete Attachment | [ View Attachment ]

9. Bibliography & References Cited | ] _Add attachment _| | JI

10. Facilities & Other Resources | | [Add attachment | [ l| ]
11. Equipment | | [Add attachment | [ || |

12, Other Attachments [Add Attachments | | } | } oO

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

2.2.5 Update data from live sources and clean and maintain data continuously

Task 2.3 — BART Model Development and Testing: In the option year we will extend the
BART model to ingest genetic data and test the contribution of this data in enhancing or
recapitulating pathogen trait data.

Subtasks

2.3.1 Develop genetic predictor variable set via neural-network autoencoding

2.3.2 Modify BART and GAM structures to incorporate genetic variables

2.3.3 Implement within-disease, all-disease, and partial-pooled disease model versions

2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 2.4 — Hybrid ML-Mechanistic Model Development and Testing: In the option year will
develop the hybrid machine-learning mechanistic model, compare its performance to a pure ML
model and determine the switch-over time when the mechanistic model becomes more predictive
in the course of the outbreak.

Subtasks

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.4 Implement within-disease, all-disease, and partial-pooled disease model versions

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze prediction-level variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 Iteratively update models to improve performance



Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD
Alagaili AN, Briese T, Mishra N, Kapoor V, Sameroff SC, Burbelo PD, de Wit E, Munster
VJ, Hensley LE, Zalmout IS, Kapoor A, Epstein JH, Karesh WB, Daszak P, Mohammed
OB, Lipkin WI. Middle East respiratory syndrome coronavirus infection in dromedary
camels in Saudi Arabia. MBio. 2014 Feb 25;5(2):c00884-14.

World Health Organization. Joint External Evaluation of IHR Core Capacities of the
Hashemite Kingdom of Jordan. Geneva, 2016.

Tasks/Scientific Goals: (Format: Year #(s). Task #. Subtask#).

TASK 1; Y1.1-02.1: Determine the presence of MERS-CoV, AT, and other zoonoses among
livestock and/or poultry in Jordan.

The awardee shall study presence of MERS-CoV, AT, Brucellosis and Leptospirosis in animals.
Sites will be selected in five geographically-representative regions across Jordan: Northern
Jordan (Al Ramtha), Middle Jordan (Al Zarqa), and Southern Jordan (Al Karak, Ma’an, and
Aqaba). These regions were selected based on preliminary findings by our team and the presence
of livestock production activities. Sites in each region shall reflect interfaces with poultry,
camels, and other livestock animals are represented (e.g. farms or markets). The awardee shall
receive permissions and approvals to work with animals prior to initiating sampling.
Nasopharyngeal and oropharyngeal swabs will be collected from 300 camels, poultry and other
livestock quarterly in Year 1, Q3 through Option Year 1, Q3 (a total of 13 sampling visits). At
three points during the project (in Y1, ¥Y3, and OY1 during the Is, 7th, and [3m sampling visits,
respectively), blood samples (serum) will be collected from 300 animals. Nasopharyngeal and
oropharyngeal swabs will be tested for MERS-CoV and AT using real time PCR. Serum samples
will be tested using serology for MERS-CoV, AI, Brucellosis, and Leptospirosis. Staff training
on proper study techniques (e.g. sampling, transport, and laboratory) will occur as detailed in
Task 5. This task will contribute to testing the hypothesis that livestock species and poultry in
Jordan show evidence of infection with MERS-CoV, Al, and/or other assayed zoonoses, as well
as inform type of transmission pathways.

Y1.2.1 Identify sites for project study.

¥ 1.2.2 Obtain local perm ms and approvals to work with animals.

¥ 1.2.3-O1.2.3 Conduct biological sampling in animals within the study area.

Y¥1,2,.4-02.2.4 Conduct PCR and/or serology testing for Al, MERS-CoV, brucellosis and

leptospirosis.

TASK 2: Y1.2- OY2.1: Determine whether (and if so, the extent to which) humans at
human-livestock/poultry interfaces display evidence of infection with these zoonoses.

The awardee shall conduct a prospective cohort study of humans to evaluate behavioral and
occupational risk factors for zoonotic infectious diseases (MERS-CovV, avian influenza,
brucellosis, and leptospirosis) among persons living in Jordan in any of the five study regions.
The study will enroll persons regularly working with livestock or poultry or sharing their living
areas with these animals as well as persons unexposed to these factors. Sites will be selected as
in Task | and in surrounding communities to enroll both exposed and unexposed populations. In
Year 1, the awardee will receive local permissions and approvals to work with human subjects
prior to initiating enrollment and sampling. Nasopharyngeal and oropharyngeal swabs will be
collected from 300 enrolled humans quarterly in Year 1, Q3 through Option Year 1, Q3 (a total
of 13 sampling visits). At three points during the project (in Year |, Year 3, and Option Year |



BUDGET JUSTIFICATION FOR ECOHEALTH ALLIANCE

EcoHealth Alliance requests a total of $4,975,681 over all-years of the proposed project to
support personnel, travel, equipment, consortium agreements, and applicable indirect costs.

A. Key Personnel

Noam Ross, Ph D., Principal Investigator (3.0 calendar months for all years). Dr. Ross will
oversee the project and be responsible for project management, research, development, design,
analysis, and manuscript writing and conference presentations, as well as communication with
DTRA. We request $25,000 in Y1 due to the high cost of living in New York City and a 5% cost
of living increase in OY L.

Emma Mendelsohn, M.Sc, Research Scientist. (6.0 calendar months for all years). Ms.
Mendelsohn will lead data engineering and contribute expertise to algorithm development. She
will contribute to manuscript writing and conference presentations. We request $42,500 in Y1
with a 5% cost of living increase in OY 1

Robert Young M.Sc, Senior Research Software Engineer. (6.0 calendar months for all years).
Mr. Young will lead infrastructure engineering and contribute expertise to algorithm
development. He will contribute to manuscript writing and conference presentations. We request
$52,500 in Y1 with a 5% cost of living increase in OY 1.

Fringe Benefits

Fringe benefits are calculated as 36.80% of base salary p.a. with $44,160 requested in Y1
calculated from the base salary for all personnel. In OY 1, we budget for a 5% per year cost of
living allowance increase in all salaries.

B. Other Personnel
No funds are requested for other personnel.

C. Equipment
No funds are requested for equipment.

D. Travel
Per recommendations in the BAA, we request travel funds to Nationally Recognized
Conferences and the Annual Technical Review.

Domestic Travel

Domestic travel is requested for one trip per year from New York City per year to Northern
Virginia the Annual Technical Review. Transportation is estimated at $350 per trip along with
the government GSA per diem rates for Northern Virginia ($161 for accommodation and $71 for
meals and incidentals). Additional domestic travel support will facilitate two EcoHealth Alliance
team members presenting at one conference. In OY1, this will be the EEID conference held in
Bozeman, MT in 2022, with transportation costs of $500, $225 for accommodation and $55 for
meals and incidentals. For each trip, $25 per day is estimated in taxi costs and for those that


a)

Contracting Officer

Defense Threat Reduction Agency. AL-ACR
Phonekb)6)

Email}

«<HDTRAI-21-1-0023.pdf>

Disclaimer

The information contained in this communication from the sender is confidential. It is intended solely for use by the
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F, Other Direct Costs Funds Requested ($)
Materials and Supplies 3, 000.00

1.

2. Publication Costs
3. Consultant Services |
4. ADP/Computer Services |
5. Subawards/Consortium/Contractual Costs Eo
6. Equipment or Facility Rental/User Fees |
7.
8.
9.
10.

Alterations and Renovations |
| J |
| J |
| | |

Total Other Direct Costs

Funds Requested ($)
Total Direct Costs (A thru F) 182,26

G. Direct Costs

H. Indirect Costs

Indirect Cost Type Indirect Cost Rate (%) Indirect Cost Base (S) Funds Requested ($)

call: Alliance's NICA rate | bs

oo) |

Total Indirect Costs

Cognizant Federal Agency
(Agency Name, POC Name, and

|. Total Direct and Indirect Costs Funds Requested ($)
Total Direct and Indirect Institutional Costs (G + H) 248,402.46

J. Fee Funds Requested (5)

K. Total Costs and Fee Funds Requested ($)
Total Costs and Fee (I + J) 248, 107.46

L. Budget Justification

(Onlyatach one file) yee pTkA M_Budgex sos.itisusion. pat] | | II

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

17 Agarwal, V., Reddy, N. J. K. & Anand, A. Unsupervised Representation Learning of
DNA Sequences. arXiv e-prints, arXiv:1906.03087 (2019).
<https://ui.adsabs. harvard.edu/abs/201 9arXiv 1 90603087A>.



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

In the option year, the awardee shall develop the hybrid machine-learning mechanistic model,
compare its performance to a pure ML model, and determine the switch-over time when the
mechanistic model becomes more predictive in the course of the outbreak.

Subtasks

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.4 Implement within-disease, all-disease, and partial-pooled disease model versions

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze prediction-level variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 Iteratively update models to improve performance

Deliverables

Reports and Documents
¢ Monthly reports on task status
e Technical papers describing algorithm design and performance
e Conference presentations

Code Modules and Database Archives

All code modules and documentation will be continually available on EcoHealth Alliance’s
GitHub page with access provided to DTRA. Databases will be made available for download and
provided on-disk to DTRA per request.


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

ecosystem types, transport infrastructure that can be updated and refined with on-the-ground data
if available.

Disease outbreak impact data: We will draw a variety of sources to build a database of several
thousand of outbreak time-series to train our models. First, we will use time series from AIDO,
the most comprehensive such repository, with approximately 700 series across 40 diseases.
Recognizing that AIDO has limits and data gaps, we will supplement this with several other
sources including One Health disease datasets compiled and maintained by EHA:

e COVID-19 Data: Data on cases, hospitalization, and deaths from COVID-19 are uniquely
available at national, sub-national and city scales for a broad swath of countries with varying
conditions and policy responses. While the COVID-19 pandemic is linked across countries,
in our hierarchical modeling framework we will to separate these out into multiple regional
outbreaks, shedding more light on the effects of environmental and socioeconomic factors

under a common host and pathogen.

© Multispecies datasets: We will supplement human disease outbreaks with data on veterinary
disease outbreaks. EHA maintains a database on veterinary disease outbreaks derived from
OIE and FAO data and supplemented by digitized archival reports (extending back to 1992),
with 2900 outbreak records across 122 diseases. This will more than quadruple the available
disease time series for training. Partial-pooling will ensure that only information in relevant
veterinary data sets will be applied to human outbreaks and vice versa. For instance, effects
of population density or health/veterinary capacity are expected to have partial correlation
across all diseases. In addition, this will allows the model to predict impacts from these
diseases, which has direct defense applications given the food supply, trade, and political
stability impacts of veterinary diseases

© Emerging Infections Disease Repository: EHA’s Emerging Infectious Disease Repository
(eidr.ecohealthalliance.org/)'* contains data on an additional 350 zoonotic disease emergence
outbreaks, giving additional insights in particular to new emergence events from zoonotic
sources. Full time-series are incomplete but outbreak size and duration are recorded, allowing
them to be integrated into our framework with multiple imputation.

Predictor variables: Disease dynamics depend on a complex interplay of pathogen, host, and
environmental conditions. We will use data on pathogens (traits, genomes), sources (reservoir
host traits, range, relatedness to humans), environmental context (climate, ecosystems traits), and
socioeconomic context (local population densities, transportation networks, healthcare systems)
as predictors for our models, as well as time-series data of infections from the earliest phase of
the outbreaks. As we expect all data will be available for neither our full training set nor in
common condition of new outbreaks, use multiple imputation!? in all models to allow for
prediction as well as uncertainty assessment under incomplete data.


Future Tasks
Subtask 2.3 Conduct biological sampling

o Equipment purchased
o Field Vehicle: 2021 MGRX8&
Sublask 2.4 Conduct testing for Al, MERS-CovV. brucellosis and leptospirosis

o Equipment purchased
o Synergy HTX Multi-Mode microplate reader and PC

Subtask 3.17 Obtain local permissions and approvals to work with human subjects

Activities and results this year:

o The Jordanian team and
EcoHealth Alliance (EHA)
behavioral risk scientists
collaboratively — built and
finalized the Animal History
Form, Field Data Sheet,
Human Questionnaire, and
Site Characterization Form.

o Local JUST IRB approval
obtained.

o The Human questionnaire,
Site Characterization Form,
and Animal History Form
have been drafted and coded Figure 4. Virtual meeting regarding project questionnaire
for Open Data Kit (ODK). development and analysis plan
Tablets were purchased for
the Jordan team.

o The JUST IRB approved an amendment for the human subjects research.

HML IRB approved the protocol for U.S.-based IRB approval.

o The human subjects research and human sampling protocol were submitted to
HRPO.

ce)

Subtask 3.3 Analyze epi causal inference to identify modifiable risk factors

Activities and results this year:

o Worked with the project staff and SMEs to compile a list of epidemiological
risk factors, behaviors, and general exposure risk factors to be used in data
analysis.

o During the human subjects research workshop, the team worked to
collaboratively compose the survey guide to provide clarity of question purpose
and application for field staff to support data fidelity across sampling events. In
scenarios where subjects might need support in understanding questions,
ensuring that field staff understand the risk factors being analyzed in each


Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

o The project staff have selected the villages below for sampling:
1. WC34+58 Dabet Hanut, Quaira, Aqaba
2. HHF4+55 Al-Mansoura, Shoabak, Ma‘an
3. 8G3R83WF+5G Al Damkhi, Karak
4. RRM8+8F Azraq, Zarga
5. F4PG+QH, Hosha Sub-District, Ramtha, Irbid
o The team developed a draft Human and Animal sampling protocol in preparation for

enrollment of human and animal participants.

Subtask 2.2 Obtain local permissions and approvals to work with human subjects

Activities and results this quarter:

oO The project team submitted the study protocol to Health Media Lab IRB Research and Ethics
(HML) for U.S. IRB approval. Approval was obtained (4/23/21).

o The project team submitted an amendment to the local ethics review board at JUST and
approval was given (4/26/21).

o The human subjects research and human sampling protocol was submitted to the Human
Research Protection Office (HRPO) (5/4/21).

Subtask 2.3 Conduct biological sampling
o Equipment purchased
o Field Vehicle: 2021 MGRX8, delivery pending completion of tax exemption approval

Subtask 2.4 Conduct testing for AL MERS-CoF. brucellosis and leptospirosis
o Equipment purchased
o Synergy HTX Multi-Mode microplate reader and PC

Task 3: Characterize causal factors in animal-to-human transmission of these zoonoses.

Subtask 3.4 Obtutin local permissions and approvals to work with human subjects
Activities and results this quarter:

o The JUST IRB approved an amendment for the human subjects research.

o HML IRB approved the protocol for U.S.-based IRB approval.

o The human subjects research and human sampling protocol were submitted to HRPO.

Future Subtask(s)
Subtask 3.2 Conduct behavioral vixk factor survers in people
Subtask 3.3 Analyze epi causal inference to identify modifiable risk factors

Task 4: Produce gcospatial maps of zoonoses in livestock and poultry and of human zoonotic
transmission
Subtask 4.1 Generate maps of laboratory results
Activities and results this quarter:

o The project staff created an R Markdown document in RStudio for future stakeholder
meetings that includes maps to demonstrate the percentage of positive tests over the total
amount of humans and animals tested for the four zoonotic pathogens of interest in Jordan
for this project.

sk.

Biological Threats Reduction Program Page 3 of 7


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: William Karesh

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:
Crimean-Congo hemorrhagic fever: Reducing an emerging health threat in Tanzania

Source of Support: DTRA

Total Award Amount: $4,848,483 Total Award Period Covered: 10/01/2019 - 07/31/2024

Location of Project: USA, Tanzania

Person-Months Per Year Committed to the Cal: 1.0 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Reducing the threat of Rift Valley Fever through Ecology, Epidemiology and Sacio-Economics

Source of Support: DTRA

Total Award Amount: $4,988,526 Total Award Period Covered: 06/01/2019 - 05/31/2024

Location of Project: USA, South Africa

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: C1] Current XX] Pending C1 Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Understanding filovirus diversity in bat reservoirs and implications for human outbreaks in West Africa

Source of Support: NIH

Total Award Amount: $7,307,869 Total Award Period Covered: 03/01/2020 - 02/28/2025

Location of Project: Global

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: C1] Current XX] Pending C1 Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Reducing the Threat from high-risk pathogens causing febrile illness in Liberia

Source of Support: DTRA

Total Award Amount: $4,912,818.06 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Liberia

Person-Months Per Year Committed to the Cal: 1.5 Acad: Sumr:

Support: LJ Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY

3


recombinant henipavirus attachment glycoprotein (G) for antigen-based serology. These G protein
antigens will be used for the planned retrospective human sera screening of banked samples stored at
KGMU, Reagents are prepared to ship and are planned to be sent as choreographed with the arrival of
the Luminex machine,

4) Key outcomes: These assays will be used to screen bat samples and hospitalized patient
sputum and CSF for viral RNA. Testing bat samples and acutely ill hospital patients by PCR may allow us
to detect viral RNA during active infection, providing important information about the diversity of viral
strains circulating within bat species that may account for human infection and observed immune
responses seen on Luminex.

Task 4.

1) Major activities: Develop/maintain project database

2) Specific objectives: Develop a functional database that allows for the secure storage,
sharing, and analysis of field and lab data and metadata, that will also connect to sample tracking
software so that data is linked to sample IDs in repository. Database will be adaptable to new types of
data that may arise during project.

3) Significant results: We have created a functional project database which is bespoke to the
current project's data needs which are complex and interdisciplinary in nature. The project database uses
an Airtable platform which is relational in structure and links data records across tables. Our project
database in Airtable allows for collaborative data entry efforts across sampling and lab efforts, as well as
secure remote storage, tiered access, and the capacity to hold multitudinous data points. The online
database is able to contain all information related to field work and field sites, bat, livestock and human
samples, and results of molecular assays, We have also created a SOP for the database which includes
clear guidance on use, data quality/data assurance measure, and a data dictionary.

4) Key outcomes: An online database tailored to the specific project needs has been created.
This database allows for the secure, organized and comprehensive storage of data across multiple
sampling and lab efforts. The database allows for report generation, data cleaning measures, and
analysis tools. The database works across data collection platforms and allows multiple data types to be
stored together in a secure, central system.

Task 5.

1) Major Activities: Screen archived human VHF and AES samples from KGMU

2) Specific Objectives:

1.5.1-2.5.1. Archived specimens identified/logged/tested

1.5.2.-3.5.2 Results entered into database.

3) Significant results: The partnership between EHA and KGMU has been formed through a
signed sub awardee agreement, representing the ability for KGMU to use their robust biobank to screen
archived samples. While the project awaits the impending shipment of supplies and the Luminex
equipment, the KGMU laboratory team has been formed and primed to receive training on the Luminex
platform.

4) Major Outcomes: The KGMU subaward has been established, and 6 hospital and laboratory
staff have been hired, in anticipation of the screening of archived samples. King George's Medical
University hospital is a referral hospital that serves patients from all over Uttar Pradesh. Annually, KGMU
sees more than 300 BHF cases and 1000 AES cases, many of which are negative to known agents such
as Dengue or JEV. Professor Jain has led studies of AES and VHF, and by screening archived serum
and CSF from patients who have previously volunteered and consented to use of their samples for this
aim, we will be able to assess the baseline prevalence and geographic distribution of henipaviruses and
filoviruses in this population of acutely ill patients, which will inform our sampling strategy for the village
cross sectional studies as well as deepen our understanding of whether these agents play a significant
role in causing severe VHF or AES in UP. We will screen 600 archived sera from VHF patients and from
900 AES patients using the Bioplex assay. Up to 300 CSF samples will be screened using PCR.



HDTRA12010029
Page 10 of 10
Section J - List of Documents, Exhibits and Other Atachments
Exhibit/Attachment Table of Contents
DOCUMENT TYPE DESCRIPTION PAGES DATE

Attachment 1 Statement of Work 7 18-MAR-2020
Attachment 2 Terms and Conditions 14 14-SEP-2020


From: Aleksei Chmura
To:

Ce: bye) Whitney Bagge: Joe Riccardi; Billy Karesh
Subject: Re: [Non-DoD Source] FRBA14-8-2-0471 Clarifications

Date: Thursday, September 17, 2020 11:38:05 AM

Attachments: DTA Jordan Budget 28 Aua 2020 xlsx

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Dear

Please find our excel file with a breakdown of EcoHealth Alliance costs and with separate tabs for our subcontracts.
Call me anytime, if you have additional questions or need other documents.
Many thanks!

~Aleksei

Aleksei Chmura, PhD
Chiet of Statf &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 > (office)
Po f (mobile)
Cantion-www.ecohealihalliance.org < Caution-hup/Avww ecohealthalliance org/ >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Sep 14, 2020, at 1 . Lyles, Latrice D CTR DTRA AL (USA)

Good morning,

Thank you for documents as they will come in handy. Would you be able to provide an excel breakdown of
EHA and subs?

From: Aleksei Chmura <chmura@ecohealthalliance.org <mailto-chmura@ecohealthalliance.org> >
Sent: Wednesday. September 2, 2020 12:34 PM
Tol)





Revised 14 September 2020 EXHIBIT B

stemming from, the research effort. Do not include copies of already submitted or
published articles in the final report.

Standard Form (SF) 298, Report Documentation Page, must be used. Item 13 of the SF-298
should contain a 100 to 200 word abstract summarizing technical progress during the
reporting period. The SF-298 may be found on the Internet at:
http://www.gsa.gov/portal/forms/download/1 16146

All of the report pages should be prepared for acquisition and distribution by DTIC. All of
the report pages should be of good quality for copying purposes. No pages should be
missing.

The format and standard required by your institution for the preparation of theses and
dissertations shall be used for the final report. In the absence of any institutional standards,
you may wish to refer to the American National Standards Institute (ANSI) document

Z39. 18-1987, “Scientific and Technical Reports: Organization, Preparation, and
Production,” for guidance. The report may be obtained from:

American National Standards Institute, Inc.
1430 Broadway
New York, NY 10018

It is anticipated that all final technical reports will be unclassified and that distribution will
not be limited. However, for final technical reports that require a limited distribution as
deemed necessary by DTRA, a Distribution List will be provided with the comments on the
draft final technical report. The Distribution List should be formatted to match the rest of the
report, placed at the end of the report, and added to the Table of Contents. The number of
pages in the Distribution List should be added to the total page count and included in the total
number of pages cited in Block 15 of the SF-298.

The draft of the final technical report will be due not later than forty-five (45) days prior to
the end of the period of performance. The draft of the final technical report (including a draft
SF-298) must be submitted electronically as follows:

¢ Email the draft of the final technical report to dtrabasicresearch@mail.mil (file size must
be less than LOMB). The file name should be ‘Draft Final Report’ and the Grant number,
e.g. Draft Final Report HDTRA1-12-1-9999.

e Provide a copy of the report to the Administrative Office identified in the Grant.

Within thirty (30) days, this draft will be reviewed by DTRA and comments will be provided
to the Grantee to ensure the report complies with DTRA final report requirements. Such
review and comment does not restrict the conduct or reporting of the project research
findings/outcomes and, in accordance with Article 35, does not restrict Grantee's ability to
publish. Grantee shall incorporate such requested changes so that the report incorporates and
complies with agreement final reporting requirements terms. Final Technical Reports are
due ninety (90) days after the expiration or termination of the award. The final submission
should be made in accordance with the draft final report submission instructions.

Final Metrics. A final metrics table (in MS Excel format) is required. A template and
specific instructions will be provided in advance of the submission deadline. The final
metrics file should be submitted along with the Final Technical Report. The fields contained

Page 6 of 14


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>, Joe Riccardi <riccardi@ecohealthalliance.org < Caution-mailto:riccardi(@ecohealthalliance org >

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Kx)

Greetings Dr. Chmura,

I just wanted to touch base to see if it was possible to receive a status update on the outstanding
clarifications. We were hoping to reach resolution of these clarifications by mid-month in order to meet a
prospective award date of 31 August however, we completely understand that EHA has its own competing priorities
and that EHA may be waiting for responses from its own prospective subcontractors for this effort. Any update you
might be able to share will help us shape expectations within our organization on a revised prospective award date.

Thank you very much and we look forward to continuing to work with you and your team! Have a
wonderful day!

Very Respectfully,

exe)

Contract Specialist
Defense Threat Reduction Agency

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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

statement of work will benefit from the significant experience of the project staff and in-kind
contribution from other EHA personnel with combined expertise in global emerging infectious
disease epidemiology, public health, veterinary medicine, machine learning, and biostatistics.
We also focus on deplovment-ready research infrastructure to minimize the need to re-engineer
models developed in the research process for applied use.

We engage multiple best practices to minimize project disruption risk. Personne/ risk entails loss
of expertise and project or institutional knowledge due to loss of core project staff. EHA
maintains project data, documentation, and code in common cross-team repositories. Version-
controlled project repositories ensure that project histories are tracked, and all team: members can
retrieve previous work by others. Through regular code review, we ensure all team members
have working knowledge of project modules and the ability to re-task. Infrastructure risk entails
loss of project infrastructure such a. EHA performs on-site high-performance computing cluster
or data storage. We keep code, data, results and documentation on-site only for active
experiments, and back up these to off-site cloud infrastructure. Our compute environment is
reproducible, being configured by pre-programmed, self-documenting methods such as
Dockerfiles, Ansible playbooks, and Bash scripts. It can be re-deployed to off-site cloud
resources or new hardware as needed.

Relevance

Our research will fundamentally improve infectious disease forecasting by identifying the
relative importance of early-stage factors in outbreak prediction. While pathogen-host-
environment linkages are fundamental in disease modeling, predictive methods developed to
assess emerging biothreats usually focus on each individually. We will also advance knowledge
of hybrid ML/mechanistic models, which have not yet been used in applied disease forecasting.
Together this research will impact C-WMD science by providing practical guidance for early-
stage data-gathering and prototyping an interactive decision support tool for biothreat impact
forecasting.

Credentials

EcoHealth Alliance (EHA) will implement this project, with Noam Ross, Principal Scientist for
Computational Research, as project lead. EHA’s mission is focused on emerging diseases and
cutting-edge research into the critical connections between human, wildlife, and ecosystem
health that drive disease emergence and spread.

PI: Dr. Ross will direct he project. He is a computational disease ecologist who provides
expertise in dynamic epidemiological processes, machine learning and biostatistics, and high-
performance computing. He has led work in ecological and epidemiological modeling and data
management for EHA’s work on USAID PREDICT and NIH NIAID viral surveillance research.
He has experience developing livestock disease outbreak and spread simulations in collaboration
with FAO as well as private industry. Dr. Ross is PI for EcoHealth’s work with DHS on machine
learning for veterinary disease outbreaks. He also a leader in open-source scientific computing.

8


From: Ava Sullivan <sullivan@ecohealthalliance.org < Caution-mailto:sullivan@ecohealthalliance.org >
<Caution-mailto:sullivan@ecohealthalliance.org < Caution-mailto:sullivan(@ecohealthalliance. org > > < Caution-

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Sent: Tuesday, Auoust 25, 2020 8:21 PM
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Ce: Jon Epstein <epstein@ecohealthalliance.org < Caution-mailto:epstein@ecohealthalliance.org >
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Caution-mailto:riccardi@ecohealthalliance org > > >, Nwani, Nkechiyere G CTR (USA)

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>; Aleksei MacDurian <chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org >
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Andre <andre@ecohealthalliance.org < Caution-mailto:andre@ecohealthalliance.org >
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Subject: Re: |Non-DoD Source] FRBAA 14-6-2-0454

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Hef)

Forgive the late response. We were expecting to submit our full packet of clarifications today. However, due to
unforeseen circumstances including issues with COVID-19 in-country, we will require a few extra days. I hope it is
acceptable that we submit this on Friday, the 28th.

Thank you for your understanding, and we look forward to providing all the requested materials, Ava

On Thu, Aug 20, 2020 at 1:38 PMO)

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HDTRAI2010029

Page 9 of 10

FUNDING PROFILE:

The amount of $2,956,308.78 is obligated for work to be performed during

the period beginning with grant award and continuing through September

28, 2023. Additional incremental funding planned, but not obligated, is:
FY 21 $855.41

The Government's liability is limited to the amount obligated

Invoice Schedule

i 9/29/2020 $79,900.24
2 LO/29/2020 $79,901

3 1/29/2020 $79,901

4 12/29/2020 $79,901

5 1/29/2021 $79,900.24
6 2/28/2021 $79,900.24
7 3/29/2021 $79,901

8 4/29/2021 $79,901

9 5/29/2021 $79,900.24
10 6/29/2021 $79,900.24
al 7129/2021 $79,900.24
12 8/29/2021 $79,901

13 9/29/2021 $79,900.24
14 LO/29/2021 $79,900.24
15 1/29/2021 $79,900.24
16 12/29/2021 $79,900, 2:
17 1/29/2022 $79,901

18 2/28/2022 $79,900.24
19 3/29/2022 $79,900.24
20 4/29/2022 $79,901

21 5/29/2022 $79,901

22 6/29/2022 $79,901

23 7129/2022 $79,900.24
24 8/29/2022 $79,900.24
25 9/29/2022 $79,901

26 LO/29/2022 $79,901

27 1/29/20: $79,900.24
28 12/29/2022 $79,900.24
29 1/29/2023 $79,900.24
30 2/28/2023 $79,901

3 3/29/2023 $79,900.24
32 4/29/2023 $79,900.24
33 5/29/2023 $79,900.24
34 6/29/2023 $79,900, 2:
35 7129/2023 $79,901

36 8/29/2023 $79,900.24
37 9/28/2023 $79,900.14

(End of Clause)


ame]

Taser)

= - f Ta —_T js |



Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

Attendance for Day One
o Ministry of Health

°

aooo0

°

Dr. Alaa Hamdallah
Dr. Saed Alnserat
Dr. Nizar Masouda
Dr. Akef Sleihat
Dr. Hayel El-Burigi
Dr. Fatima Zerriouh

o Ministry of Agriculture

°

ooo 000

°

o Ministry of Environment

°

°

Dr. Esam Hawa, Chief Veterinary Figure 9. Invited speakers table at opening of
Officer the workshop.

Dr. Majed Hawaosheh
Dr. Samer Aldwairi

Dr. Khaldoon Malkawi
Dr. Hassan Al-Husainat
Dr. Marwan Awabdeh
Dr. Sharher Abu Dalbouh
Dr. Haitham Athamneh

Eng. Belal Shagarin, Director of
Climate Change Direcotorate

Eng. Saddam Diab Al-Khawaldeh,
Nature Protection Directorate
Eng. Nour M Alghazo, Climate

Change Directorate Figure 10. A 10-minute presentation on One
Noura Hamad Alshra’a, Climate Health in Jordan by Dr. Alaa Hamdallah from
Change Directorate the Ministry of Health.

o Jordan University of Science and

Technology

°

ce)

oo0o0o0 00 00

Dr. Mohammad Khalifeh, Dean of
Faculty of Veterinary Medicine
Dr. Tareq Mukattash, Dean of
Research

Dr. Ehab Abu-Basha, Project Lead
Dr. Mustafa Ababneh

Mr. Bilal Al-Omari

Dr. Mohammad Borhan Al-Zghoul
Dr. Basil Hameed Amarneh

Dr. Saied Jaradat

Dr. Omar Al-Omari Figure 11. Group exercise during dav one of the
Eng. Hani Talafha Survey Design and Risk Communication
Alaa Fahmawi, Nurse, King Abdulla Workshop.

Hospital

Biological Threats Reduction Program 7


Section E - Inspection and Acceptance

INSPECTION AND ACCEPTANCE TERMS

Supplies/services will be inspected/accepted at:

CLIN INSPECT BY
0001 Government
000101 N‘A

0002 Government

0003 Destination Government

ACCEPT AT
Destination

HDTRAI2010029

Page 5 of 10

ACCEPT BY
Government
N/A
Government
Government


Revised 14 September 2020 EXHIBIT B

8. Patent Rights.

Patent Rights are governed by 37 CFR 401.14 with the following clarifications: All DTRA-
related disclosures, confirmatory licenses to the government, patent applications, and other
communications should be submitted using iEdison (https://public.era.nih.gov/iedison), a
single web interface for government grantees to report details of inventions and patents. .

The 37 CFR Part 401 invention reporting requirements are summarized at iEdison
(https://era. nih.gov/iedison/invention_timeline.htm). If the grantee organization is not
already an iEdison registrant, then iEdison registration is required prior to submission of the
below invention reports. The grant shall not be closed out until all invention reporting
requirements are met.

The recipient shall also submit interim and final Reports of Inventions using the DD882 form
(https://www.arl.army.mil/www/pages/218/d882.pdf). Interim invention reports shall be
submitted annually, listing subject inventions reported during that period or that there are no
such inventions. These reports are due no later than 1 July of each year. Grants effective
after 31 January will not require a report until | July the following year. A final report shall
be submitted within ninety (90) days after end of the project, listing all subject inventions or
stating that there were no such inventions. These Reports of Inventions should be submitted
to:

1) The DTRA Grants Officer via email (the address specified in the grant’s clause
252.601-9000 may be used);

2) The Administrative Officer listed in the Grant via email (Block 6 of the SF-26 award or
Block 7 of the SF-30 modification);

3) As directed by DTRA, email or portal; and

4) E-mailed to dtrabasicresearch@mail.mil (file size must be less than 1OMB). File should
be named by the Grant number and “Invention Report” (e.g. HDTRAI-12-1-9999
Invention Report).

9. Technical Reporting Requirements.

Research Performance Progress Report (RPPR). Except under rare cases, RPPRs are
required annually. The RPPR is due no later than | July of each year. Grants effective after
31 January will not require a RPPR until | July of the following year.

The RPPR is not a cumulative report. The first RPPR shall only include actions that
occurred from the Period of Performance start date up to submission of the first RPPR. Each
subsequent report shall only include actions that occurred during the 12-month period
following the previous year’s RPPR.

A RPPR is not required in the final year of the award if the period of performance ends
within 60 days of the RPPR due date. In this instance the Final Report will satisfy the
requirement. Broadly the RPPR shall address the following items:

e Accomplishments
¢ Products

e Participants and Other Collaborating Organizations

Page 4 of 14


Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

o April 6, 2021: The focal point meeting
took place in Amman, Jordan, in
April. Dr. Ehab Abu Basha gave a
brief description of the project. This
event provided an opportunity to
clearly establish the role of the
Ministry of Agriculture in the context
of the project and to discuss upcoming
lab visits in the south of Jordan.
Follow-up meetings will be conducted
online due to COVID-19 regulations.

o In attendance to the focal point
meeting:

= Dr. Ehab Abn Basha, JUST

» Dr. Zaidoun Hijazeen, JUST

=» Dr. Yasmin Daradkeh, JUST
= Dr. Laura Sawalha, WHO

= Dr. Hussein Zyoud, Head of Animal Health Labs, MoA
= Dr. Essam Hawwa, CVO, MoA

® Colonel Oday Mrayat, Jordan Armed Forces

Figure 5. Focal point meeting in Jordan.

o Jordan Team Meeting
o May 10, 2021: The Jordan in-country team held a meeting to discuss training and
workshops, the human questionnaire, and purchasing activities in preparation for the
beginning of data collection. The Jordan team will purchase lab and field materials and
establish and review standard operating procedure (SOPs) for animal and human
sampling, questionnaire administration, and laboratory procedures.

Future Subtask(s)

Subtask 3.1 Conduct project kick-off meeting in Amman with local stakeholders

Subtask 5.3 Host annual partuers und stukeholders meeting

Subtask 3.4 Complete annual report and share with partners and stakeholders

Subtask 3.5 Conduct local-regional training workshops

o COVID-19 restrictions have limited in-person workshop, but as the restrictions begin to ease,
we are hopeful that we will be able to set a date for the local-regional training workshop, with
Human Link Lab at the National Research Centre, Egypt, in August. The team looks forward
to engaging with our regional stakeholder to further serve the regional biosecurity and
capacity-building objectives of the project.

Tasks to be initiated after Year 1:

Task 6. Identify specific modifiable risk factors for human infection with these zoonoses

Biological Threats Reduction Program Page 6 of 7


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

YEAR 1, QUARTER 4 PROGRESS UPDATES FOR EACH TASK

Vask L: Determine the presence of MERS-CoV. AL. and other zoonoses among livestock and
poultry prdan,

Subtask fof ldentify sites for project studi
Activities and results this quarter:

o Four project staff from Ecohealth Alliance traveled to Amman, Jordan from August 6" -
14" to provide a two-day workshop and an investigation of selected study sites. During the
trip the project staff visited four different potential human-animal interface sites. Two of
the were located in the Tura subdistrict in Al-Ramtha and the other two sites were located
in the Hmeimeh and the Al-Dissi subdistricts in Quaira, Aqaba.

Figure 1. A camel interface site in the Tura Figure 2. A camel interface site in AlDissi,
subdistrict in Al-Ramtha in the north of Jordan. Quaira, Aqaba in south of Jordan.

Subtask 1.2 Obtain local permissions and approvals to work with animals
Activities and results this quarter:
co All required approvals for animal work have been obtained. The team is still awaiting
approval from the Office for Human Research Protections (HRPO) so that human and
animal work can be conducted simultaneously (One Heath approach).

Future Subtask(s)

Subtash 1.3 Conduct biological xanipling of animitly in the study aves
Subtask bf Condes fab testing for AL MERS-Col,

brucellosis and leptospirosis

Vask 2: Det
intertf:

mine whether (and if so. the extent to which) humans al buman-livestock/poultry
ces display evidence of infec

Subtask 2.1 fdentify sites and conduct carollment for hiwnan study component
Activities and results this quarter:

Biological Threats Reduction Program 2


Task 8.

1) Major Activities: Analyze data

2) Specific Objectives: Analyze field epi and lab results.

3) Significant results: None to report.

4) Major Outcomes: While no major data collection efforts have been undertaken at the point in
the project, the project database has been created with this major activity and specific objective in mind.
The project database has been designed in order to facilitate rapid analysis and generate, present and
publish epidemiological and other project data.

Task 9.
1} Major Activities: Disseminate results to relevant stakeholders
2) Specific Objectives:
1.9.1-5.9.1 Submit annual reports to DTRA.

1.9.2-OY5.9.2 Provide results to local stakeholders.

3) Significant results: None to report.

4) Major Outcomes: Annual report generated for DTRA, July 2021. A local stakeholders meeting
is planned to occur July 2021 with leadership from Co-Pl’s Dr. Amita Jain of KGMU, Dr. Prejit Nambiar of
KVASU, Dr. Arun Zacariah of Kerala Wildlife Department, and Dr. Rajesh Bhatia, formerly of WHO
SEARO. This planned stakeholder's meeting represents a major point of relationship building and
stakeholder engagement for the project, and is expected to inform project ability to gain rapid approvals,
disseminate project results, and further implement the project successfully.

What samples has the project collected and what methodologies has the project implemented?
For this reporting period, use the impact questions below to guide your discussion of: 1) sample
collection; 2) sample storage; 3) laboratory techniques; and 4) sustainment of skills. You should provide
an annual sample repository list to include all samples collected from this research effort. Discuss how
you plan to maintain samples collected during the proposed research effort, along with relevant metadata,
for at least 12 months after the project end date. If nearing the end of the project, you should state a plan
and schedule for final disposition of EDP samples collected in support of the research effort.

Samples

- What are the roles and responsibilities of each partner in sample collection for pathogen identification?
- How will the sample collection, field or clinical, methodologies practiced during this research project
have long-term impact on sustained and durable capabilities?

Laboratory or Clinical Techniques

- What are the laboratory techniques built or reinforced during the duration of the research project?

- How will the techniques and practices taught through the duration of the research project support long-
term sustainment and durability of skills in partner country(ies)?

At this point in the project, approvals, permissions, and permits are underway to allow for sample
collection to occur. Due to the hardship imposed by the ongoing COVID-19 pandemic in India, this
already involved process has an elongated timeline, As such, no samples have been collected for the
project thus far, Despite this, progress has been made in terms of the creation of methodologies for future
use.

All biological samples will be collected in duplicate and stored in our partner lab at KGMU in
secure ultra-cold freezers. Samples will be tracked using specialized lab software and linked to a
database with all metadata collected at the time of sampling. Samples will be tracked using specialized
lab software and linked to our project database with all metadata collected at the time of sampling.



DTRA Basic Research Annual Report

Please answer all sections of the document. You are welcome to use figures and tables to
complement or enhance the text. For annual reports, please only describe work for the period of
performance (July 1, 2020 - June 30, 2021). (Project awarded September 24, 2020)

Granti/Award #: HDTRA1-20-1-0026

PI Name: Dr. Jonathan Epstein

Organization/Institution: EcoHealth Alliance

Project Title: Biosurveillance for Spillover of Henipaviruses and Filoviruses in Rural India

What are the major goals of the project?

List the major goals of the project as stated in the approved application or as approved by the agency. if
the application lists milestones/target dates for important activities or phases of the project, identify these
dates and show actual completion dates or the percentage of completion. Generally, the goals will not
change from one reporting period to the next. However, if the awarding agency approved changes to the
goals during the reporting period, list the revised goals and objectives. Also, explain any significant
changes in approach or methods from the agency approved application or plan.

Our overall goal is to characterize the distribution of henipaviruses and filoviruses in bats and
determine whether undetected spillover into livestock or people has occurred in rural communities in Uttar
Pradesh and Kerala, India. By enhancing capacity at partner institutions to conduct targeted surveillance
for filoviruses and henipaviruses in wildlife, domestic animals, and high-risk rural human populations,
using a One Health approach, we will reduce threat of future outbreaks. The proposed interdisciplinary
research effort has three objectives: 1) enhance capacity for surveillance for henipaviruses and filoviruses
in animals and people in UP and Kerala; 2) identify wildlife hosts for henipaviruses and filoviruses and
characterize prevalence within these hosts; and 3) conduct targeted biosurveillance in wildlife, livestock,
and humans to determine exposure rates and detect spillover of these high consequence viruses.

What was accomplished under these goals? For this reporting period describe: 1) major activities; 2)
specific objectives; 3) significant results, including major findings, developments, or conclusions (both
positive and negative); and 4) key outcomes or other achievements. include a discussion of stated goals
not met. As the project progresses, the emphasis in reporting in this section should shift from reporting
activities to reporting accomplishments.

Task 7,

1) Major activities: Apply for permits and ethical approvals.

2) Specific objectives: To gain approval across One Health research and project areas,
including human sampling permissions, animal sampling permissions, livestock sampling permissions,
ethical approvals, for compliant work to occur across both geographical states (UP and Kerala) through
both local and US-based approving bodies. This includes HMSC research permits/ethical approval via
the Indian Medical Research Council, local |ACUC permissions, wildlife permits will be obtained via the
Forest Department, US IRB and US-based [ACUC through Tufts Vet School.

1.1.1. Apply for HMSC research approvals & local ethical approvals;
1.1.2. Apply for IRB, IACUC, HRPO, ACURO approval in US;
1.1.3-2.1.3. Apply for wildlife permits in UP and Kerala.

3) Significant results: All local permissions and approvals have been generated and submitted
for local clearance. Significant efforts related to this objective include the submission of the human-
sampling ethics clearance through King George Medical University (KGMU), Kerala State and UP State
Forest Department permissions, livestock sampling permissions through Indian Council of Agricultural



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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Attachment 1 - Statement ef Work (SOW)
Project Title: Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Document Date: May 28, 2020

Objective

The project’s objectives are to determine the efficacy of machine learning (ML) techniques in
predicting epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats,
both intentional (e.g. novel genetically-modified pathogens) and natural (e.g. zoonotic).
Specifically, we will answer three questions that will enable rapid decision-making based on ML
models of epidemic impacts:

Q1: How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

Q2: What variables are most predictive of epidemic impacts at early stages of an outbreak?
Q3: At what point in epidemic growth do hybrid models that incorporate mechanistic
epidemiological components become more accurate than pure ML models?

This work will contribute to fundamental knowledge of properties and variables that contribute
most to predictive accuracy at early-stage stages in emerging outbreak prediction and
forecasting. It will enable the development of robust and deployable predictive models at early
stages of outbreaks, and support informed choices for data collection and model selection.

Scope

The awardee proposes a two-year study to determine the efficacy of ML techniques in predicting
epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats. The
awardee team shall focus on the following major goals and milestones:

e Year 1, Milestone 1 (6 months): Create and curate predictor database, project
infrastructure, continuous integration/validation, establish data-imputation and automatic
predictor workflow (Tasks 1.1-1.2).

® Year 1, Milestone 2 (12 months): Bayesian additive regression tree (BART) model fitting
and tuning, determination of variable contribution to prediction accuracy (Task 1.3)

@ Option Year, Milestone 3 (18 months): Maintain and document project infrastructure,
extend the database to include additional predictors, extend the BART model to ingest
genetic data (Tasks 2.1-2.3)

© Option Year, Milestone 4 (24 months): Construction of hybrid ML/mechanistic model
structure, comparative testing of pure and hybrid version, determination of timing when
current data overtakes transfer data (Task 2.4)

Background

Situational awareness and risk assessment are critical at early stages of disease outbreaks, when
interventions can best mitigate adverse outcomes. Yet decision-makers often face a paucity of
data at these stages. Traditional modeling methods to forecast disease impact severity rely
heavily on prior knowledge or assumptions about mechanisms of disease spread progression, and
they frequently do not anticipate the actual mechanisms.'? They also take considerable time and
effort to develop.


Please fill out the requested information on all tabs (click on each bottom colored tab or
sheet} labeled Cover Page, Personnel, Publications, Presentations, Impacts, Intellectual
Property, and Transitions. For annual reports, please only describe DTRA funded work for

the reporting period (July 1, 2019 - June 30, 2020}.
Tab Description

Providing information about the project participants and collaborating organizations allows an assessment of

performance in promoting partnerships and collaborations.

The products from an effort demonstrate the excellence of the research and efficacy with which the results are being

communicated to colleagues, potential users, and the public. Publications are the characteristic product of research.
blicatio If there is nothing to report during this reporting period, the awardee shall state "Nothing to Report".

The products from an effort demonstrate the excellence of the research and efficacy with which the results are being

communicated to colleagues, potential users, and the public. Many projects (thought not all) may develop products

other than publications. In all cases, if there is nothing to report during this reporting period, the awardee shall state
Presentations "Nothing to Report”.

knowledge, enlarges the pool of people trained to develop that knowledge and techniques or put it to use, and

improves the physical, institutional, and information resources that enable those people to get their training and

perform their functions. In all cases, if there is nothing significant to report during this reporting period, the awardee

shall state "Nothing to Report”.
Information on any intellectual property demonstrates the value of protectable innovative ideas resulting from

sponsored research and includes patents, inventions, and licenses. Submission of this information is not a substitute
for other invention reporting requirements under the awards terms and conditions. In all cases, if there is nothing
ee era ET es) clas ge Significant to report during this reporting period, the awardee shall state "Nothing to Report".

Refers to cases where knowledge resulting from your DTRA sponsored effort will be further developed for, or will be
Transitions used in, a technology application. Transition sponsors can be entities in DoD, other federal agencies, or industry.



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Recent theory suggests disease dynamics are fundamentally predictable*, and ML methods have
been successfully applied in emerging disease science. ML refers to a group of methods, ranging
from simple nearest-neighbor approaches to function-approximation methods such as boosted
trees and penalized splines, to complex deep-learning neural networks. ML has been used to
predict the likelihood of disease emergence and the zoonotic potential of various hosts and
viruses*-°, and deep-learning approaches have been used to infer viral hosts from the
comparatively richer data of genetic sequences of viruses.’*

ML methods are generally unstructured, flexible, and offer greater predictive power over
traditional statistical approaches. However, these methods are data-intensive, and a key
challenge with ML development is data paucity and/or a low signal-to-noise ratio. Surveillance
data for even existing, well-studied diseases is rarely rich enough for the data scale of many
modem ML techniques. For instance, a ML approach to predict impacts from a new outbreak of
Ebola would draw training data from fewer than twenty previous events, each with varying
levels of data coverage.

Transfer learning, or data fusion, provides a solution to data paucity issues by using data from
other domains to train parts of models that are general, thus limiting new data requirements for
more specific components. Hierarchical modeling, or partial pooling, is a transfer learning
approach for partitioning out which data can be shared between models and which are only
applicable to a specific case. For instance, Ebola fore for countries outside the historic
range of the disease may be improved by calibrating against Cholera outbreaks, which have
occurred both within and outside countries with Ebola outbreaks. The degree to which the
disease outcomes are correlated is learned, rather than assumed.

An alternative recent development in disease forecasting is the development of hybrid models
that incorporate simple mechanistic structures and predictive ML methods to learn model
parameters. These approaches have demonstrated high predictive accuracy for near-term
forecasting of epidemic growth and impact, particularly during the COVID-19 pandemic’.
However, these approaches require adequate time-series of data to project forward from current
conditions. Their utility in early stages and long-term forecasting accuracy has yet to be
determined. In opting to apply these models, it is critical to understand these data requirements
and at what point in an outbreak hybrid approaches can overtake pure ML early-stage predictions
in accuracy. This project seeks to develop, apply, and compare pure and hybrid-ML techniques
to enable the development of robust, rapidly usable predictions at early stages of outbreaks.

Key references (additional references can be found in the Technical Proposal):

L MacDiarmid, S. C. et al. Handbook On Import Risk Analysis for animals and animal
products: quantitative risk assessment. Vol. 2 (Office International des Epizooties, 2004).

2 Moura, J. A., McManus, C. M., Bernal, F. E. M. & de Melo, C. B. An analysis of the
1978 African swine fever outbreak in Brazil and its eradication. Rev. Sci. Tech. 29, 549-
$63, doi:10.20506/rst.29.3, 1992 (2010).

3 Delgado, J. e¢ af, U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing
Controls. Risk Anal. 37, 1768-1782, doi:10.111 L/risa.12704 (2017).


Frter all presentations (poster or oral} given during the reporting period that resulted from this DTRA project or contained significant results from this DTRA project.

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HOTRAT-20-1-0026

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HOTRAL-20-1-0026

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HOTRAT-20-1-0026

HOTRAT-20-1-0026

HOTRAL-20-1-0026

MOTRAL 20.1 0026

HOTRAT-20-1-0026

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HOTRAT-20-1-0026

HOTRAT-20-1-0026

HOTRAI-20-1-0026

HOTRAL-20-1-0026

MOTRAL 20.1 6026

Proceed to Impacts



RESEARCH & RELATED BUDGET - Budget Period 2

OMB Number: 4040-0001
Expiration Date: 12/31/2022

ORGANIZATIONAL DUNS:

Enter name of Organization: [po jeaiet: Alliance

Budget Type: [XjProject [_] Subaward/Consortium Budget Period: 2

A. Senior/Key Person

Start Date:

End Date: |¢

Months Requested Fringe Funds
Prefix First Middle Last Suftix Base Salary (3) Cal. Acad. Sum. Salary ($) Benefits ($) Requested ($)
are Juoam l l 16,250.00] 3, G85 05 58, 916.03)
Project Role: levsen
| Ik | [so | 3 2 35,411.09]
Project Role: [senior Engineer
[ Jemma [eesaersozs | [ | 1 61,087.03]
Project Role: [ ]
| Total Funds requested for all Senior
Additional Senior Key Persons: Add Attachment | Key Persons inthe attachedfile [id
Total Senior/Key Person 368.00
B. Other Personnel
Number of | Months Requested Fringe Funds
Personnel Project Role Cal. Acad. Sum. Salary ($) Benefits {$) Requested ($)

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Tracking Number:GRANT13118386

Total other Personnel [___

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


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Emma Mendelsohn
EcoHealth Alliance, 520 Eighth Avenue, Ste. 1201, New York City, NY 10001, USA
E-mail: mendelsohn@ecohealthalliance.org

Professional Preparation

Wesleyan University, CT Earth and BA 2010
Environmental Science
Duke University, NC Ecotoxicology and MEM 2015

Environmental Health

Appointments

Research Scientist, EcoHealth Alliance 2018 - present
Open-source software peer reviewer, rOpenSci 2019 - present
Project Scientist, Integral Consulting 2015 - 2018
Statistics Teaching Assistant, Duke University 2014
Environmental Health Science Intern, Cardo ChemRisk 2014
Publications

Li, H., E. Mendelsohn, C. Zong, E. Hagan, W. Zhang, N. Wang, S. Li, H. Yan, H. Huang, G.
Zhu, N. Ross, A. Chmura, P. Terry, M. Fielder, M. Miller, Z. Shi, P. Daszak. (2019). Human-
animal interaction and bat coronavirus spillover potential among tural residents in Southern
China. Biosafety and Health. 1(2):84-90.

Iwai, H., Hoberman A.M., Goodrum, P.E., Mendelsohn, E., and Anderson, J.K. (2019).
Addendum to Iwai and Hoberman (2014) Reassessment of Developmental Toxicity of
PFHxA in Mice. International Journal of Toxicology. 38(3):183-191.

Mendelsohn, E., A. Hagopian, K. Hoffman, C.M. Butt, A. Lorenzo, J. Congleton, T.F. Webster,
and H.M. Stapleton. (2016). Nail polish as a source of exposure to triphenyl phosphate.
Environment International. 86:45 51.

Hoffman, K., C.M. Butt, T.F. Webster, E.V. Preston, S.C. Hammel, C. Makey, A. Lorenzo, E.M.
Cooper, C. Carignan, J.D. Meeker, R. Hauser, A. Soubry, S.K. Murphy, T.M. Price, C. Hoyo,
E. Mendelsohn, J. Congleton, J.L. Daniels, and H.M. Stapleton. (2017). Temporal trends in
exposure to organophosphate flame retardants in the United States. Environmental Science
and Technology Letters. 4(3):112-118

Kopelovich, L., A.L. Perez, N. Jacobs, E. Mendelsohn, and J.J. Keenan. (2015). Screening level
human health risk assessment of toluene and dibutyl phthalate in nail lacquers. Food and
Chemical Toxicology. 81:46 53.



Tasks to be initiated after Year 1:

Task

Samples and Laboratory Techniques

Samples:

Sample collection will be performed by the JUST field team, under supervision of the Country PI,
Dr. Ehab Abu-Basha. This project is conducted by a large group of scientists, researchers and
trained field personnel from Jordan University of Science and Technology and Princess Haya
Biotechnology Center. Several graduate students are included in the research team where they
will receive intensive training on laboratory techniques including serology such as ELISA,
complement fixation test, antibody neutralization test as well as molecular diagnostic techniques
including DNA isolation, cDNA, PCR, and sequencing. This group of young researchers will
continue to provide research and diagnostic services in various locations in the country.
Therefore, the project will not only help us be part of a global initiative for the surveillance of major
emerging and remerging viral infections of both human and animal origins, but also will provide a
nidus for continued national development of capacity in the surveillance and diagnosis of these
diseases in Jordan. Furthermore, the project will help provide animal care takers, farmers, and
the public at large with valuable data and unique educational opportunities about the occurrence,
recognition, and prevention of important zoonotic diseases.

Laboratory or Clinical Techniques:

The pseudo-particle neutralization (ppNT) assay is technique new to JUST and Jordan. Human
serum will be tested for MERS-CoV antibodies at the Princess Haya Biotechnology Center and
Molecular & Virology Laboratory at JUST. Co-I Kayali from Human Link will be training the JUST
team to implement this method.

Many molecular and diagnostic assays by a group of well trained, national scientists and young
graduate students and technicians will be performed according to the latest scientific standards.
Highly advanced molecular diagnostic assays such as cloning, preparing different PCR products
for sequencing, sequence analysis, and utilizing data entry will be performed and routinely used
in this laboratory. This will reduce the running time to obtain valuable diagnostic results at a
significantly lower cost since scientists and researchers from Jordan used to send such samples
to international specialized laboratories around the world for cloning and sequencing.

In Jordan, the molecular and virology laboratory is expected to be an important hub for training
and preparing future scientists as many graduate students are being trained in the laboratory to
perform various techniques from sample handling to DNA extraction, cDNA synthesis, performing
different PCR protocols, cloning, plasmid purification, and sequencing analysis. Several Graduate
and undergraduate students will be trained during project time.

12



Future Tasks

Subtask 1.3 Conduct biological sampling of animals inthe study areas

Subtask 1.4 Conduct lab testing for Al, MERS-CoV brucellosis and leptospirosis

livestock/poultry interfaces display evidence of infection.

Subtask 2.1 identify sites and conduct enrollment for human study component

Activities and results this year:

°

The project staff determined there will likely be a
higher rate of loss to follow-up than initially
projected. To account for an increased loss to
follow-up, the target number for human
enrollment for the first year will be increased to
100 people at each site for a total of 500 people
for the first year. Protocol and approval
documents have been updated as appropriate.
The project staff have selected the villages
below for sampling:

WC34+58 Dabet Hanut, Quaira, Aqaba
HHF4+55 Al-Mansoura, Shoabak, Ma‘an
8G3R83WF+5G Al Damkhi, Karak
RRM8+8F Azraq, Zarqa

F4PG+QH, Hosha Sub-District, Ramtha, Figure 3. Map of the study area and
Irbid villages selected within the 5 regions.
The team developed a draft Human and Animal

sampling protocol in preparation for enrollment of human and animal participants.

ARON>

Subtask 2.2 Obtain local permissions and approvals to work with human subjects

Activities and results this year:

°

Obtained human subjects research approval from the local ethics review board

at JUST (2/23/21). The human subjects research and human sampling protocol

was submittedto HML IRB for US-based approval.

Prior to purchasing equipment, the team needs to acquire a tax exemption

letter from the Jordanian government.

Supported JUST in their application for a Federal Wide Assurance number.
Approval of the human subjects research and human sampling protocol from HML
IRB was obtained (4/23/21).

Submitted an amendment to the local ethics review board at JUST and full approval
was given (4/26/21).

The human subjects research and human sampling protocol was submitted to the
Human Research Protection Office (HRPO) (5/4/21).

Biological Threats Reduction Program


Cautiol
Cautiol
Cautiol
Cautior
wrote:

Good Morning Dr. Chmura,

I just wanted to follow up to confirm that you did receive the below e-mail. I hope you had a nice
weekend. Thanks in advance and have a wonderful day!

Very Respectfully,

Contract Specialist

Defense Threat Reduction Avene:

Pe

<Cautio}
Caution

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< Cauti
Caution
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Original Messag

Sent: Monday, Augus

3, 2020 3:15 PM

To: ‘Aleksei Chmura’ <chmura@ecohealthalliance.org <

Caution-mailto:chmura@ecohealthalliance.org

> <Caution-mailto:chmura@ecohealthalliance.org <

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< Caution-Caution-mailto:chmura@ecohealthalliance org < Caution-mailto:chmura@ecohealthalliance.org > > <

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< Caution-Caution-mailto:chmura@ecohealthalliance org < Caution-mailto:chmura@ecohealthalliance.org > > >

> < Cantion-Caution-Caution-Caution-Caution-mailto:chmura@ecohealthalliance.org <

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> < Caution-Caution-mailto:chmura@ecohealthalliance.org <

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Caution-Caution-

aution-mailto:chmura@ecohealthalliance.org < Caution-mailto:chmura(@ecohealthalliance.org >



Noam Rass
EcoHealth Alliance, 460 W 34" St, New York, NY 10001
E-mail: ross@ccohcalthalliance.org

Professional Preparation

Brown University Environmental Science BS. 2006
University of California-Davis Ecology Ph.D. 2015
Appointments

Principal Scientist, Computational Research, EcoHealth Alliance 2020 - present
Senior Research Scientist Ecologist, EcoHealth Alliance 2016 - 2019
Disease Ecologist, EcoHealth Alliance 2015 - 2016
Graduate Researcher, University of California-Davis 2010 - 2015
Senior Analyst, Environmental Strategy and Markets, GreenOrder 2007 - 2009
Analyst, Environmental Strategy and Markets, GreenOrder 2006 - 2007
Contract Researcher: Energy Efficient Products Initiative, Wal-Mart 2006
Publications

Albery, G.F., Eskew, E.A., Ross, N. et al. Predicting the global mammalian viral sharing
network using phylogeography. Nature Communications 11, 2260 (2020).

Carlson CJ, Albery GF, Merow C, Trisos CH, Zipfel CM, Eskew EA, Ross Net al. Climate
change will drive novel cross-species viral transmission. bioRxiv, Cold Spring Harbor
Laboratory; (2020)

Eskew EA, White AM, Ross N, Smith KM, Smith KF, Rodriguez JP, et al. United States wildlife
and wildlife product imports from 2000 2014. Scientific Data 16;7(1).

Li, H., Mendelsohn, E., Zong, C., Zhang, W., Hagan, E., Wang, N., ... Ross N., ... Daszak, P.
(2019). Human-animal interactions and bat coronavirus spillover potential among rural
residents in Southern China. Biesafety and Health, 1(2), 84 90.

Ross, N., Eskew, E., & Ray, N. (2019). citesdb: An R package to support analysis of CITES
Trade Database shipment-level data. Journal of Open Source Software, 4(37), 1483.

Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized
additive models in ecology: an introduction with mgcv. PeerJ, 7, 6876.

Carlson CJ, Kracalik I, Ross N, Alexander K, Hugh-Jones ME, Fegan M, Elkin B, Epp T, Shury
T, Bagirova M, Getz WM, Blackburn JK. (2019). The global distribution of Bacillus
anthracis and associated anthrax risk to humans, livestock, and wildlife. Nature
Microbiology. 4(8):1337-1343 a

Rostal MK, Ross N, Machalaba C, Cordel C, Paweska JT, Karesh WB. (2018) Benefits of a One
Health approach: An example using Rift Valley fever. One Health 5:34-36.

Olival KJ, Hosseini P, Zambrana-Torrelio C, Ross N, Bogich T, Daszak P. (2017) Host and viral
traits predict zoonotic spillover from mammals. Nature 546:646 650.

Salerno J, Ross N, Ghai R, Mahero M, Travis DA, Gillespie TR, Hartter J. (2017) Self-reported
fever associated with human-wildlife interactions across park landscapes in western Uganda.
EcoHealth 14(4):675-690.

Schreiber 8, Ross N. (2016) Individual-based integral projection models: The role of size-
structure on extinction risk and establishment success. Methods in Ecology and Evolution
7(7):867-74.

Boettiger C, Ross N, Hastings A. (2013) Early warning signals: The charted and uncharted
territories. Theoretical Ecology 6(3):255-64.

1


question will support survey strength.

Future Subtask(s)

Sublask 3.2 Conduct behavioral risk factor surveys in people

Subtask 4.1 Generate maps of laboratory
results

Activities and results this year:

o The project staff created a geospatial risk
summary report using the package R
Markdown in RStudio for future stakeholder
meetings that includes maps to demonstrate
the percentage of positive tests over the
total amount of humans and animals tested
for the four zoonotic pathogens in Jordan for
this project.

Future Subtask({s):
Subtask 4.2 Generate risk maps with human

behavioral risk factors

V. Alan her
ind Lebanon

ing of MERS-~
zoon. in nr:

Subtask 5.1 Conduct project kick-off meeting in
Amman with local stakeholders

Activities and results this year:

Reducing the Threat af Micdle East Respiratory
Syndrome Coronavirus and Avian Influerza in Jordan
& Stengthening Regional Disease Surveilance
Capacity

Figure 5. Geospatial map R Markdown file
with mock taboratory data

o The Jordan MERS/AI project launch meeting was held on January 27", 2021.
In order to adhere to COVID-19 safety guidelines, the meeting was held
virtually using a password-protected system. The launch meeting was a
success and highlighted the centrality and investment in collaboration for this
project. The launch was also an opportunity to engage additional key
stakeholders to promote synergies with existing biodefense/health security
activities, and provided a forum to share One Health approaches that could
potentially be sustained by the Ministry of Health.


Biosurveillance for Spillover of Henipaviruses and Filoviruses in Rural India,

Dr. Jonathan Epstein
4 EcoHealth Alliance
| HDTRA1-20-1-0026

Objective: To enhance capacity for surveillance for high priority zoonoses in
animals and people in Uttar Pradesh and Kerala. To identify wildlife hosts for
henipaviruses and filoviruses and characterize their distribution near at-risk
human populations, and to conduct targeted biosurveillance in wildlife, livestock
and humans to detect exposure to or infection with high consequence viruses.
Threat Reduction Impact: 1} enhancing field and laboratory capacity to safely
conduct biosurveillance in animals and people for henipaviruses and filoviruses;
and 2) conducting targeted biological surveillance in wildlife (esp. bats),
livestock and people in rural communities in Uttar Pradesh and Kerala to
determine reservoir hosts for and detect spillover of these viruses; and 3) assess|
the role of livestock and geography in exposure rates of henipa- and filoviruses
in rural communities. Capacity/Capability Building: Through the development of
lab capacity, joint multi-sectoral field surveillance activities, and graduate
student training, India’s capacity to detect and respond to emerging zoonoses
and identify additional causes of AES and VHF will be significantly strengthened

Surveillance in animals

A and people at high risk to
“ detect & respond to
Le ¥ spillover earlier

Status of effort: Current collaboration with public
health and vet partners in UP & Kerala; laboratory
team hired, Luminex and lab supplies procurement
underway, local permissions submitted, archived
human samples from study sites currently available
25 reagents for filo and henipavirus antibody
detection produced and validated.Personnel
Supported: 4 Researchers, 4 University Faculty, 2
Field Veterinarians, 2 Graduate Students, 1 Post-
Doctoral Fellow, 2 Scientific Advisors; 6 Lab
Research Assistants/Techs; 6 Field Scientists, 1
Program Assistant. Publications & Meetings:

Enhance capacity within India to conduct surveillance fc
henipa- and filoviruses Years 1-5Produce & validate
reagents to detect henipa- and filovirus antibodies Year:
5.Conduct wildlife, livestock, and human biosurveillance
rural communities Years 1-5.Carry out hospital-based a
encephalitis and viral hemorrhagic fever studies in clinic
settings Years 1-5Funding Profile: Y1 $970,566.84
$995,322.63 Y3 $999,666.56 OY4 $998,965.88 OY5
$998,985.95 TOTAL Y1-Y5 $4,963,517.86 Contact
information: Pl: Jonathan H. Epstein 21238-4467
Epstein@ecohealthalliance.orgCo-Pl: Amita Jain

a)



Ce: Dr. Jon Epstein <epstein@ecohealthalliance.org < Caution-mailto:epstein@ecohealthall

Sullivan <sullivan@ecohealthalliance.org < Caution-mailto:sullivan@ecohealthalliance.org > >; {PX6)
KeX6)

Subject: Re: [Non-DoD Source] FRBAA 14-6-2-0454

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

DearfP}O)

Thank you for your continued patience with us and our partners as we gathered the requested documentation
and clarifications.

Attached are the following files:

- DTRA India KVASU budget justification v0 docx
- DTRA India KVASU supporting docs.pdf

- DTRA India HMJF budget justification v0 .docx
- DTRA India HMJF supporting docs.pdf
- DTRA India HMJF Supplies breakout with live links/URLs.xIsx

- DTRA India KGMU supporting docs.pdf
- DTRA India KGMU budget justification vO docx

- DTRA India EHA supporting docs.pdf
- DTRA India EHA budget justification v01.docx
- DTRA India EHA budget vO1.xIsx

- DTRA India EHA Clarifications vO1 xlsx
- DTRA India EHA Questions and Responses vO1 docx

Note that we have included (last file listed above) a word document version of our responses to the questions
emailed previously, but these also are included in our Clarifications (xIsx} file.

Please call anytime, if you have questions, require additional details, or would like us to rapidly direct you to
any specific documentation in our attached files.

Many thanks!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff &
Authorized Organizational Representative


Quarterly Report: HDTRA12010029 - Year 1, Quarter 2 (12/29/20 - 03/28/21)

= Safe human and animal sampling - led by Dr. Basil Amarneh and Dr. Zaidoun
Jijazeen

Subtask 5.6 Data Management

co The project staff have finalized data management
tools and decided to use ODK connect servers to
securely store survey data and Airtable servers for
human and animal lab data. Only the specific app
users for each of the servers can access the secure

data.
o Training for the data management systems was Figure 4. Second virtual day: first annual
completed in the project training workshop. project training workshop.

Subtask 3.7 Conduct presentations/mectings af times and places specified

o Project kick off for HDTRAI-20-1-0029 Jordan was held on January 13, 2021. The kick-
off meeting was a success and the team had a positive collaboration with DTRA, and Jordan
partners.

o Meeting on March 7" at Jordan Armed Forces GHQ (JAF) with LTC. Mohammad Malkawi,
Arms Control & International Organizations branch, Directorate of Planning & Organization
(DPO), JAF to follow up with letters send to Ministry of Agriculture, Ministry of Health and
Ministry of Environment to form a Focal point committee to the project. Letters are also sent
to RSS, Badia Fund, WHO, FAO, USAID, AOAD and RVC to join the committee. This
committee will follow up on One Health approach as well.

Future Tasks

Subtask 3.3 Host annual partners and stakeholders meeting

Subtask 3.4 Complete annual report and share with partners and stakeholders

Subtask 3.5 Conduct local-regional training workshops

o A proposed date has been set for the training at the Human Link Lab at the National
Research Centre, Egypt. While details remain to be confirmed due to considerations for
COVID-19, the team looks forward to engaging with our regional stakeholder to further

serve the regional biosecurity and capacity-building objectives of the project.
Tasks to be initiated after Year 1:

Task 6. Identify specific moditiable risk factors for human infection with these zoonoses

Presentations and Publications
1. Presentations and Other Conference Submissions: All Institutes

o Developed slide set to present project concept for wider awareness of an applied One Health
approach for threat reduction.

2. Publications: All Institutes

No project scientific publications to report as of yet.

Biological Threats Reduction Program Page 5 of 5


Subcontractor

SUBCONTRACTOR COST DATA —— Prime 1 (Fill-in)

Subcontracts/Interorganizational Transfers — A fully disclosed cost proposal as detailed as the Offerar’s cost proposal including support documentation
In accordance with FAR 15.404-3(b) and 15,404-3(c), the prime contractor shall perform and provide a cost/price analysis of each subcontractor’s cost

Certified cost or pricing data may be required for subcontractor proposals over $2,000,000.00, in accordance with Section 811 of the National Defense
Authorization Act for Fiscal Year 2018 (Pub. L. 115-91).

Type of Competitive J
sn . ype of Quotes or Sole Cost/Price
Subcontractor CAGE Competitive/Sole | Costiprice analysis | Subcontract (i.e., Sonce Total amount exceeds | A ivaig
Code Source included (Y/N) | Fixed Price, Time , $750K (VIN)
and Materials, etc.)| 20cumentation (Note 1)
+ 86) included (Y/N)
Note 1; Provide the location (section, page) in the proposal's Cost Narrative/Supporting Documentation (BAA para 3.5.2.2.1) of the cost/price analysis for each individual subcontractor.
Offeror:

Page 4 of 9 Pages


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Background

Situational awareness and risk assessment are critical at early stages of disease outbreaks, when
interventions can best mitigate adverse outcomes. Yet decision-makers often face a paucity of
data at these stages. Traditional modeling methods to forecast disease impact severity rely
heavily on prior knowledge or assumptions on mechanisms of disease spread progression, and
they frequently do not anticipate the actual mechanisms.'? They also take considerable time and
effort to develop.

Recent theory suggests disease dynamics are fundamentally predictable*, and machine learning
(ML) methods have demonstrated utility for some aspects of emerging disease science, e.g. to
predict the likelihood of disease emergence and zoonotic potential of various hosts and viruses*°,
and deep learning has been used to infer viral hosts from the comparatively richer data of genetic
sequences of viruses.”*

Machine learning (ML) refers to a group of methods, ranging from simple nearest-neighbor
approaches to function-approximation methods such as boosted trees and penalized splines, to
complex deep-learning neural networks. These methods are generally unstructured, flexible, and
offer greater predictive power over traditional statistical approaches. However, these methods
can also be data-intensive; a key challenge with ML methods development is that data are too
sparse, or signal/noise ratio too low, to effectively train algorithms. Surveillance data for even
existing, well-studied diseases is rarely rich enough for the data scale of many modern machine
learning or deep learning techniques. For instance, an ML approach to predict impacts from a
new outbreak of Ebola would have training data from to less than twenty previous events for
which there is comprehensive epidemiological data from 10 West and Central African nations

Transfer learning, or data fusion, provides a solution for using data from other domains to train
parts of models that are general, thus limiting new data requirements for more specific
components. Hierarchical modeling, or partial pooling, is a transfer learning approach for
partitioning out which data can be shared between models and which are only applicable to a
specific case. For instance, Ebola forecasts from countries outside of the virus' historic range
may be improved by calibrating against influenza outbreaks, which have occurred both within
and outside countries with Ebola outbreaks. The degree to which the disease outcomes are

correlated (e.g, similarity of modes of transmission) are learned, rather than assumed.

An alternative recent development in disease forecast is the development of hybrid models that
incorporate simple mechanistic structures and predictive ML methods to learn model parameters.
These approaches have demonstrated high predictive accuracy for near-term forecasting of
epidemic growth and impact, particularly during the COVID-19 pandemic®. However, these
approaches require adequate time-series of data to project forward from current conditions. Their
utility in early stages and long-term forecasting accuracy has yet to be determined. In opting to
apply these models, it is critical to understand these data requirements and at what point in an
outbreak hybrid approaches can overtake pure ML early-stage predictions in accuracy.


Objective: Work with government and private
sector partners to characterize causal factors in
animal-to-human transmission of MERS, Al, and
other zoonoses in Jordan, implement serological
technology for enhanced diagnostic and detection
capabilities, and strengthen regional detection and
reporting capacity to reduce biological

threats. Threat Reduction Impact: Establishing
laboratory and field SOPs to assist partners in
detecting presence and exposure to high-
consequence zoonotic pathogens.
Capacity/Capability Building: Pseudoparticle
neutralization assay technology transfer from
Human Link to JUST, training of graduate and
undergraduate students, site Selection and

SPARES OP Gita HAP Pas ty manapeb avigral risk
abe ned? VADER eroval pending, Ai and

laboratory teams prepping for field work and
sample testing. Personnel Supported: This project
is supporting 7 faculty, 6 other scientists, 2
graduate students, 2 undergraduate
studentsPublications & Meetings: 13 workshop
participants

Reducing the Threat of Middle East Respiratory Syndrome
Coronavirus and Avian Influenza in Jordan, PI:

Occupation(s)?Specific
behaviors?

4

MERS-CoVAY
InfluenzaBruc

* ptospira

ke

Nay,

Major Goals and Milestones:Determine evidence of
MERS, Al, and other zoonoses in humans, livestock
and poultry in Jordan (Y1-OY2)Identify risk factors
and modifiable behaviors for threat reduction (Y1-
OY2)Improve knowledge and diagnostic capacity (Y1-
OY2)Funding Profile Year 1 09/2020-09/2021: $984,351,
Year 2 09/2021-09/2022: $981,695, Year 3 09/2023-
09/2024: $991,118Contact information PI: Dr. William
B. Karesh,

212-380-4463 Karesh@ecohealthalliance.org



<iailto[b)6)

Subject: Re: [Non-DoD Source] FRBA 14-6-2-0471 Clarifications

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Much appreciate

[ cannot imagine how you get through all these documents. We only have a few proposals‘awards to justify and
it seems like an enormous task for us!

If [ may help you quickly locate certain documents in our forms and files, please call or text my handphone
anytime day or night.

Many thanks again!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

< tel: 1.646.413.3437 > (mobile)

Caution-www.ecohealthalliance.org <http:/‘caution-www ecohealthallianceorg’> <
Caution-http:/Avww ecohealthalliance.org >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Sep 2. 2020, at 12:17{0)6)
<mailtd®X°) F> wrote:

Yes , they were received. Thank you

Original Messag
From: William B. Karesh <karesh@ecohealthalliance.org <mailto:kareshi‘ecohealthalliance.org> <

Caution-mailto:karesh@ecohealthalliance.org > >

Sent: Wednesday, September 2, 2020 11:16 AM.

TofOVO) |
<mailtof¥6) b=

Ce: Aleksei Chmura <chmura@ecohealthalliance.org <mailto:chmura?ecohealthalliance.org> <
Caution-mailto:chmuraiéecohealthalliance.org > >

Subject: Fwd: [Non-DoD Source] FRBA14-6-2-0471 Clarifications

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the




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Grant Training DCP Matrix

Instructions: this matrix will help track and capture the alignment of BAA research training and professional development activities to BTRP’s
Domains, Competencies, and Proficiencies Framework. Please note: alignment is only required to the competency level definitions shown below.

J. in the left-hand column of the above chart, list every training activity to be performed on a separate line, using their task or sub-task

number (ex. Task 2; subtask 2.1; subtask 2.1.1)

2, place an x or tally against the competencies that align to the training activity. Competency decisions can be made by determining using
the definitions listed below and determining if a) the training subject matter corresponds to the defined competency or b) if the training
outcome corresponds to the defined competency. Please note: training activities can align to multiple competencies across the &
domains. (Note: if completing an annual report, stop at step 2; if drafting a Full Proposal, continue to step 3)

3. once completed, list the aligned competencies under its corresponding training activities in the grant proposal.

Domain

Domain Definition

Competency

Competency Definition

1. Disease
recognition and
prevention of spread

1.1 Understanding of
emerging and high-
consequence pathogens
(EHCP) in the following
realms: morbidity,
mortality, transmission,
control, disease
presentation and

distribution, and impact.

1.1.1 Disease characteristics and
impact

Disease characteristics and epidemiology of
especially high consequence pathogens
(EHCPs) and the diseases they cause, including
etiology, signs and symptoms, morbidity,
mortality, risk factors, incubation period,
period of communicability, mode of
transmission, clinical presentation,
mechanisms of spillover, reservoir species,
spatiotemporal and geographic distributions of
affected populations, and demographic
characteristics of affected populations
(species/age/sex/occupational status/ etc.),
including outcomes on population health.



we have decided to assemble and train the animal team in Kerala, In doing so, we have identified
pragmatic reasons for developing plans to work in both states at the earliest point of approval. This will
allow us to commence field work as scon as permissions are granted fully. This shift will give us
increased flexibility should there be unavoidable delays in one state or another, based on the pandemic,
natural/climate conditions, political conditions, or otherwise. It also allows the project to best utilize the

capacity and leadership of the emerging Animal Health team in Kerala, working in concert with team in
Uttar Pradesh.



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Grant Training DCP Matrix

DOMAINS

Domain

Domain 6

Domain 7

Domain 8

COMPETENCIES

Training Task

5.1.1

5.1.3

5.1.5

6.1.1

6.1.2 6.1.3

7.11

7.1.3

8.11

8.1.2

Ex. Task 1.3.2:
Conduct
laboratory-
based
diagnostic
trainings

1.2.2. hire and
train field and
lab teams

1.2.5-OY5.2.5.
Train students
&team
through field &
lab activities.

1.3.3-2.3.3.
Train
technicians
from KGMU to
develop
reagents & run
pseudovirus
neutralization
assay.



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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Background

Situational awareness and risk assessment are critical at early stages of disease outbreaks, when
interventions can best mitigate adverse outcomes. Yet decision-makers often face a paucity of
data at these stages. Traditional modeling methods to forecast disease impact severity rely
heavily on prior knowledge or assumptions on mechanisms of disease spread progression, and
they frequently do not anticipate the actual mechanisms.'? They also take considerable time and
effort to develop.

Recent theory suggests disease dynamics are fundamentally predictable*, and machine learning
(ML) methods have demonstrated utility for some aspects of emerging disease science, e.g. to
predict the likelihood of disease emergence and zoonotic potential of various hosts and viruses*°,
and deep learning has been used to infer viral hosts from the comparatively richer data of genetic
sequences of viruses.”*

Machine learning (ML) refers to a group of methods, ranging from simple nearest-neighbor
approaches to function-approximation methods such as boosted trees and penalized splines, to
complex deep-learning neural networks. These methods are generally unstructured, flexible, and
offer greater predictive power over traditional statistical approaches. However, these methods
can also be data-intensive; a key challenge with ML methods development is that data are too
sparse, or signal/noise ratio too low, to effectively train algorithms. Surveillance data for even
existing, well-studied diseases is rarely rich enough for the data scale of many modern machine
learning or deep learning techniques. For instance, an ML approach to predict impacts from a
new outbreak of Ebola would have training data from to less than twenty previous events for
which there is comprehensive epidemiological data from 10 West and Central African nations

Transfer learning, or data fusion, provides a solution for using data from other domains to train
parts of models that are general, thus limiting new data requirements for more specific
components. Hierarchical modeling, or partial pooling, is a transfer learning approach for
partitioning out which data can be shared between models and which are only applicable to a
specific case. For instance, Ebola forecasts from countries outside of the virus' historic range
may be improved by calibrating against influenza outbreaks, which have occurred both within
and outside countries with Ebola outbreaks. The degree to which the disease outcomes are

correlated (e.g, similarity of modes of transmission) are learned, rather than assumed.

An alternative recent development in disease forecast is the development of hybrid models that
incorporate simple mechanistic structures and predictive ML methods to learn model parameters.
These approaches have demonstrated high predictive accuracy for near-term forecasting of
epidemic growth and impact, particularly during the COVID-19 pandemic®. However, these
approaches require adequate time-series of data to project forward from current conditions. Their
utility in early stages and long-term forecasting accuracy has yet to be determined. In opting to
apply these models, it is critical to understand these data requirements and at what point in an
outbreak hybrid approaches can overtake pure ML early-stage predictions in accuracy.


Research (ICAR), and Institutional Biosafety Committee clearance through Kerala Veterinarian and
Agricultural Sciences University (KVASU), all of which have been submitted and are pending approval,
US-based IACUC and IRB have been generated and submission is imminent.

4) Key outcomes: All major local permissions have been submitted, and permission is expected
upon committee hearings. Individual meetings, including those with the State Surveillance Officer in Uttar
Pradesh, were held to discuss and prime stakeholders on project activities in order to facilitate
permissions and approvals. Once all local and US-based permissions are gained the full project activities
will be able to commence. Permission and approvals further represent a key opportunity and outlet for
stakeholder sensitization and have provided the project with a platform to introduce the objectives of the
research, build local relationships with stakeholders, and build interest and buy-in from community and
institutional gate-keepers.

Task 2.

1) Major activities: Enhance capacity within India to conduct biosurveillance for henipaviruses
and filoviruses

2) Specific objectives:
1.2.1. Transfer BioRad Bio-Plex 200 machine & reagents and PCR protocals to KGMU;
1.2.2. Hire and train field and lab teams, including a 7-day Luminex training
1.2.3 Select 1 PhD student at KGMU and USU and hire a postdoc at EHA
1.2.4-2.2.4 Create sample repository with tracking system at KGMU.

3) Significant results
The transfer of a Luminex machine has been initiated with Luminex US for shipment of KGMU in
Lucknow, UP, India. The shipment of supplies including reagents is underway between partners from
Uniform Services University (USU} to King George Medical University, for use with Luminex machine. A
Luminex training has been planned to occur once the machine arrives at KGMU, The lab team at KGMU
has been hired, including a doctoral research associate, research assistants, laboratory technicians, and
a data entry operator. Laboratory SOPs have been shared, and training sessions are being planned to
commence once supplies and equipment are received.

4) Key outcomes: 7 hospital collaborators have been identified and 6 laboratory and hospital
staff have been hired for this study. The purchase and shipment of a Luminex machine, as well as a
shipment of necessary laboratory supplies, has been initiated. Implementing serological surveillance for
groups of viruses known to cause encephalitis and hemorrhagic disease will greatly enhance the capacity
of local health systems to detect exposure to or past infection in wildlife, domestic animals and human
populations. To establish this capacity, EHA and KGMU will establish a joint human and animal
surveillance team and train members to safely sample animals and people, using appropriate personal
protective equipment and other biosafety techniques. We will provide a BioRad Bio-Plex 200 machine
with computer console to KGMU and coordinate training activities for serological surveillance and pan-
henipavirus and pan-filovirus consensus PCR assays for molecular analysis using existing PCR
capabilities at KGMU’s virology lab. The hiring of the research team allows for targeted and novel
capacity building efforts in at KGMU, a highly skilled and respected diagnostic laboratory, in disease
detection methods and One Health approaches to surveillance.

Task 3.

1) Major activities: Produce & validate reagents to detect henipa- and filovirus antibodies:

2) Specific objectives:
1.3.1,-OY5.3.1 Provide reagents to partner labs;
1.3,2-OY5,3.2 Produce reagents for newly discovered viruses;
1.3,3-2.3.3 Train technicians from KGMU to develop reagents & run pseudovirus neutralization assay,

3) Significant results: Protein production and assay validation is part of the ongoing
collaboration between EcoHealth Alliance, the Uniformed Services University, and the King George
Medical University in Lucknow, India. Throughout Year 1, our team has produced and validated reagents



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Phase II Technical Proposal
ABSTRACT

Accurately forecasting the impacts of novel biological agents in the early stages of their
emergence is critical to planning control measures for warfighters and civilians. However, at the
beginning of outbreaks, data about pathogen properties and dynamics are often scarce. Machine
learning (ML) techniques generally require large data sets, thus hampering their utility in these
contexts. Our proposed work addresses this challenge by using cross-disease prediction via
transfer learning — utilizing data from previous emergence events to improve early-stage
predictions for future events. We will use partial-pooling to determine the degree to which
information can be shared across diseases according to hierarchical similarities in pathogen and
host properties, and in environmental and population conditions. We will also determine which
properties contribute most to predictive accuracy, providing guidance for prioritizing data-
collection in early outbreak stages. We will test and compare two approaches — pure ML
predictions and hybrid ML/mechanistic models — to determine which provides better predictions
of outbreak size and impacts. We will determine at what point in an outbreak current data
overtakes historical data as a better predictor of outbreak dynamics. Making use of data sets from
multiple host species as well as efficient model-validation techniques, we will expand the size of
available training data and thus models’ predictive capacity.

SCOPE

Objective

Our primary research objectives are to determine the efficacy of ML techniques in predicting
epidemic impacts in early-stage, data-sparse conditions, particularly novel biothreats both
intentional (e.g. novel genetically-modified pathogens) and natural (e.g. zoonotic). Specifically,

we will answer three questions that will enable rapid decision-making based on ML models of
epidemic impacts:

Q1: How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

Q2: What variables are most predictive of epidemic impacts at early stages of an outbreak?
Q3: At what point in epidemic growth do hybrid models that incorporate mechanistic

epidemiological components become more accurate than pure ML models?

This work will contribute to fundamental knowledge of properties and variables that contribute
most to predictive accuracy at early-stage stages in emerging outbreak prediction and
forecasting. It will enable the development of robust and deployable predictive models at early
stages of outbreaks, and support informed choices for data collection and model selection.

1


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Ava Sullivan
PREDICT-2 Operations Coordinator
Country Liaison, India

EcoHealth Alliance
460 West 34th Street — 17th floor
New York, NY 10001
5 (direct)
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EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation


Change in plan for the next reporting period to
accomplish the goals:

Task 1: Determine the presence of MERS-CovV, Al, and other zoonoses among livestock
and/or poultry in Jordan

1.3 Conduct biological sampling in animals within the study area
1.4 Conduct lab testing for Al. MERS-CoV, brucellosis and leptospirosis
e These tasks were scheduled to begin in Q3 of Project Year Y1 but due to both the
COVID-19 pandemic and pending HRPO approval, we anticipate to begin sampling in
Q4 of Project Year Y1.

Task 2: Determine whether (and if so, the extent to which) humans at human-
livestock/poultry interfaces display evidence of infection

2.3 Conduct biological sampling
2.4 Conduct testing for Al. MERS-CoV, brucellosis and leptospirosis
« Same as above, we anticipate to begin sampling in Q4 of Project Year Y1.

Task 3: Characterize causal factors in animal-to-human transmission of these zoonoses
3.2 Conduct behavioral risk factor surveys in people

3.3 Analyze epi causal inference to identify modifiable risk factors
« Same as above, we anticipate to begin sampling in Q4 of Project Year Y1 and
preliminary analysis will shortly follow.

Task 4: Produce geospatial maps of zoonoses in livestock and poultry and of human
zoonotic transmission risk

4.1 Generate maps of laboratory results and share with partners
4.2 Generate risk maps with human behavior risk data and share with partners
« We anticipate to begin sampling in Q4 of Project Year Y1 and to generate the geospatial
maps for the following quarter, Q1, Y2.

Task 5: Strengthen local capacity for detection and reporting of MERS-CoV. Al, and other
zoonoses in Jordan, Iraq, and Lebanon

5.3 Host annual partners and stakeholders meeting

« No Change.
5.4 Complete annual report and share with partners and stakeholders
e No Change.

5.5 Conduct localfregional training workshops,
e Regional trainings will be conducted considering team safety and regional restrictions on
travel. However, we anticipate still holding the training at Human Link in 4 of Project
Year 1 as planned.
5.6 Dala management

e No Change.
5.7 Conduct presentations/meetings at times and places specified
« No Change.

14



Revised 14 September 2020 EXHIBIT B

1. Terms and Conditions Incorporated by Reference.

The DoD Research and Development General Terms and Conditions, most current version as
of the date of grant award, are hereby incorporated by reference and are available for
download at website http://www.onr.navy.mil/Contracts-Grants/submit-proposal/grants-
proposal/grants-terms-conditions.aspx.

2. Acceptance of Grant.

The recipient is not required to countersign the Grant document; however, the recipient
agrees to the conditions specified in the Research Grant and the Articles contained herein
unless notice of disagreement is furnished to the Grants Officer within fifteen (15) calendar
days after the date of the Grants Officer's signature. In case of disagreement, the recipient
shall not assess the Grant any costs of the research unless and until such disagreement(s) is
resolved.

3. Recipient Responsibilities.

The recipient will bear primary responsibility for the conduct of the research and will
exercise judgment towards attaining the stated research objectives within the limits of the
Grant's Terms and Conditions.

The Principal Investigator(s) (PI specified in the Grant award will be continuously
responsible for the conduct of the research project and will be closely involved with the
research effort. The PI, operating within the policies of the recipient, is in the best position to
determine the means by which the research may be conducted most effectively.

4, Standards for Financial Management Systems.

Where the Federal Government guarantees or insures the repayment of money borrowed by
the recipient, DTRA, at its discretion, may require adequate bonding and insurance if the
bonding and insurance requirements of the recipient are not deemed adequate to protect the
interest of the Federal Government.

DTRA may require adequate fidelity bond coverage where the recipient lacks sufficient
coverage to protect the Federal Government's interest.

Where bonds are required in the situations described above, the bonds shall be obtained from
companies holding certificates of authority as acceptable sureties, as prescribed in 31 CFR
Part 223, “Surety Companies Doing Business with the United States.”

5. Modification of the Grant.

The only method by which this Grant may be modified is by a formal, written modification
signed by the Grants Officer. No other communications, whether oral or in writing, are
valid.

Prior Approvals are required as follows:

1) Expenditures on equipment costing $5,000 or more not specifically identified in the
budget at time of award. (Approval via written notification from the Grants Officer.)

2) Expenditures for foreign travel not specifically identified in the budget at time of award.
(Approval via written notification from the Grants Officer.)

Page 2 of 14


Peter Daszak
EcoHealth Alliance, 520 Eighth Avenue, Suite 1201, New York, NY 10018, USA
E-mail: daszak@ecohealthalliance.org

Professional Preparation

Bangor University, UK Zoology BS 1986

Univ. of East London, UK Infectious Diseases PhD 1993
Appointments

President & Chief Scientist, EcoHealth Alliance New York 2009- present
Executive Director, Consortium for Conservation Medicine 2001- 2009
Faculty Research Scientist, University of Georgia 1999- 2001
Guest Researcher, Centers for Disease Control and Prevention (CDC) 1999

Faculty Research Scientist, Kingston University, UK 1993- 1998
Research Assistant, University of East London, UK 1989- 1992
Publications

Nikolay B, Salje H, Hossain MJ, Khan AKMD, Sazzad HMS, Rahman M, Daszak P, Stréher U,
Pulliam JRC, Kilpatrick AM, Nichol ST, Klena JD, Sultana 8, Afroj $, Luby SP, Cauchemez S
& Gurley ES. (2019). Transmission of Nipah Virus - 14 Years of Investigations in Bangladesh.
New Engl J Med 380:1804-1814

Carroll D, Daszak P*, Wolfe ND, Gao GF, Morel C, Morzaria S, Pablos-Méndez A, Tomori O,
Mazet JAK (2018). The global virome project. Science 359: 872-874

Zhou P, Fan H, Lan T, Yang X-L, Shi W-F, Zhang W, Zhu Y, Zhang Y-W, Xie Q-M, Mani 8,
Zheng X-S, Li B, Li J-M, Guo H, Pei G-Q, An X-P, Chen J-W, Zhou L, Mai K-J, Wu Z-X, Li
D, Anderson DE, Zhang L-B, Li S-Y, Mi Z-Q, He T-T, Cong F, Fuo P-J, Huang R, Luo Y, Liu
X-L, Chen J, Huang Y, Sun Q, Zhang X-L-L, Wang Y-Y, Xing S-Z, Chen Y-S, Sun Y, Li J,
Daszak P*, Wang L-F*, Shi Z-L*, Tong Y-G*, Ma J-Y* (2018). Fatal Swine Acute Diarrhea
Syndrome caused by an HKU2-related Coronavirus of Bat Origin. Nature 556: 255-258

Olival KJ*, Hosseini PR, Zambrana-Torrelio C, Ross N, Bogich TL, Daszak P* (2017). Host and
viral traits predict zoonotic spillover from mammals. Nature 546, 646 650

Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, Breit N,
Olival KJ, Daszak P* (2017). Global hotspots and correlates of emerging zoonotic diseases.
Nature Comm 8: 1124

Ge X-Y, Li J-L, Yang X-L, Chmura AA, Zhu G, Epstein JH, Mazet JK, Hu B, Zhang W, Peng
C, Zhang Y-J, Luo C-M, Tan B, Wang N, Zhu Y, Crameri G, Zhang S-Y, Wang L-F, Daszak
P*, Shi Z-L* (2013). Isolation and characterization of a bat SARS-like Coronavirus that uses
the ACE2 receptor. Nature 503: 535-538

Daszak P*, Zambrana-Torellio C, Bogich TL, Fernandez M, Epstein JH, Murray KA, Hamilton
H (2013). Interdisciplinary approaches to understanding disease emergence: The past, present
and future drivers of Nipah virus emergence. PNAS 110: 3681-3688

Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, Wang H, Crameri G, Hu Z, Zhang H, Zhang J,
McEachern J, Field H, Daszak P, Eaton BT, Zhang $8, Wang L-F. (2005). Bats are natural
reservoirs of SARS-like coronaviruses. Science 310: 676-679

Daszak P*, Cunningham AA, Hyatt AD (2000). Emerging infectious diseases of wildlife -
threats to biodiversity and human health. Science 287: 443-449



Quarterly Report: HDTRA12010029 - Year 1, Quarter 2 (12/29/20 - 03/28/21)

Task 2: Determine whether (and if so, the extent to which) humans at human-livestock/poultry
interfaces display evidence of infection.

Subtask 2.1 identify sites and conduct enrollnent for hinan study component

Activities and results this quarter:

o The project staff determined that it is likely that there will be a higher rate of loss to follow-
up than initially projected. To account for an increased loss to follow-up, the target number
for human enrollment for the first year will be increased to 100 people at each site for a total
of 500 people for the first year. Protocol documents have been updated as appropriate.

Subtask 2.2 Obtain local permissions and approvals to work with human subjects

Activities and results this quarter:

o Obtained human subjects research approval from the local ethics review board at JUST
(2/23/21). The human subjects research and human sampling protocol will now be submitted
to Health Media Lab IRB Research and Ethics (HML) for domestic approval.

© Prior to purchasing equipment, the team needs to acquire a tax exemption letter from the
Jordanian government.

o Supported JUST in their application for a Federal Wide Assurance number.

Future Tasks
Subtask 2.3 Conduct biological sampling

Subtask 2.4 Conduct testing for AL MERS-CoF. brucellosis and leptospirosis

Lask 3: Characterize causal factors in animal-to-human ransmission of these zoonoses.

Subtask 3.1 Obtain local permissions and approvals to work with human subjects

Activities and results this quarter:

o The Jordanian team and EcoHealth Alliance (EHA) behavioral risk scientists collaboratively
built and finalized the Animal History Form, Field Data Sheet, Human Questionnaire, and
Site Characterization Form.

o Local JUST IRB approval obtained.

o The Human questionnaire, Site Characterization Form, and Animal History Form have been
drafted and coded for Open Data Kit (ODK). Tablets were purchased for the Jordan team.

Subtask 3.3 Analyze epi causal inference to identify modifiable risk factors

Activities and results this quarter:

o Worked with the project staff and SMEs to compile a list of epidemiological risk factors,
behaviors, and general exposure risk factors to be used in data analysis.

o During the human subjects research workshop, worked to collaboratively fill-out a survey
guide to provide clarity of question purpose and application for field staff. In scenarios where
subjects might need support in understanding questions, ensuring that field staff understand
the risk factors being analyzed in each question will support survey strength.

Future Subtask(s)

Subtask 3.2 Conduct behavioral risk factor surveys in people

Biological Threats Reduction Program Page 3 of 5


Grant Training DCP Matrix

given to documents designed for health care
workers directly combatting an outbreak.

6.1.2 Identification and analysis

Knowledge and proficiency with principles and
processes for developing and maintaining
disease outbreak situational awareness
capabilities, including coordination and
communication or reporting across
surveillance, laboratory, and other data
sources, epidemic intelligence, data analysis,
and field epidemiological and outbreak
investigation techniques, such as contact
tracing, and decision-making in support of
these activities.

6.1.3 Communication systems

Knowledge and proficiency with development,
implementation, and sustainment of
communications systems for outbreak
situational awareness and response, including
multisectoral and intrasectoral coordination
(including via emergency operations centers or
equivalent assets), operational risk
communication, and public risk
communication.

7. Biosafety

7.1 The application of
knowledge, techniques, and
equipment to prevent
accidental exposure to and
release of emerging and
high consequence
pathogens (EHCP).

7.1.1 Biohazard Identification and
classification

Knowledge, understanding and proficiency
with identification and classification of
biohazards associated with EHCPs, for
laboratories, hospitals, other biomedical and
clinical facilities, and the community, including
biohazard risk assessment and
characterization, and biohazard identification
policies and compliance procedures.

7.1.2 Biohazard Control Measures

Knowledge, understanding and proficiency



authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Dear KPX6)

I just wanted to confirm that you received the email below earlier this week.

Thanks!!

William B. Karesh, D.V.M
Executive Vice President for Health and Policy

EcoHealth Alliance
520 Eighth Avenue, Suite 1200.
New York, NY 10018 USA

+1.212.380.4463 (direct)

+1.212.380.4465 (fax)

Ca Saution-www.ecohealthalliance.org <http://caution-caution-www.ecohealthallianceorg/> <
Caution-http://caution-Caution-www_ecohealthalliance.org/ > < Caution-
Caution-mailto:karesh(@ecohealthalliance.org < Cantion-mailto:karesh@ecohealthalliance.org > >

President, OIE Working Group on Wildlife
Co-chair, IUCN Species Survival Commission - Wildlife Health Specialist Group

EPT Partners Liaison, USAID Emerging Pandemic Threats - PREDICT-2 Program

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

Begin forwarded message:

From:Aleksei Chmura <chmura@ecohealthalliance.org <mailto:chmura@ecohealthalliance.org> <
Caution-mailto:chmura@ecohealthalliance.org > < Caution-Caution-mailto:chmura@ecohealthalliance org <
Caution-mailto:chmura@ecohealthalliance.org > > >

Subject:Re: [Non-DeD Source] FRBA14-6-2-0471 Clarifications.

Date: August 30, 2020 at 8:14:36 PM EDT


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

2.2.5 Update data from live sources and clean and maintain data continuously

Task 2.3 — BART Model Development and Testing: In the option year we will extend the
BART model to ingest genetic data and test the contribution of this data in enhancing or
recapitulating pathogen trait data.

Subtasks

2.3.1 Develop genetic predictor variable set via neural-network autoencoding

2.3.2 Modify BART and GAM structures to incorporate genetic variables

2.3.3 Implement within-disease, all-disease, and partial-pooled disease model versions

2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 2.4 — Hybrid ML-Mechanistic Model Development and Testing: In the option year will
develop the hybrid machine-learning mechanistic model, compare its performance to a pure ML
model and determine the switch-over time when the mechanistic model becomes more predictive
in the course of the outbreak.

Subtasks

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.4 Implement within-disease, all-disease, and partial-pooled disease model versions

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze prediction-level variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 Iteratively update models to improve performance



Emma Mendelsohn
EcoHealth Alliance, 520 Eighth Avenue, Ste. 1201, New York City, NY 10001, USA
E-mail: mendelsohn@ecohealthalliance.org

Professional Preparation

Wesleyan University, CT Earth and BA 2010
Environmental Science
Duke University, NC Ecotoxicology and MEM 2015

Environmental Health

Appointments

Research Scientist, EcoHealth Alliance 2018 - present
Open-source software peer reviewer, rOpenSci 2019 - present
Project Scientist, Integral Consulting 2015 - 2018
Statistics Teaching Assistant, Duke University 2014
Environmental Health Science Intern, Cardo ChemRisk 2014
Publications

Li, H., E. Mendelsohn, C. Zong, E. Hagan, W. Zhang, N. Wang, S. Li, H. Yan, H. Huang, G.
Zhu, N. Ross, A. Chmura, P. Terry, M. Fielder, M. Miller, Z. Shi, P. Daszak. (2019). Human-
animal interaction and bat coronavirus spillover potential among tural residents in Southern
China. Biosafety and Health. 1(2):84-90.

Iwai, H., Hoberman A.M., Goodrum, P.E., Mendelsohn, E., and Anderson, J.K. (2019).
Addendum to Iwai and Hoberman (2014) Reassessment of Developmental Toxicity of
PFHxA in Mice. International Journal of Toxicology. 38(3):183-191.

Mendelsohn, E., A. Hagopian, K. Hoffman, C.M. Butt, A. Lorenzo, J. Congleton, T.F. Webster,
and H.M. Stapleton. (2016). Nail polish as a source of exposure to triphenyl phosphate.
Environment International. 86:45 51.

Hoffman, K., C.M. Butt, T.F. Webster, E.V. Preston, S.C. Hammel, C. Makey, A. Lorenzo, E.M.
Cooper, C. Carignan, J.D. Meeker, R. Hauser, A. Soubry, S.K. Murphy, T.M. Price, C. Hoyo,
E. Mendelsohn, J. Congleton, J.L. Daniels, and H.M. Stapleton. (2017). Temporal trends in
exposure to organophosphate flame retardants in the United States. Environmental Science
and Technology Letters. 4(3):112-118

Kopelovich, L., A.L. Perez, N. Jacobs, E. Mendelsohn, and J.J. Keenan. (2015). Screening level
human health risk assessment of toluene and dibutyl phthalate in nail lacquers. Food and
Chemical Toxicology. 81:46 53.



Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

YEAR 1, QUARTER 1 PROGRESS UPDATES FOR EACH TASK

Task L: Determine the presence of VIERS-CoV. AL and other zoonoses among livestock and
poultry in Jordan,

Subtask fof ldentify sites for project studi

Activities and results this quarter:

o Project staff reviewed site selection criteria and with input from local subject matter experts
(SMEs), the list of sites was narrowed down to include villages with the three interface sites
represented and sufficient human population. From this list of thirteen potential sites, the Shiny
App, was used to randomly generate the final five sites in Jordan to be used for the field study
components of the project. Jordian project partners participated throughout the process and
learned the methodology and rationale for randomizing site selection.

“igs! : Se! ae os
Figure 1. Sites selected using spatial sampling “Shiny App” (red points).
Lefi image contains every point prior to SME local input and the right
image contains the randomly selected points.

Subtash 1.2 Obtain local permissions and approvals to work with anintaly

Activities and results this quarter:

o Jordan ACUC and Tufts IACUC approvals and ACURO surveillance project determination
were obtained in previous quarters. No further action is required.

Future Subtask(s)

Subtask 1.3 Conduct biological sampling of animals in the study areas
Subtash Lf Conduct lah testing for Ah MERS-Col,

brucellosis and leptospiroxin.

Task 2: Determine whether (and if so, the extent to which) humans at human-livestock‘poultry
interfaces display evidence of infection,

Subtask 2.1 ddentify sites and conduct enrollment for human studv component
Activities and results this quarter:

Biological Threats Reduction Program Page 2 of 7


¢C. Equipment Description

List items and dollar amount for each item exceeding $5,000
Equipment item Funds Requested {$)

Additional Equipment: | [Add attachment | | l[

Total funds requested for all equipment listed in the attached file |
Total Equipment

D, Travel Funds Requested ($)

1. Domestic Travel Costs ( Incl. Canada, Mexico and U.S. Possessions)
2. Foreign Travel Costs
Total Travel Cost

E. Participant/Trainee Support Costs Funds Requested {$)

1. Tuition/Fees/Health Insurance |
Subsistence |

. Other [ ] [ ]

panifirainee Support Coste [

ap wen

[___] number of Participants/Trainees

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Prime 1 (Fill
[rrp purpose | Mumber [Numberot [number | Al/Ratt [Per Diem Ferien | Renal | parking [eleage | Tan | other [TOTAL [supporting |

in}

City, State
New York, NY Conference Attendance: 1 2 5 $1,500.00 $155.00 $196.00 $0.00 $0.00 © $262.50 $300.00
Montpelier, France Totals _ $3,000.00 $1,395.00 $1,568.00 $0.00 $0.00 $0.00 $525.00 $600.00 $7,088.00
New York, NY Annual Technical Review: a 1 5 $350.00 $71.00 $161.00 $0.00 $0.00 © $262.50 $0.00
Alexandria, VA Totals $350.00 __ $319.50 $644.00 $0.00 $0.00 $0.00__ $262.50 $0.00 $1,576.00
New York, NY Conference Attendance: 1 2 5 $500.00 $55.00 $225.00 $0.00 $0.00 © $262.50 $300.00
Bozeman, Montana Totals $1,000.00 _ $495.00 $1,800.00 $0.00 $0.00 $0.00 $525.00 $600.00 $4,420.00
New York, NY Annual Technical Review: a 1 5 $350.00 $71.00 $161.00 $0.00 $0.00 © $262.50 $0.00
Alexandria, VA Totals $350.00_ $319.50 3644.00 $0.00 $0.00 $0.00 _ $262.50 $0.00 $1,576.00
TOTAL $14,660

+ When completing the “From” and "To" information, enter the city and state where travel will originate and end, For example, from Seattle WA to Washington DC. Do not list airport
codes.

+ Include the trip purpose. It must be referred to in the Technical Proposal. Publications, conference attendance, and presentations are encouraged but must be justified to and
approved by the Program Officer.

+ Remember that you cannot stay overnight the same number of days that you travel. There must always be one more day than night

+ Do not combine any categories.

+ For lodging and MIE, use federal Per Diem Rates found at hitp:i/www_defensetravel.dod.mil/site/perdiemCale. cfm.

+ For M&IE, input the full rate in the white block. The gray block will automatically calculate the total to include 75% for the first and last day of travel.

Estimates and the resultant costs claimed must contorm to the applicable Federal cost principles.
Include a separate section in the above table for the base and each option

if there are miscellaneous expenses associated with the trip, provide description and rationale.

Provide the location (section, page) in the proposal's Cost Narrative/Supporting Documentation (BAA para 3.5.2.2.1) used to support your price basis (e.g. copy of quote,
page from catalog, recent invoice or PO}.


- How has this research leveraged local and regional threat reduction networks to strategically address
current or future biological threats?

- How has this research strengthened the capabilities of partner country scientists to identify and secure
competitive research funding through networking and scientific coflaboration activities?

Nothing to Report

How have the results been disseminated to communities of interest?

if there is nothing significant to report during this reporting period, state “Nothing to Report.”

Use the impact questions below to guide your discussion of how the results have been disseminated to
communities of interest, Include any outreach activities that have been undertaken to reach members of
communities who are not usually aware of these research activities, for the purpose of enhancing public
understanding and increasing interest in learning or careers in science, technology, and the humanities.

Transparency

- How has this research endorsed a culture of transparency within, and between local, regional, and
international stakeholders associated with this effort?

- How has this research supported public awareness and prevention strategies in the local and regional
communities of partner country(ies)?

intellectual Property/Publications

- How have the protocols and information learned during the research project informed disease detection
and surveillance knowledge (protocols, SOPs, training) in the long-term?

- How will the information garnered from the research affect standard guidance on disease detection for
clinical health professionals? This impact can be in the form of guidance updates, clinical guidelines, or
supporting evidence for policy changes at the country-level.

Evidence Based Data Communication to Stakeholders

- How has this research enhanced or supported mechanisms (SOPs, commitlees, outreach, etc.) fo share
resources, expertise, and data between local, regional, and international stakeholders?

- How has this research supported a communication plan to national and international public health
stakeholders in the event of a public health emergency of international concern?

Nothing to report

What do you plan to do during the next reporting period to accomplish the goals?

if there are no changes to the agency-approved application or plan for this effort, state “No Change,”
Describe briefly, what you plan to do during the next reporting period to accomplish the goals and
objectives.

At the time of this report, there are no major changes to the agency-approved plan for this effort, and we
expect that all tasks and subtasks will be achieved despite the increased difficulties as a result of the
COVID-19 pandemic in India. However, an adjustment has been made in order to best achieve the goals
of the project. In the approved proposal, the earliest surveillance efforts for human hospital sampling,
human community sampling, wildlife sampling, and livestock sampling were slated to occur in firstly Uttar
Pradesh, while our efforts in Kerala were staggered to begin later in Y2, and increase in subsequent
years. During the period in which we are seeking permissions, approvals and permits, we have been
seeking permissions to work in both states. Based on leadership and identified capacity in Kerala for
performing wildlife training, especially through our partners at Kerala Wildlife Department, and KVASU,



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

This publicly releasable abstract is provided to DTRA for use in fulfillment of Section 8123 of the
Defense Appropriations Act and future versions of the same.

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

Project Summary

Accurately forecasting the impacts of new disease outbreaks is key to decision-making and
planning. However, at the early stage of outbreaks, little data is available to to make predictions.
Modern “Big Data” machine learning (ML) techniques generally require large data sets, reducing
their usability in these situations. Our proposed work addresses this challenge by using models
that can learn how much data from previous events is useful for making predictions about current
conditions. We will use this approach to understand what aspects of diseases, their hosts, or
environments can be used for prediction and how much they can contribute to making accurate

forec:


TofPVO)

<mailto] aution-
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<karesh@ecohealthalliance.org <mailto:karesh @ecohealthalliance.org> <
Caution-mailto:karesh(@ecohealthalliance.org > < Caution-Caution-mailto:karesh@ecohealthalliance,org <
:karesh(@ecohealthalliance.org > > >, Whitney Bagge <bagge@ecohealthalliance.org
<mailto:bagge@ecohealthalliance.org> < Caution-mailtocbagge@ecohealthalliance.org > < Caution-
Caution-mailto:bag ge@ecohealthalliance.org < Caution-mailto:bagge(@ecohealthalliance.org > > >, Catherine
Machalaba <machalaba@ecohealthalliance.org <mailto:machalaba@ecohealthalliance.org> <
Caution-mailto:machalaba@ecohealthalliance.org > < Caution-Caution-mailto:machalaba@ecohealthalliance.org <
Caution-mailto:machalaba@ecohealthalliance.org > > >, Joe Riccardi <riccardi@ecohealthalliance.org
<mailto:riccardi@ecohealthalliance.org> < Caution-mailto:

Caution-maill

ardi(@ecohealthalliance.org > < Caution-
ardi(@ecohealthalliance.org < Caution-mailto:riccardi(@ecohealthalliance.org > > >

Caution-maill

Please find attached seven files:

1) FRBAA14-6-2-0471_Clarifications_FINAL xlsx
2) EHA Budget Justification_FINAL.docx

3) EHA Documentation_FINAL pdf

4) Human Link Budget Justification_FINAL docx
5) Human Link Documentation_FINAL.pdf

6) JUST Budget Justification_FINAL docx

7) JUST Documentation_FINAL. pd

There are three sets of paired files with the requested documentation (PDFs) and track-change budget
justifications (MS Word) for EcoHealth Alliance and our two subcontracts under this proposal. We have also
included the clarifications (MS Excel) with responses to the specific questions in each tab.

Please let me know, if there are additional questions or any other documentation is required.
Many thanks!

-Aleksei

Aleksei Chmura, PhD
Chief of Staff &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1,212.380.4473 < tel: 1.212.380.4469 < tel: 1.212.380.4469 > > (office)
pe b > (mobile)
Caution-Caution-www.ecohealthalliance org <hitp //caution-caution-www.ecohealthalliancearg’> <
Caution-http:/caution-Caution-wwwecohealthalliance.ore/ > < Caution-Caution-hittp://www_ecohealthalliance.org.
< caution-Caution-http/‘www.ecohealthalliance.org! > >



Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.)

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: Peter Daszak

Support: J Current KX Pending LJ Submission Planned in Near Future —_] *Transfer of Support
Project/Proposal Title:

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus (MERS) and Avian Influenza (Al) in Jordan &
Strengthening Regional Disease Surveillance Capacity (current proposal)

Source of Support) DTRA

Total Award Amount: $4,975,681.29 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Jordan

Person-Months Per Year Committed to the Cal: 2.0 Acad: Sumr:

Support: LJ Current X Pending LJ Submission Planned in Near Future — L] “Transfer of Support

Project/Proposal Title:
CNHz2: Land-use change and microbial spillover as a coupled human-Resilience

Source of Support: NSF

Total Award Amount: $1,599,738 Total Award Period Covered:

Location of Project: EcoHealth Alliance and Malaysia

Person-Months Per Year Committed to the Cal: 0.50 Acad: Sumr:

Support: (J Current Kl Pending (J Submission Planned in Near Future —(_] *Transfer of Support

Project/Proposal Title:
Understanding Risk of Zoonotic Virus Emergence in EID Hotspots of South East Asia

Source of Support: NIH

Total Award Amount: $7,573,721.35 Total Award Period Covered: 3/1/2020 — 2/28/2025

Location of Project: EcoHealth Alliance and International Field and Lab Locations

Person-Months Per Year Committed to the Cal: 3.0 Acad: Sumr:

Support: W& Current UL Pending LJ Submission Planned in Near Future — LJ “Transfer of Support

Project/Proposal Title:
One Health Workforce — Next Generation

Source of Support: USAID

Total Award Amount: $5,000,000 Total Award Period Covered: 10/1/2019 - 9/30/2020

Location of Project: EcoHealth Alliance and International Field and Lab Locations

Person-Months Per Year Committed to the Cal: 1.44 Acad: Sumr:

Support: LJ Current LJ Pending ] Submission Planned in Near Future — LJ “Transfer of Support

Project/Proposal Title:
Strategies to Prevent (STOP) Spillover

Source of Support: USAID

Total Award Amount: $100,000,000 Total Award Period Covered: 8/31/2020 — 8/30/2025
Location of Project: EcoHealth Alliance and International Field and Lab Locations
Person-Months Per Year Committed to the Cal: 3.0 Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY


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Prime 1 (Fill-in) COST PROPOSAL

Sub 2 {Fill-in}
GENERAL INFORMATION
The purpose of the requested information in the attached worksheets is to assist government personnel in the review and evaluation of cost proposals submitted by offerors.

Options: Unpriced Options will not be accepted. Any Option that is not fully priced, will not be included in any resulting award.
Enter the proposed cost detail for the Base and each Option period {as needed) on the "Total Amount" tab.
+ The formulas in this spreadsheet are based upon common business practices; however, Offerors may edit formulas as necessary (edited formulas must still be visible).

«Ensure all costs from other worksheets are correct.
Below is a summary of the proposed cost. This chart will automatically fill in from the "Total Amount" tab.

Total Direct Labor Costs $286,937
Total Fringe Benefit Costs $105,593
Total Labor Overhead Costs so
Total Subcontract Costs so
Total Consultant Costs so
Total Other Direct Costs $22,660
Total Material Handling Costs $o
Subtotal Costs $420,190
Total G&A Costs $150,596
Subtotal Costs $570,787
Total Cost of Money 50
Total Estimated Costs $870,787
Fixed Fee (If proposing a CPFF contract) $0
Total Estimated Costs Plus Fixed Fee $570,787





HDTRAI-14-24-FRCWMD-BAA
CBEP = Thrust Area 6 — Cooperative Counter WMD Research with Global Partners

zoonotic virus, carried by bats, that causes acute encephalitis in domestic animals and people. Since
2001, more than 20 NiV outbreaks have been reported in Bangladesh and India, with a mean mortality
rate of 70% (100% in some outbreaks). Repeated spillover from bats; high mortality; and human-to-
human transmission signifies NiV’s importance as an emerging zoonotic pathogen with pandemic
potential. Kerala, India reported its first outbreak of Nipah virus encephalitis in May 2018, which
infected 23 people (case fatality rate = 91%). Pteropus medius bats are a reservoir for henipaviruses in
Bangladesh and India. Our group has shown that Nipah virus incidence in pteropid bats in Bangladesh is
low (~1%-4%). Viral infections are transient and periodic and spillover depends on coincident active
infection within a local bat population and a route of transmission. In Bangladesh, spillover happens
seasonally when people consume date palm sap that has been contaminated by bat excreta. The
mechanism of spillover in Kerala remains unknown, and despite evidence of NiV in bats in northern and
southern India, there is a lack of surveillance in people and livestock. Asa result, the extent to which
NiV contributes to AES in India is unknown,

Bats also carry filoviruses, which cause viral hemorrhagic fever. Several species of bats have been
associated with Ebola and Marburg viruses in Africa. The 2014 Ebola outbreak in West Africa was
hypothesized to have begun with a single spillover event from a bat, though which bat species remains
unknown, In Asia, outbreaks of viral hemorrhagic fever have not been associated with filoviruses;
however, antibodies against Reston ebolavirus, a non-pathogenic filovirus, were detected in pigs and
people in the Philippines in 2008. Our group identified Reston ebolavirus RNA and antibodies in local
Mineopterus schreibersii, a common insectivorous bat, suggesting that pig and human cases were due to
spillover from bats, There is now more evidence that various bats in Asia carry filoviruses, though the
diversity of those viruses and their threat to human health is unknown. Mengla virus is a new species of
filovirus discovered in Rousettus bats in China. It is different from Ebola and Marburg, but shares structural
and functional traits with them, suggesting that it can infect people. Prior to this discovery, we found
antibodies against unknown Ebola-like viruses in Rousettus feschenauiltii and Pteropus medius (the Nipah
reservoir) in Bangladesh — both of which are also common in India, suggesting that related filoviruses also
circulate in South Asia. Despite this, there is currently no surveillance for filoviruses in India.

SARS CoV emerged in live animal markets in southern China in 2002 and caused a severe respiratory and
gastrointestinal disease in people. It spread to 26 countries and infected more than 8,000 people, with
9% mortality. Middle East respiratory syndrome (MERS) CoV, related to SARS CoV, was first identified in a
person who died from severe respiratory disease in Saudi Arabia in 2012. Our group identified Chinese
horseshoe bats as the natural reservoir for SARS and SARS-like CoVs. Horseshoe bats are also common in
India and it is unknown whether SARS-like CoVs have contributed to human respiratory disease in India.

Scientific & threat reduction impact: The possible presence of bat reservoirs for zoonotic viruses and
lack of surveillance presents a situation where high consequence viral pathogens may emerge in human
populations undetected. This project will establish a surveillance platform that will help mitigate the
threat of zoonotic viral outbreaks in animals and people in india. The proposed project closely aligns
with the goals of the Cooperative Biological Threat Reduction Program. Improving coordination among
public health and animal health agencies; increasing capacity to detect viral zoonotic agents in animals
and people; and identifying high risk behaviors for spillover in communities where there is contact among
these groups, is necessary to adequately respond to outbreaks and reduce the threat to people’s health
and well-being. This project will provide important information about the risk of spillover of high
consequence zoonotic viruses to livestock and people where people and animals live in very close
association. This project will implement biosurveillance using a One Health framework, and capability
building through engagement with local and federal institutions. It will also support and complement



SHADED FIELDS ARE REQUIRED

Report Submitted to Defense Threat Reduction Agency
Grant/Award # HOTRAI-20-1-0029
Organization/Institution EcoHealth alliance
Project Title Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in Jordan & Strengthening Regional Disease Surveillance Capacity
Name of Submitting Official Dr. William &. Karesh
Title of Submitting Official Executive Vice President for Health and Policy
Email Address karesh@ecohealthalliance.org
Phone Number 212-380-4463
Submission Date 7/4/2021
Reporting Period End Date June 30th, 2021
Reporting Period Annual
Foreign Spending Country Foreign Spending ($)
Country 1 Jordan $ 201,514.72
Country 2
Country 3

Proceed to Personnel


Quarterly Report: HDTRA12010029 - Year 1, Quarter 3 (03/29/21 6/30/21)

Subtask 3.6 Datu Management

Incorporated the Arabic translations of the surveys into ODK.

o The project team developed field data sheets and input into the
AirTable data management software, and reviewed
accessibility and overall design for improvements in
conjunction with their review of the field and laboratory
protocols.

o The project team has created specific QR codes on ODK
Collect for each of the team members to access their own
survey to practice using the tablets for when sampling begins.

o For real-time quality control of data collection and entry, the — Figure 3. A mock example of
project team has coded and developed a series of automated — “he QR codes that each
reports using R Studio Connect to be in place for when data dividual team member
collection begins. The intention is that field and laboratory
staff serve an active role in data cleaning as

they can review data on a biweekly basis and 2 3
work with EHA data management staff to “ve "
identify any issues in real time 3 ne

o The ODK validation report has been
coded for the purpose of real-time
quality control for the surveys that are
delivered by field team members.
o The data management team developed a draft
project dashboard for full project progress
monitoring, to be shared among partners to 9 7777"
share team and project progress at-a-glance. 12
The dashboard ensures the full team can see ~—
project activities on all fronts laboratory,
surveys, human and animal sampling. The
dashboard differs from the automated sampling
reports, in that the sampling reports are highly
detailed accounts of each effort tailored to the
sub-team doing that sampling for data cleaning
purposes, while the dashboard summarizes the
full team effort.

Figure 4. An example of the dashboard
demonstrating how data will be summarized.

Subtask 3.7 Conduct presentations/meectings af times and places specified
Activities and results this quarter:
o Focal Point Meeting

Biological Threats Reduction Program Page 5 of 7


Nwani.
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Aleksei Chmura <chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org >

<Caution-mailto:chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org > > < Caution-
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Caution-mailto:chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org > > <

Caution-Caution-mailto:chmura@ecohealthalliance.org < Cantion-mailto:chmura@ecohealthalliance.org > <

Caution-Cantion-mailto:chmura@ecohealthalliance.org < Cantion-mailto:chmura@ecohealthalliance.org > > >>;

Zaution-mailto:andre(@ecohealthalliance.org >

<Caution-mailto:andre@ecohealthalliance.org < Caution-mailto-andre@ecohealthalliance org > > <Caution-

Alison Andre <andre@ecohealthalliance.org <

Caution-mailto:andre@ecohealthalliance.org < Caution-mailto:andre@ecohealthalliance.org > > < Caution-

Caution-Caution-mailto:andre(@ecohealthalliance.org < Caution-mailto:andre@ecohealthalliance.org > < Caution-

Caution-mailto:andre@ecohealthalliance.org < Cantion-mailto:andre@ecohealthalliance.org > > > >
Subject: Re: |Non-DoD Source] FRBAA 14-6-2-0454

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Akind hello,

1am jumping on this thread on behalf of EcoHealth Alliance. We expect to submit the clarifications by
Tuesday, August 25. I do hope this is agreeable.

Thank you,
Ava
Ava Sullivan

Project Manager and Research A

EcoHealth Alliance
520 Eighth Avenue — Suite 1200
New York, NY 10018

a)

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www ecohealthalliance.org < Caution-http://Caution-Caution-Caution-Caution-www.ecohealthalliance.org > > <

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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

2 Moura, J. A.. McManus, C. M., Bernal, F. E. M. & de Melo, C. B. An analysis of the
1978 African swine fever outbreak in Brazil and its eradication. Rev. Sci. Tech. 29, 549-
$63, doi:10.20506/rst.29.3.1992 (2010).

3 Delgado, J. et a/, U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing
Controls. Risk Anal. 37, 1768-1782, doi:10.1111/risa. 12704 (2017).

4 Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat.
Commun. 10, 898, doi:10.1038/s41467-019-08616-0 (2019).

5 Olival, K. J. et ai. Host and viral traits predict zoonotic spillover from mammals. Nature,
doi: 10.1038/nature22975 (2017).

6 Allen, T. e¢ af. Global hotspots and correlates of emerging zoonotic diseases. Nat.

Commun. 8, 1124, doi:10.1038/s41467-017-00923-8 (2017).

7 Mock, F., Viehweger, A., Barth, E. & Marz, M. VIDHOP, viral host prediction with
Deep Learning. Bioinformatics (2019).

8 Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod
vectors from evolutionary signatures in RNA virus genomes. Science 362, 577-580,
doi:10.1126/science.aap9072 (2018).

9 Gu, Y. COVID-19 Projections Using Machine Learning, <https://covid19-
projections.com/about#historical-performance> (2020).

Data Sources

Fitting and testing models will draw from disease outbreak data (counts, timing, and geography)
and predictor data including pathogen traits (disease symptoms, pathogen traits, modes of
transmission), pathogen sources (point of origin, geographic range of potential natural hosts),
environmental context (climatic conditions, ecosystem traits), and socioeconomic context (local
population densities, demographics, transportation networks, healthcare systems). Please see the
Technical Proposal for available data sources.

Tasks/Scientific Geals (Format: Year.Task.Subtask)
YEAR1
Task 1.1 — Develop and deploy project infrastructure
The awardee shall develop a computational environment that enables continuous model
development and testing and allows for research and models to be reproduced and extended by
external teams. The computational environment will include continuous maintenance and
documentation of project codebase.

Subtasks

1.1.1 Layout a provision internal project network

1.1.2 Set up continuous integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

Task 1.2 — Engineer project database
The awardee shall curate, integrate, cross-link, and validate relevant datasets from various
sources into a project database, accounting for differing space and time scales, data formats, and
data completeness.

Subtasks

1.2.1 Design database schema

1.2.2 Write pipelines to ingest data

1.2.3. Cross-link data sources via common ontology


©. The third day of the workshop (May
27", 2021) included training on
basic laboratory safety: biosafety
and biosecurity, facilitated by Dr.
Mustafa Ababneh, and cold chain
implementation, facilitated by Dr.
Borhan Al-Zghoul.

o Due to the COVID-19 group
limitation, 7 people attended
the third in-person
workshop:

= Dr. Mustafa
Ababneh (JUST)

«= Dr. Borhan Al-
Zghoul (JUST)

= Yasmin Issa
Daradkeh (graduate
student, JUST)

Dr. Omar Al-Omari, MD

Figure 7. in-person workshop on basic laboratory safety

Farah Al. Nabulsi (graduate student, JUST)
Abed Elrahman Talfha (undergraduate student, JUST)
Leen Al-Bayari (undergraduate student, JUST)

o March 29, 2021: Dr. Enhab Abu-Basha reviewed the diagnostic laboratories in Al-
Karak and Ma’an to determine the limitations and status of veterinary services in the

south of Jordan (the report was a follow up to previous visits in 2017 and 2019).
o Recommendations are as follows:

Complete laboratory building infrastructure with suitable benches,
sinks, and cabinets (Karak Lab).

Hire adequately trained veterinarians and laboratory technicians to
the laboratories.

Provide adequate training on safety, proper use, and maintenance of
laboratory equipment and chemicals.

Provide adequate amount of laboratory diagnostic chemicals,
supplies and kits.

MOA should recruit staff to Ma’an and provide incentives for

relocation.

Subtask 5.6 Data Management

o The project staff have finalized data
management tools and decided to use
ODK Connect servers to securely store
survey data and Airtable servers for
human and animal lab data. Only the
specific app users for each of the
servers can access the secure data.

o Training for the data management
systems was completed in the project
training workshop.

Sy ;
wal 2
|;

a

Collection Cleaning

Reports

aacep enn

stato
St Conse

Figure 8. Data management system, from collection to
cleaning and reporting results


SF 424 (R&R) apptication For FEDERAL ASSISTANCE Page 2

14. PROJECT DIRECTOR/PRINCIPAL INVESTIGATOR CONTACT INFORMATION

Last Name: [Ross | Suttix: [ ]

PositionTitle: [principal Scisatish, Sompahanional asssarca |

Organization Name: [ucotlealth ALiiance ]

Department, ] Division: | ]

Street: [yea west 34Lh Slreel ]
Street2

Suite L791 ]
City: [wey verk ] County / Parish: fay ]
State: HY: Hew verk ] Province: | ]
Country: { USA: UNITED STATES ] ZIP i Postal Code: [: oi: -2.3.7

Phone Number: [7175

] Fax Number: [2122864455 ]

Email: [ross#ecohea_tha=_iance.org ]

15. ESTIMATED PROJECT FUNDING. 16. IS APPLICATION SUBJECT TO REVIEW BY STATE EXECUTIVE ORDER
12372 PROCESS?

= a YES [[] THIS PREAPPLICATION/APPLICATION WAS MADE
a. Tolal Federal Funds Requested AVAILABLE TO THE STATE EXECUTIVE ORDER 12372
b. Total Non-Federal Funds PROCESS FOR REVIEW ON:

5.NO [7] PROGRAM IS NOT COVERED BY E.0. 12872; OR

PROGRAM HAS NOT BEEN SELECTED BY STATE FOR
REVIEW

c. Total Federal & Non-Federal Funds

4d. Estimated Program Income

17. By signing this application, | certify (1) to the statements contained in the list of certifications* and (2) that the statements herein are
true, complete and accurate to the best of my knowledge. | also provide the required assurances * and agree to comply with any resulting
terms if | accept an award. | am aware that any false, fictitious. or fraudulent statements or claims may subject me to criminal, civil, or
administrative penalties. (U.S. Code, Title 18, Section 1001)

QJ lagree

‘The fist of certifications and assurances, or an internet site where you may obtain this list, is contained in the announcement or agency specitic instructions.

18. SFLLL (Disclosure of Lobbying Activities) or other Explanatory Documentation
[ ] [ Add Attachment ] [ ] [ ]

19. Authorized Representative

Prefix: [pr ] First Name:

Shmara ] Suffix: [ ]

Last Name

Organization: Ecottealth Alliance ]

Department: [ Division: [

steott: [fey gen barn Siren

Street2:

City: [New vor County / Parish: [ny

State: | NY: New Yorr ] Province: [ ]

Country: OUU-2327

] ZIP / Postal Code:

Phone Number:

[pizssaaaca

Email: [orrarate

Signature of Authorized Representative Date Signed

Aleksei Chmura

20. Pre-application [ ] [Ada Attachment | l|

21, Cover Letter Attachment | ] [Add attachment | lI

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Grant Training DCP Matrix

1.1.2 Clinical Infection control and
animal health biosecurity

Understanding of basic infection prevention
and control procedures related to especially
high consequence pathogens (EHCPs) for
human and animal clinical settings in line with
GHSA and IHR milestones (when applicable),
including standard precautions (PPE use;
disinfection; sterilization); environmental
infection control; laboratory testing;
nosocomial infections, including AMR; and
animal health biosecurity principles,
procedures.)

2. Disease
identification,
detection,
confirmation, and
reporting

2.1 Positive identification of
emerging and high
consequence pathogens
(EHCP) and reporting to
appropriate authorities.

2.1.1 Sample collection

Knowledge, understanding and proficiency
with procedures for safely and securely
collecting appropriate specimens/samples for
testing of pathogens (human; animal;
environmental), especially EHCPs, and
handling and packaging of samples for
transport to diagnostic testing laboratories,
based on national and international guidelines
and best practices, while ensuring specimens
will are maintained in condition required for
testing (cold chain and or fixation, etc.)

2.1.2 Test procedures
(presumptive/confirmatory)

Knowledge, understanding and proficiency
with procedures for conducting screening,
diagnostic, and confirmatory testing and
interpreting test results for ECHPs from
different types of samples using different
types of tests (ELISA, PCR, etc.) for high
consequence pathogens, including test
accuracy, reliability, specificity, and sensitivity
and quality control/quality assurance (QA/QC)
measures needed to validate testing results.

2.1.3 Reporting

Knowledge, understanding and proficiency in



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross

Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

We will develop algorithms te predict the dynamics and impacts of outbreaks of new
infectious diseases, based on data available at carly stages of outbreaks. In doing so, we will
answer the following key questions.

e How much does partial-pooling improve predictions of epidemic impacts, and what
information transfers best across diseases?

e What variables are most predictive of epidemic impacts at early stages of an outbreak?

e At what point in epidemic growth do hybrid models that incorporate mechanistic
epidemiological components become more accurate than pure ML models?

Algorithmic Methods:

We will test the ability to ML methods incorporating partial pooling to predict impacts from a
new outbreak of disease. Our primary model structure will be built around Bayesian additive
regression trees (BARTs)'°. BARTs provide three features appropriate for this application: First,
they can provide both high-quality predictions as well as robustness in relatively data-poor
scenarios. Secondly, they have explicit uncertainty structures is better able to characterize error
than other regression tree methods that do not. Thirdly, they are able to incorporate hierarchical
partial pooling. In some scenarios where data are smooth rather than categorical, generalized
additive models (GAMs) can offer similar performance and similar features'!, and we will test
these as an alternate formulation.

We will parametrize the models using data on pathogen traits (disease symptoms, pathogen
traits, modes of transmission), pathogen sources (point of origin, geographic range of potential
natural hosts), environmental context (climatic conditions, ecosystem traits), and socioeconomic
context (local population densities, demographics, transportation networks, healthcare systems),
as well as time-series data of infections from the earliest phase of the outbreaks. Not all these
data are typically available at the early stages of a new bioagent outbreak, and are in many cases
incomplete even in the case of historical outbreaks. Thus, we will use multiple imputation” in all
models to allow for model fitting, prediction and assessment of uncertainty under incomplete
data.

We will explore multiple model structures, but all formulations will be fit and tested under three
conditions: within-disease forecasting where models are fit only on data from a single disease
and tested at ability to predict impacts from that disease, a//-disease forecasting where models
are fit on data from a// diseases and tested against ability to predict each disease, and partial-
pooled where models are fit all models from all diseases but parameter sharing across diseases
are partially pooled. (See ‘Model Evaluation and Validation’ below)

While ML approaches are best for predictive power, they do not have the flexibility to be used
for alternate scenarios or intervention evaluation that traditional compartmental models do. We
will compare "pure" ML algorithms to a hybrid approach using ML to generate parameters for
mechanistic epidemiological models. In this hybrid case, we will use the ML model to predict
prior parameters for compartmental epidemiological model using the same data as used in the

3


Grant Training DCP Matrix

7.1.3 Biohazard Incident
Management

Knowledge, understanding and proficiency
with development and implementation of
policies, procedures, and operational
guidelines, including emergency operation
communications systems, for Biohazard
Incident Management for EHCP outbreaks,
releases or exposures.

8. Laboratory
biosecurity

8.1 Prevent theft, misuse,
loss, or deliberate release of
emerging and high
consequence pathogens
(EHCP).

8.1.1 Biosecurity Risk Identification

Knowledge, understanding and proficiency
with principles and procedures for Biosecurity
Risk identification, risk assessments,
characterization and compliance with
recommended international standards and
best practices to prevent the loss, theft, or
misuse of biological organisms and
laboratory/research technologies.

8.1.2 Biosecurity Risk Mitigation

Knowledge, understanding and proficiency
with international best practices for
Biosecurity Risk Mitigation, and related risk
reduction approaches to enhance physical
security, personnel reliability, material control
and accountability, transport security, and
information security/cybersecurity, and the
development and implementation of
Biosecurity Risk Mitigation strategies for
laboratories, facilities, and personnel to
prevent the loss, theft, or misuse of biological
organisms, especially EHCPs, and
laboratory/research technologies.

8.1.3 Biosecurity Incident
Management

Knowledge, understanding and proficiency
with international best practices for
Biosecurity Incident Management, and
development and implementation of



Grant Training DCP Matrix

prevent the loss, theft, or misuse of biological
organisms, especially EHCPs, and
laboratory/research technologies.

8.1.3 Biosecurity Incident
Management

Knowledge, understanding and proficiency
with international best practices for
Biosecurity Incident Management, and
development and implementation of
Biosecurity incident management policies and
procedures for suspected or confirmed illicit
access to pathogens, intruder alerts, and other
biosecurity incidents.



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

trained on the same pathogen. For example, for Ebola outbreak predictions, all previous Ebola
outbreaks will be held out from training data.

Model Evaluation and Validation

Models will be fit and tested against their ability predict (a) size of an outbreak (total number of
cases), (b) peak size of outbreak (number of cases at highest point), (c) time to completion of an
outbreak, (d) time to peak of an outbreak, and (e) geographic spread of outbreak (distance to
farthest case from point of origin). Prediction scores will consist of mean-square-error of each of
these prediction against true values, as well as a score of equal-weighted errors of all five
measures. All models will also be tested for fidelity of uncertainty measures the fraction of true
data values that fall within prediction confidence intervals and these metrics will be reported
along with predictive performance.

All model performance scores will be compared against meaningful, “practical naive” baselines.
While it is common practice to use a “null” or “zero-knowledge” baseline, this often amounts to
inflating the apparent power of the algorithm. We will opt for a more realistic, “practical naive”
baseline, which instead follows what an analyst would approximate in practice. For these
models, we will use a practical baseline model of expecting outbreaks to follow the average size
and timing of outbreaks of the same disease in the past 25 years. All model scores will be
reported as percentage improvements over this baseline.

Algorithm performance will be measured against holdout data to validate true predictive
performance. Repeat testing on holdout data can lead to overfitting, with predictions too specific
to the hold-out set and not indicative of future performance.’ Yet increasing holdout reduces the
availability of training data. Thus, we will use a differential privacy approach drawn from
information security to validate prediction results. The hold-out data will be obfuscated, and
validation performance will be measured against bootstrapped replicates of validation data,
essentially introducing noise to reduce overfitting against the hold-out. This will ensure accurate
estimates of predictive performance in an operational environment.!°

Data Sources

Data sources for fitting and testing models include output data of number, timing, and geography
of disease outbreaks, and predictor data include pathogen traits (disease symptoms, pathogen
traits, modes of transmission), pathogen sources (point of origin, geographic range of potential
natural hosts), environmental context (climatic conditions, ecosystem traits), and socioeconomic
context (local population densities, demographics, transportation networks, healthcare systems).
Gathering predictor data is often as large a factor in the time required to create first-pass
predictions at the early stage of an outbreak. Thus, we will create automated pipelines for
gathering, merging, and updating these data sets, and to extract key predictors from basic
location and host variables to minimize manual inputs required. For instance, by linking to high-
resolution geospatial databases, we will automatically populate data for population density,


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

Task 2.3 — Hybrid ML-mechanistic model development and testing

In the option year, the awardee shall develop the hybrid machine-learning mechanistic model,
compare its performance to a pure ML model, and determine the switch-over time when the
mechanistic model becomes more predictive in the course of the outbreak.

Subtasks

2.4.1 Establish quantitative and qualitative metrics of model performance

2.4.2 Design mechanistic model structure

2.4.3 Determine mechanistic model prior forms

2.4.4 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.5Implement within-disease, all-disease, and partial-pooled disease model versions

2.4.6Measure variable contribution by re-fitting

2.4.7Analyze prediction-level variable contributions and model performance using criteria

developed in Task 1.3
2.4.8Compare Hybrid and ML performance along outbreak time-series progression
2.4.9Iteratively update models to improve performance

Task 2.4 —- Maintain and document project infrastructure
In the option year, the awardee shall document the methodology for extended database schema,
and data ingestion pipelines, QA/QC tests, extraction routes, and ML models established in Tasks
2.1-2.3.

Subtasks

2.4.1 Write and maintain initial codebase documentation

2.4.2 Perform code review, testing, and validation

OPTION TASK (OPTION II):

Task 3.1 — Develop Model Graphical User Interface
As an additional optional task, the awardee shall develop a graphical user interface (GUI) for the
ML model developed in task 1.3, allowing users to input of early-stage outbreak data and generate
predictions, uncertainty bounds, and interpretable explanations of influential variables.
Subtasks
3.1.1 Port model into portable framework usable for standalone or web-based interaction
3.1.2 Optimize for rapid response time and application size
3.1.3 Design input interface
3.1.4 Design visualizations for predictions, uncertainty, and variable importance

Task 3.2 — Maintain and document project infrastructure
In the option task, the awardee shall document usage of the GUI, and perform code review of
testing and validation of the GUI codebase created in tasks 3.1
Subtasks
3.2.1 Create GUI documentation
3.2.2 Perform code review, testing, and validation


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

e Pathogen Trait Data consists of information about pathogen taxa, transmission mode,
structural, and biological traits. Relevant traits for pathogens, and have been largely
aggregated for viruses by our Host-Pathogen Phylogeny Project® and parallel projects for
bacterial pathogens'* and non-zoonotic human viruses!®.

e Pathogen Genetic Data consist of genetic sequence of representative samples of
individual pathogen species. Representative sequences of nearly all relevant pathogens
are available via genetic repositories GenBank, ICTV, or GISAID. We expect pathogen
data at the scale of pathogen species to largely predict similar information as pathogen
traits, but genomic data may be faster to use in cases of rapid sequencing of novel
pathogens. We will test whether incorporating genetic data into model features via
neural-network autoencoders'’ provides improved performance over separate
representation of pathogen traits (e.g., taxonomic classification, transmission mode), or
other representations of genetic data such as phylogeny. Whether this performance is
additive, or whether these two types of information are redundant. Even if redundant,
genetic data may improve predictive capacity in the case of new pathogens where
pathogen traits have not been fully delineated. Rich inputs from genetic data will be
integrated using neural-network encoders.

¢ Source Data refers to intelligence about the origin of the disease, which includes location
and mode of origin (environmental, zoonotic host species, laboratory or accidental
release). In many cases source data is unknown, particularly in the early stages of a new
outbreak event. For zoonotic-origin diseases, the pool of known host species and their
geographic provide probabilistic information on the source. Our own Host-Pathogen
Phylogeny Project aggregates host traits for all known mammalian diseases hosts.>

«© Environmental Data refers to information describing the conditions in which the disease
may spread. These will include local climate variables from the BIOCLIM database,
land-use coverage (HYDE), and human and livestock population densities (World Bank,
FAO).

© Socioeconomic Data these include economic activity, governance, medical and veterinary
capacity (World Bank, FAQ), and transportation network connectivity in the region of
disease spread.

Programmatics
All work will be undertaken by the project prime, EcoHealth Alliance (EHA). EHA has a

proven track record of project management with U.S. government agencies including USAID,
DTRA, DHS, NIH, and NSF, ranging from global epidemiological research and surveillance
programs to small-to-medium scale technology development.

Our management approach is to bring production-quality software development practices to data
science for research and development. We use agile development methodology, with weekly
development meetings, monthly project status reviews, and regular organization-wide review
seminars to gather feedback from our broader interdisciplinary expertise. All tasks in the

q


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> wrote:

Hi Ava,

Thanks so much for your e-mail. We look forward to receiving the responses. Have a great day!

Very Respectfully,

6)

Contract Specialist

Defense Threat Reduction Agency

eye)

<Q ion-
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From: Ava Sullivan <sullivan@ecohealthalliance.org < Caution-mailto:sullivan@ecohealthalliance.org >
<Caution-mailto:sullivan@ecohealthalliance.org < Caution-mailto:sullivan(@ecohealthalliance. org > > < Caution-
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ullivan@ecohealthalliance.org < Caution-mailto:sullivan@ecohealthalliance.org > <

Caution-Caution-mailto
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Sent: Thursday, August 20, 2020 1:34 PM

TOKO)
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Ce: Dr. Jon Epstein <epstein@ecohealthalliance.org < Caution-mailto:epstein@ecohealthalliance.org >
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Riccardi <riccardi@ecohealthalliance.org < Caution-mailto:riccardi@ecohealthalliance.org >
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Proposal Number
Phase | Proposal Number
Topic

Proposal Title

Applicant Information

Applicant

Tax ID
DUNS
CAGE
Website

POC Name

Cost

Applicant Certification

Organization Type

FRBAAS-6-2-0502

Thrust Area &

Predicting Biothreat Impacts fram Early-Stage Data via Transfer Learning

EcsHealth Alliance Mail Address
341726494 City
OFr0S00K60000 State/Province
Zip
Country
Or Hoar Ross POC Email

Hon-Profit Organization

Are Human Subjects involved? No
Are Vertebrate Animals involved? No
Has a proposal for essentially No

equivalent work been submitted to

other US government agencies or

DoD components?
Agency 1
Agency 2

Agency 3

ContracvGrant No.

Contract/Grant No.

Contract/Grant No.

Are you a current DoD Contractor or No

Grantee?
Agency

Phone #

Point Of Contact

460 Vest 34th Street Suite
1701

Hew York
HY

10004 - 2317
USA

ross@ecohealthalliance org


Grant Training DCP Matrix

different types of samples using different
types of tests (ELISA, PCR, etc.) for high
consequence pathogens, including test
accuracy, reliability, specificity, and sensitivity
and quality control/quality assurance (QA/QC)
measures needed to validate testing results.

2.1.3 Reporting

Knowledge, understanding and proficiency in
policies and procedures for disease
notification and/or bi-directional reporting of
surveillance and laboratory data through
national information-sharing systems, or to
notify regional/international authorities such
as WHO/OIE/FAO of a disease, other
significant epidemiological event, or potential
public health emergency of international
concern (as appropriate to roles and
responsibilities at each operational level),

3. Analytics,
assessment, and
research

3,1 Appropriate study
design, data analysis, and
implementation of
processes, as applies to
each discipline,

3.1.1 Study design and ethics

Knowledge and understanding of scientific
methodologies and study design principles
(hypothesis development, research plan
development, sampling design, strategy for
data collection and analysis) and application of
research ethics to responsibly address safety,
security, and ethical issues related to the
research aims and hypotheses, including
understanding of issues around dual-use
research of concern.

3.1.2 Methodologies and analysis

Knowledge, understanding and proficiency in
the use of statistical and other relevant
methodologies for scientific research analyses
including practices and approaches for data
collection, use and interpretation of statistical
data analyses, data visualization, and use of





RESEARCH & RELATED BUDGET - Budget Period 2

OMB Number: 4040-0001
Expiration Date: 12/31/2022

ORGANIZATIONAL DUNS:

Enter name of Organization: [po jeaiet: Alliance

Budget Type: [XjProject [_] Subaward/Consortium Budget Period: 2

A. Senior/Key Person

Start Date:

End Date: |¢

Months Requested Fringe Funds
Prefix First Middle Last Suftix Base Salary (3) Cal. Acad. Sum. Salary ($) Benefits ($) Requested ($)
are Juoam l l 16,250.00] 3, G85 05 58, 916.03)
Project Role: levsen
| Ik | [so | 3 2 35,411.09]
Project Role: [senior Engineer
[ Jemma [eesaersozs | [ | 1 61,087.03]
Project Role: [ ]
| Total Funds requested for all Senior
Additional Senior Key Persons: Add Attachment | Key Persons inthe attachedfile [id
Total Senior/Key Person 368.00
B. Other Personnel
Number of | Months Requested Fringe Funds
Personnel Project Role Cal. Acad. Sum. Salary ($) Benefits {$) Requested ($)

Past Doctoral Associates

Graduate Students

Undergraduate Students

Secretarial/Clerical

Ld]
Ld
[
[|

JI JI |
II II |
|| || |
|| || |
II II |

[J Total number other Personnel

Total Salary, Wages and Fringe Benefits (A+B)

Tracking Number:GRANT13118386

Total other Personnel [___

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


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>> >>

Subject: [Non-DoD Source] Re: FRBAA14-6-2-0454
Importance: High

All active links contained in this email were disabled. Please verify the identity of the sender, and
confirm the authenticity of all links contained within the message prior to copying and pasting the address to a Web
browser.

Apologies for my slow reply. We are delighted to read your email!
Ihave cc’ed our Finance Manager Joe Riccardi on this email as well.

We will work through your requests this week and get responses back to you and Latrice as rapidly
as possible.

T hope this email finds you and yours safe and healthy most,
Sincerely!

~Aleksei

Aleksei Chmura
Chiet of Staff &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 < tel: 1.212.380.4469 > < tel:1.212.380.4469 <
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>>>

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EcoHealth Alliance
520 Eighth Avenue, Suite 1200.
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 < tel: 1.212.380.4469 > > (office)

pe > (mobile)

Caution-Caution-www ecohealthalliance.org < Caution-hitp./Caution-Caution-www.ecohealthalliance.org > <
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EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Sep 1, 2020, at 14:52, Ava Sullivan <sullivan@ecohealthalliance.org <

Caution-maill ullivan@ecohealthalliance.org > <Caution-mailto:sullivan@ecohealthalliance.org <

Caution-maill ullivan@ecohealthalliance.org > > > wrote:

Thank yor
We will su

full clarification by OOB tomorrow. We apologize for the late submission, we have run into
a few obstacles related to COVID, among other delays. Despite these delays, we will be able to submit very shortly
and look forward to sharing the information with this group.

Thanks,

Ava Sullivan

On Sep 1, 2020, at 2:30 PM, Lyles, Latrice D CTR DTRA AL (USA) {OV)

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Good Afternoon,
I will be picking up for Terrie.
I know it was mentioned that the clarification questions/documents would be submitted Friday August 28,

2020, 1 wanted to follow up on that and make sure we get the documents in so we can proceed with awarding this
grant. I hope to hear back from you soon. Thanks!

From: Ava Sullivan <sullivan@ecohealthalliance.org < Caution-mailto:sullivan@ecohealthalliance. org >

<Caution-mailto:sullivan@ecohealthalliance.org < Caution-mailto:sullivan(@ecohealthalliance.org > > >
Sent: Wednesday, August 26, 2020 9:15 AM

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Grant/Award #: HDTRA12010029
PI Name: Dr. William B. Karesh
Organization/Institution: EcoHealth Alliance
Project Title: Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and
Avian Influenza in Jordan & Strengthening Disease Surveillance Capacity
Goal number, Milestone Proposed completion
1, Implement animal study Y1-OY1
1, Conduct PCR and serology testing Y1-O0Y2
2, Implement human cohort study Y1-OY1
2, Conduct PCR and serology testing Y1-O0Y2
3, Implement behavioral risk survey Y1-OY1
3, Conduct epidemiological analyses Y1-O0Y2
4, Generate geospatial distribution and risk maps ¥1-OY2
5, Host workshops Y¥1-2, OY1-2
5, Submit reports and publications ¥1-OY2
5, Attend presentations/meetings ¥1-OY2
6, Conduct causal interference analyses Y3-OY2
6, Policy recommendations on modifiable risk Y3-OY2
factors
Project Goals

1, Determine the presence of MERS-CoY, Al. and other zoonoses among livestock and
poultry in Jordan

a. Description: Implement animal study (Y¥1-OY1); Conduct PCR and serology testing
{¥1-OY2)

b. Status of Effort: Approval for animal research has been obtained from the Jordan
University of Science and Technology (JUST} and Tufts University, and
surveillance determination has been obtained from ACURO. Obtaining these
approvals included achieving many milestones, including drafting and finalizing
procedures, and the animal owner consent to sample. The animal field and
laboratory teams are finalizing SOPs for data collection and testing.

2. Delermine whether (and if so. the extent to which} humans at human-livestock/poultry
interfaces display eviclence of infectian with these zoonoses

a. Description: Implement human cohort study (Y1-OY1); Conduct PCR and
serology testing (Y1-OY2)

b. Status of Effort: Approval for human subjects research has been obtained from
JUST (in-country approval) and Health Media Lab Research and Ethics (HML} IRB
{U.S.-based approval), and we are awaiting the determination for human subjects
research approval from HRPO. The data collection tools have been finalized
including the surveys, code of surveys into Open Data Kit (ODK) for use on tablets,
and the data management system in Airtable for laboratory data.

Biological Threats Reduction Program


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

This publicly releasable abstract is provided to DTRA for use in fulfillment of Section 8123 of the
Defense Appropriations Act and future versions of the same.

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

Project Summary

Accurately forecasting the impacts of new disease outbreaks is key to decision-making and
planning. However, at the early stage of outbreaks, little data is available to to make predictions.
Modern “Big Data” machine learning (ML) techniques generally require large data sets, reducing
their usability in these situations. Our proposed work addresses this challenge by using models
that can learn how much data from previous events is useful for making predictions about current
conditions. We will use this approach to understand what aspects of diseases, their hosts, or
environments can be used for prediction and how much they can contribute to making accurate

forec:


HDTRAI2010029
Page 8 of 10

b. SCOPE AND LIMITATIONS OF AUTHORITY FOR DTRA GRANTS OFFICER’S
REPRESENTATIVE

1. The Grants Officer’s Representative (GOR) is responsible for monitoring the technical and
programmatic performance of the grant or cooperative agreement (hereina(ter referred 1o as agreement)
including compliance with the agreement's reporting requirements. Perceived devialions from the
agreement's terms and conditions shall be brought to the attention of appropriate recipient personnel and if
not corrected, to the attention of the Grants Officer. Deviations or other occurrences indicative of the
recipient's inability or unwillingness to conform to the agreement's requirements shall be brought to the
immediate attention of the Grants Officer.

2. The GOR has no authority to modify the stated terms of the agreement or specifications in any manner,
or to approve any action that would result in additional charges to the Government. The DTRA Grants
Officer must make all such changes in writing.

3. The GOR is authorized to correspond directly with the recipient on matters within the scope and
limitations of his or her authority. All such correspondence shall be signed personally by the GOR and a
copy will be furnished the Grants Officer for placement in the official agreement file.

4. The GOR will provide technical advice to the recipient to the extent such advice will not alter any of
the agreement's terms and conditions or result in an increase in the estimated cost of the agreement's
performance as specified in the agreement. The GOR is encouraged to discuss with the recipient new ideas
related to the original scope of the agreement that may be of special interest to DTRA. The GOR should
also be alert for indications that any part of the research is becoming unfruitful. If, as a result of such

di ions, questions arise as to the possible need for a modification, the matter shall be brought to the
immediate attention of the Grants Officer. The GOR shall not direct the recipient to undertake any action
that the recipient believes to be contrary to the agreement's scope, terms or conditions without the written
approval of the Grants Officer.

5. Any visits to the recipient's (acililies on matters pertaining 1o the agreement will be documented in a
brief memorandum. A copy will be provided the Grants Officer for placement in the official agreement
file.

6. The GOR will maintain such work files as he or she deems necessary to properly document
performance of the duties and responsibilities as aGOR. Upon completion of the agreement and delivery
of all required reports, such work files will be forwarded to the Grants Officer for placement in the official
agreement file.

7. In performance of the GOR’s duties, the GOR shall constantly stress protection of the Government's
interests. Similarly, the GOR shall avoid any act which may tend to compromise the position of DTRA,
any individual member of DTRA or which will impact confidence in the integrity of DTRA with the
business community.

(End of Clause)

252.632-9000 GRANT FUNDING PROFILE (MAR 2012)


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Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data
cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location
information

1.2.6 Update data from live sources and clean and maintain data continuously

Task 1.3 — Machine Learning (ML) model development and testing
The awardee shall design, implement, test, and analyze performance of ML model structures and
iteratively improve them.
Subtasks
1.3.1 Establish quantitative and qualitative metrics of model performance
1.3.2 Implement “practical naive” model baseline
1.3.3 Design Bayesian additive regression tree (BART) model structure
1.3.4 Design alternative generalized additive model (GAM) structure
1.3.5 Implement within-disease, all-disease, and partial-pooled disease model versions
1.3.6 Measure variable contribution by re-fitting
1.3.7 Analyze prediction-level variable contributions and model performance
1.3.8 Iteratively update models to improve performance

Task 1.4 — Maintain and document project infrastructure

The awardee shall document the methodology for database schema, data ingestion pipelines,
QA/QC tests, extraction routes, and ML models established in Tasks |. 1-1.3.

Subtasks

1.4.1 Write and maintain initial codebase documentation

1.4.2 Perform code review, testing, and validation

OPTION YEAR (OPTION I):

Task 2.1 — Extend project database
In the option year, the awardee shall extend the database to include additional predictors and
continuously maintain and update to accumulate more data from upstream sources.
Subtasks
2.1.1 Extend database schema to include genetic data and auto-encoded variables
2.1.2 Write pipelines to ingest genetic data
2.1.3 Cross-link data sources via common ontologies
2.1.4Write QA/QC tests for data and perform data cleaning
2.1.5Update data from live sources and clean and maintain data continuously

Task 2.2 — Extend BART model
In the option year, the awardee shall extend the BART model to ingest genetic data and test the
contribution of this data in enhancing or recapitulating pathogen trait data.
Subtasks
2.2.1Develop genetic predictor variable set via neural-network autoencoding
2.2.2 Modify BART and GAM structures to incorporate genetic variables
2.2.3 Implement within-disease, all-disease, and partial-pooled disease model versions
2.2.4 Compare performance of genetic, pathogen-trait, and combined models


Ava Sullivan,
Project Manager and Research Assistant

EcoHealth Alliance

520 Eighth Avenue — Suite 1200

New York, NY 10018

607-280-7669 < tel:607-280-7669 < tel:607-280-7669 > < tel:607-280-7669 < tel:607-280-7669 > <tel:607-
280-7669 < tel:607-280-7609 > > > > (mobile} Caution-Caution-Caution-www.ecohealthalliance.org <
Caution-http:/Caution-Caution-Caution-www ecohealthalliance.org > <C;

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www ecohealthalliance org < Caution-http://Caution-Caution-Caution-www.ecohealthalliance.org > > < Caution-
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Caution-www ecohealthalliance.org/ > > < Caution-Caution-Caution-httpyiwww.ecohealthalliance.org < Caution-

Caution-Cantion-http:/www.ecohealthalliance.org > < caution-Caution-Caution-http.//www.ecohealthalliance.org
< caution-Caution-Caution-http/www.ecohealthalliance.org > > >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation


Opportunities for training and professional
development:

Training

The Jordan Training Workshop 1 provided an opportunity for the team to have in-depth
conversations about ethics in human subjects research with particular attention paid to informed
consent, recruitment, and safe data handling. Our project's strong focus on tailoring tools and
approaches to be reflective of our communities of interest and their protections ensures that team
members are fully prepared to work with communities in a manner that is respectful, robust, and
able to generate nuanced and important insights. This is an asset to this project, and to the team’s
future research projects.

In addition to the Jordan Training Workshop 1, all personnel who will be working with human
subjects data have completed CITI training in Social-Behavioral-Educational Research and
Biomedical Research.

Two graduate and two undergraduate students are being trained as part of this project, including:
« Yasmin Issa Daradkeh (graduate student, JUST)
e Farah Al Nabulsi (graduate student, JUST)
e Abed Elrahman Talfha (undergraduate student, JUST)
e Leen Al-Bayari (undergraduate student, JUST)

During the summer months (July through September), the students will have hands on training
for 10 weeks on molecular biology techniques: RNA and DNA extraction, RNA and DNA
quantification, cDNA synthesis, conventional and real-time PCR. They will also be trained on how
to use an Eliza for serology. Once field work begins, the students will also have the opportunity
to participate with the animal and human sampling.

Dissemination of results to communities of interest:

The team developed a 2-page project summary to share with stakeholders on project overview,
goals, and intended outputs. This document was shared with the Ministry of Agriculture, Ministry
of Health, Ministry of Environment, World Health Organization (WHO)-Jordan, Royal Scientific
Society (RSS), Jordan Army Forces (JAF), Food & Agriculture Organization of The United Nation
{FAQ}, and Badia Fund.

A focal point Steering Committee was established to regularly to update all partners about our
project and help in implementation of One Health Approach with WHO. Our partners on this
committee are the Ministry of Agriculture (MOA), Ministry of Health (MOH), Ministry of
Environment (MOE), World Health Organization (WHO), Food and Agriculture Organization
{FAQ}, Royal Scientific Society (RRS), and Badia Fund. Each organization has identified its’
representative and are now fully engaged in the project.

13



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

In the option year, the awardee shall develop the hybrid machine-learning mechanistic model,
compare its performance to a pure ML model, and determine the switch-over time when the
mechanistic model becomes more predictive in the course of the outbreak.

Subtasks

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Modify ML model to predict mechanistic parameters rather than outcomes

2.4.4 Implement within-disease, all-disease, and partial-pooled disease model versions

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze prediction-level variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 Iteratively update models to improve performance

Deliverables

Reports and Documents
¢ Monthly reports on task status
e Technical papers describing algorithm design and performance
e Conference presentations

Code Modules and Database Archives

All code modules and documentation will be continually available on EcoHealth Alliance’s
GitHub page with access provided to DTRA. Databases will be made available for download and
provided on-disk to DTRA per request.


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Original Message--—--
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< Caution-Caution-mailto:chmura@ecohealthalliance org < Caution-mailto:chmura@ecohealthalliance.org > > >

>>

Sent: Monday, August 10, 2020 9:23 AM

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o Focal Point Meeting

The Focal Point meeting took place
in Amman, Jordan on April 6, 2021.
Dr. Ehab Abu-Basha presented an
overview of the project. This event
provided an opportunity to clearly
establish the role of the Ministry of
Agriculture in the context of the
project and to discuss upcoming lab
visits in the south of Jordan. Follow-
up meetings will be conducted online
due to COVID-19 regulations.

o In attendance at the focal point
meeting: Figure 11, Focal point meeting in Jordan.
«Dr. Ehab Abu-Basha,
JUST
+ Dr. Zaidoun Hijazeen, JUST
«Dr. Yasmin Daradkeh, JUST
«Dr. Laura Sawalha, WHO
« Dr. Hussein Zyoud, Head of Animal Health Labs, MoA
« Dr. Essam Hawwa, CVO, MoA
* Colonel Oday Mrayat, Jordan Armed Forces

o Jordan Team Meeting
+ The Jordan in-country team held a meeting that discussed training and
workshops, the human questionnaire, and purchasing activities in
preparation for the beginning of data collection on May 10, 2021. The
Jordan team is purchasing lab and field materials and establishing and
reviewing standard operating procedure (SOPs) for animal and human
sampling, questionnaire administration, and laboratory procedures.

Future Subtasks

Subtask 5.3 Host annual partners and stakeholders meeting

Subtask 5.4 Complete annual report and share with partners
and stakeholders

Subtask 5.5 Conduct local-regional training workshops

o COVID-19 restrictions have limited in-person workshops, but as the restrictions begin
to ease, we are hopeful that we will be able to set a date for the local-regional training
workshop, with Human Link Lab at the National Research Centre, Egypt, in August.
The team looks forward to engaging with our regional stakeholder to further serve
the regional biosecurity and capacity-building objectives of the project.

11


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

o During the August 6" to 14" trip to Jordan, the project staff investigated the sites that were
randomly chosen for the project. They communicated and discussed the project’s
objectives with the people who lived/worked with the camels at four different interface
sites. These sites were located in the Hmeimeh and the Al-Dissi subdistricts in Quaira,
Aqaba, and the Tura subdistrict in Al-Ramtha, Irbid.

Figure 3 Meeting with the four families from a Figure 4. Discussing the project with the head of
camel interface site in Hmeimeh subdisirict in the household at a camel interface site in the Tura
Quaira, Aqaba. subdistrict in Al-Ramtha, Irbid.

Subtask 2.3 Conduct biological sampling
o Equipment purchased

o Field Vehicle: 2021 MGRX8 was delivered and is currently with the in-country
team.

o Lab supplies were purchased for the project. These supplies include PCR testing
kits, direct RNA sequencing kits, tubes and caps, pipettes, probes, and etc.

o The JUST team created a purchasing committee from three members that will
review different vendors for product quality and price, and recommend the
purchases of items accordingly. In addition, they will also follow up with products
once delivered to check specifications and sign the required receipt forms as per
JUST regulations.

Subtask 2.4 Conduct testing for AL MERS-CoF. brucellosis and leptospirosis
o During the trip to Jordan, project team members visited Jordan University of Science and
Technology (JUST), and visited the laboratories that the project’s lab personnel will be
using to conduct testing on samples for exposure to Al, MERS-CoV, Brucella Sp. and
Leptospira Sp.

Biological Threats Reduction Program 3


KVASU- DTRA INDIA BUDGET

‘OL tly 2018] 1July 2020 | Tuy 2021 | tly 2022 | 1 ily 2023
: Total
30June 2020 |/30June 2021] 30 une 2022|| 30June 2023 | 30 June 2024
MONTHS YEARI | YEAR? | VEAR3 _JOPTION YEAR I|OPTION YEAR 2| OF
fvi]¥2]vs[ovi[ov2| — @ASE [A SENIOR KEY PERSONNEL
Tafa fa 8 amb, preyt Laboratory Lead 30.00 30.00 30.00 30.00 $0.00 30.00
TOTAL SENIOR KEY PERSONNEL 30.00 50.00 50.00 $0.00 $0.00 $0.00
@. OTHER PERSONNEL
ia)aafaa] 12 [a2 |S 5,900 | Tao Research Assistant O2 35,000.00] $5,00000] 5.00000] $5,000.00] $5,000.00 $25,000.00
[TOTAL OTHER PERSONNEL $5,000.00] $5,000.00] $5,000.00] 55,000.00] $5,000.00] _ $25,000.00
TOTAL SALARY 35,000.00] $5,000.00] $5,000.00 $5,000.00 $5,000.00] $25,000.00
EQUIPMENT
30.00 30.00 30.00 30.00 $0.00 30.00
[TOTAL EQUIPMENT 50.00 $0.00 $0.00 $0.00 $0.00 $0.00
D.TRAVEL
T. Domestic 30.00, 35.00, 3000 30.00. 3000 30.00.
2-International 30.00) 30.00, 30.00 30.00 $0100 30.00
TOTAL TRAVEL 30.00 30.00 30.00 30.00 $0100 30.00
, PARTICIBANT/TRAINEE SUPPORT COSTS
1, Turton/Fees/Health Insurance 30.00 30.00 30.00 30.00 $0.00 30.00
2, Stipends $0.00 $0.00 $0.00 $0.00 $0.00 $0.00
3. Travel 50.00) 50.00) 50.00 50.00. $0.00 $0.00.
4. Subsistence 50.00, 50.00, 50.00 $0.00 $0.00 $0.00
5. Other 30.00 30.00, 30.00 30.00 $01.00 30.00
Total Partemant/Trainee Support Costs 30.00 30.00 30,00 $0.00 $0.00 30.00
F. OTHER DIRECT COSTS
1, Materials and Supplies szs30.02| $2.95002| $295002[ s295002| $2,950.02) $14,750.10
2, Publication Costs $0.00 $0.00 $0.00 50.00 $0.00 $0.00
5. Consultant Services 50.00) 50.00) 50.00 $0.00. $0.00 $0.00.
‘2 ADP /Computer Services 30.00) 30.00, 30.00 30.00 $0100 30.00
5, Subawards/Consortium/Contr actual Costs 30.00 30.00, 30.00 30.00 $0.00 30.00
. Equipment or Facility Renta/User Fres 30.00 30.00 30,00 30.00 $0.00 30.00
7. Alterations and Renovations 30.00 50.00 50,00 $0.00 $0.00 $0.00
8. 2-Day Annual Meeting Costs $2,900.00 $2,000.00] $2,000.00 $2,000.00| $2,000.00 $10,000.00
Total Other Direct Costs s4.950.02| 5495002] $4950.02] 54,950.02] $4,950.02[ $24,750.10
[G. DIRECT & MODIFIED DIRECT COSTS
Direct Costs 3 aasou2 [§ 998002 [$ sso |s sas002 |S 935002 [5 49,750.10
Modified Direct Comte 3 a.950.02 [$ 9.95002 [5 9,950.02 |§ 9.95002 |S 9,950.02 [5 49,750.10
H. INDIRECT COSTS
[Be Rae| 10.00%, 399500 [5 935.00 [5 soso |S sas00 |S _sos00 |S _4975.02
Total nairect Costs $995.00 [5 935.00 [5995.00 |s 995.00 [s 995.00 [5 __4.975.01
TOTAL DIRECT AND INDIRECT COSTS,
Direct + indeect Stoanson [5 109%5.07 [6 wassa2 |S s9SISo2 |S 1094502 |S 5472511
FEES
0.00% SRA FS eC 3 [5 zi
i TOTAL COSTS AND FEES
Total Costs $ 10,945.62 [5 10,985.02 [5 wosaso2 |§ 1094502 [$ 1094502 |S 5472511



Quarterly Report: HDTRA 12010029 - Year 1, Quarter 1 (9/29/20 - 12/28/20)

Future Tasks
Subtask 2.3 Conduct biological sampling
Subtask 2.4 Conduct testing for Al, MERS-Co¥, brucellosis und leptospirosis

Task 3: Characterize causal factors in_animal-to-human transmission of these zoonoses.

Subtask 3.4 Obtain local permissions and approvals to work with human subjects

Activities and results this quarter:

o Compiled materials for local JUST and HML IRBs. The Jordanian team and EHA behavioral
risk scientists collaboratively built and finalized the human questionnaire and site
characterization form.

Subtask 3.3 Analyze epi causal inference to identify modifiable visk factors

Activities and results this quarter:

o Project staff reviewed data analysis plan using causal inference framework to identify all data
needed for the models that will be produced to test study hypotheses. All proposed model
variables were mapped onto specific questions to be asked in the questionnaire. The
questionnaire was then arranged according to logical conversational flow in light of how it
will be administered.

o Examined socio-economic factors that could affect vulnerability to both risks and impacts,
including household wealth indicators used in prior surveys (e.g. for comparability with
World Bank reports). These will be relevant for economic context and policy
recommendations for biothreat reduction.

© Project staff investigated the literature for risk factors and confounders associated with
leptospirosis, brucellosis, Al, and MERS-COV.

Future Subtask(s)
Subtask 3.2 Conduct behavioral risk factor surveys in people

Task 4: Produce geospatial maps of zoonoses in livestock and poultry and of human zoonotic
Wransmission risk.

Subtask 4.1 Generate maps of laboratory results

Activities and results this quarter:

o The project staff identified the workflow and mock-ups for generation of these maps for
quarterly reporting.

Subtask 4.2 Generate risk maps with human behavioral risk factors

Activities and results this quarter:

o The project staff identified which exposures would be best to map for inclusion in the
questionnaire.

Task 5. Strengthen local capacity for detection and reporting of MIERS-CoV, Al, and other
zoonoses in Jordan, Irag, and Lebanon

Subtask 3.1 Conduct project kick-off meeting in Amman with local stakeholders
Activities and results this quarter:

Biological Threats Reduction Program Page 3 of 6


SF 424 (R&R) apptication For FEDERAL ASSISTANCE Page 2

14. PROJECT DIRECTOR/PRINCIPAL INVESTIGATOR CONTACT INFORMATION

Last Name: [Ross | Suttix: [ ]

PositionTitle: [principal Scisatish, Sompahanional asssarca |

Organization Name: [ucotlealth ALiiance ]

Department, ] Division: | ]

Street: [yea west 34Lh Slreel ]
Street2

Suite L791 ]
City: [wey verk ] County / Parish: fay ]
State: HY: Hew verk ] Province: | ]
Country: { USA: UNITED STATES ] ZIP i Postal Code: [: oi: -2.3.7

Phone Number: [7175

] Fax Number: [2122864455 ]

Email: [ross#ecohea_tha=_iance.org ]

15. ESTIMATED PROJECT FUNDING. 16. IS APPLICATION SUBJECT TO REVIEW BY STATE EXECUTIVE ORDER
12372 PROCESS?

= a YES [[] THIS PREAPPLICATION/APPLICATION WAS MADE
a. Tolal Federal Funds Requested AVAILABLE TO THE STATE EXECUTIVE ORDER 12372
b. Total Non-Federal Funds PROCESS FOR REVIEW ON:

5.NO [7] PROGRAM IS NOT COVERED BY E.0. 12872; OR

PROGRAM HAS NOT BEEN SELECTED BY STATE FOR
REVIEW

c. Total Federal & Non-Federal Funds

4d. Estimated Program Income

17. By signing this application, | certify (1) to the statements contained in the list of certifications* and (2) that the statements herein are
true, complete and accurate to the best of my knowledge. | also provide the required assurances * and agree to comply with any resulting
terms if | accept an award. | am aware that any false, fictitious. or fraudulent statements or claims may subject me to criminal, civil, or
administrative penalties. (U.S. Code, Title 18, Section 1001)

QJ lagree

‘The fist of certifications and assurances, or an internet site where you may obtain this list, is contained in the announcement or agency specitic instructions.

18. SFLLL (Disclosure of Lobbying Activities) or other Explanatory Documentation
[ ] [ Add Attachment ] [ ] [ ]

19. Authorized Representative

Prefix: [pr ] First Name:

Shmara ] Suffix: [ ]

Last Name

Organization: Ecottealth Alliance ]

Department: [ Division: [

steott: [fey gen barn Siren

Street2:

City: [New vor County / Parish: [ny

State: | NY: New Yorr ] Province: [ ]

Country: OUU-2327

] ZIP / Postal Code:

Phone Number:

[pizssaaaca

Email: [orrarate

Signature of Authorized Representative Date Signed

Aleksei Chmura

20. Pre-application [ ] [Ada Attachment | l|

21, Cover Letter Attachment | ] [Add attachment | lI

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

Subtask 4.1 Generate maps of laboratory results Geospatial Maps of Zoonoses in@=@
ae . Livestock, Poultry. and Fumans n Jordan
Activities and results this quarter: a
o Updated the original geospatial map with mock data that
is connected to the project’s AirTable to create updated
automated reports to be sent to our project partners and
stakeholders.

Figure 7. Updated
Future Subtask(s): geospatial map based
on laboratory results
of humans, livestock,
and poultry positive for
the four pathogens.

Subtask 4.2 Generate risk maps

wih fuaman behavioral risk factors

‘Task trengthen local capacity for
detection and reporting of VIERS-CoV,
Al, and other zoonoses in Jordan, Iraq, and Lebanon

Subtask 5.2 train local project staff in proper techniques

o The JUST graduate and undergraduate students
working on the project train for 8 weeks in PCR
techniques at the Molecular and Biology
Laboratory, JUST. After training, the students may
come in on their own time to practice.

o September 14, 2021, the human studies field team,
graduate and undergraduate students gathered for
training on how to use the tablets and give the

questionnaire to the participants. Figure 8. Dr. Basil demonstrates
© The project staff team and the students have created "0 fo use the tablets and
a “WhatsApp” group chat for communication. questionnaires to the human field
team, graduate, and
Subtask 3.3 Conduct local-regional training workshops undergraduate students,

Activities and results this quarter:

o On August 10 & 11", 2021, EHA project staff travelled to Amman, Jordan, to lead a two-
day Survey Design and Risk Communications Workshop at the Grand Hyatt Amman. The
workshop was a highly interactive event in which project team leaders facilitated
presentations, discussions, and workbook activities designed to build skills around question
and survey design and risk communications strategies. The workshop additionally included
training on key tenets of ethical research with human populations. Including the project
team, 48 participants attended the first day, and 37 participants attended the second day. In
keeping with the One Health foundations of the project, an important component of this
workshop was having a group of participants who would be representative of a diverse
cross-section of government ministries and national One Health stakeholders from the
public and private sector. As the participant list below illustrates, this was successfully
achieved. Our esteemed participants received certificates at the close of the workshop, and
the project team received positive feedback and suggestions for future trainings.

Biological Threats Reduction Program 5


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

4 Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat.
Commun. 10, 898, doi: 10.1038/s41467-019-08616-0 (2019).

5 Olival, K. J. ef a. Host and viral traits predict zoonotic spillover from mammals. Nature,
doi: 10.1038/nature22975 (2017).
6 Allen, T. e¢ a/. Global hotspots and correlates of emerging zoonotic diseases. Nat.

Commun. 8, 1124, doi:10.1038/s41467-017-00923-8 (2017).

7 Mock, F., Viehweger, A., Barth, E. & Marz, M. VIDHOP, viral host prediction with
Deep Learning. Bioinformatics (2019).

8 Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod
vectors from evolutionary signatures in RNA virus genomes. Science 362, 577-580,
doi: 10.1126/science.aap9072 (2018).

9 Gu, Y. COVID-19 Projections Using Machine Learning, <https://covid19-
projections.com/about#historical-performance> (2020).

Data Sources

Fitting and testing models will draw from disease outbreak data (counts, timing, and geography)
and predictor data including pathogen traits (disease symptoms, pathogen traits, modes of
transmission), pathogen sources (point of origin, geographic range of potential natural hosts),
environmental context (climatic conditions, ecosystem traits), and socioeconomic context (local
population densities, demographics, transportation networks, healthcare systems). Please see the
Technical Proposal for available data sources.

Tasks/Scientific Goals (Format: Year. Task.Subtask)
YEAR 1
Task 1.1 — Develop and deploy project infrastructure
The awardee shall develop a computational environment that enables continuous model
development and testing and allows for research and models to be reproduced and extended by
external teams. The computational environment will include continuous maintenance and
documentation of project codebase.

Subtasks

1.1.1 Layout a provision project network

1.1.2 Set up continuous integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation

1.1.6 Perform code review, testing and validation

Task 1.2 — Engineer project database
The awardee shall curate, integrate, cross-link, and validate relevant datasets from various
sources into a project database, accounting for differing space and time scales, data formats, and
data completeness.

Subtasks

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.3 Cross-link data sources via common ontologies


Objective: Test new ML approaches - transfer learning by

partial pooling, and hybrid mechanistic-ML models, for

predicting impacts of novel biothreats at early, data-sparse

stage of outbreak.

Method:

- Quantify gain in prediction by training models to learn
information across diseases (partial pooling)

- Assess variables most predictive of epidemic impacts at
early stages outbreak.

- Quantify gain in power from using model to predict
parameters of mechanistic compartmental model.

- Determine point in epidemic growth where hybrid models
become more accurate than pure ML models.

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Dr. Noam Ross, EcoHealth Alliance, HDTRA1-14-24c-FRCWMD-BAA

Data from Pathogen, Source, Current

i i Outbreak Hybrid
previous Environmental & Curbreak | __fHvbrid._.
outbreaks Socioeconomic Data ¥! Approach H

Hierarchical Partial
Pooling H

Tree/Spline ML Algorithm|-- 22"...
Approach :

Benefits of Technology: Disease forecasts in early stages

of outbreaks, especially novel outbreaks, require strong

assumptions in highly structured models. Hierarchical partial

pooling learns what factors from previous outbreaks may be

transferred to apply to new predictions, improving early-stage

assessments of severity and risk.

Challenges:

- Missing data in early-stage outbreak conditions - Learn
most important variables to prioritize data collection

- ML models non-interpretable and poor at extrapolation >
Use hybrid approach with ML parameterizing structured
mechanistic model capable of simulating scenarios

Readiness: TRL1

| rs Se :
! Mechanistic =! H
---4 Epidemiological -«-----
1 Model '
Milestones:

- ¥1Q2: Engineer project database & test infrastructure

- ¥1Q4: Build, fit, and compare ML model structures to

- OY1Q2: Extend models to incorporate genetic data

- OY1Q4: Compare pure ML models to ML-mechanistic hybrids

Funding Profile:
$246,211 Year 1, $248,402 Option Year

Contact information:
Noam Ross, Senior Research Scientist,
ross@ecohealthalliance.org, 1.212.380.4471



Revised 14 September 2020 EXHIBIT B

research facility is responsible for notifying the DoD sponsor if Association for the
A ment and Accreditation of Laboratory Animal Care accreditation is lost or the
facility is under USDA inspection. DoD also has the right to a site inspection under
these circumstances.

The grant recipient (including subcontractors) is expressly forbidden to use laboratory
animals in any manner whatsoever without the express written approval of MRMC
ACURO.

The grant recipient shall complete the ACURO Animal Use Appendix for Research
Involving Animals found at the following web site:
http://mrme.amedd.army.mil/index.cfm?pageid=research_protections.acuro_animalap
pendix. Submit the completed ACURO appendix, contact information, the DTRA
grant number and a copy of the grant for processing to the email address listed at the
ACURO website. Once ACURO approves the effort, the grant recipient will receive
written approval to begin animal use from the US Army MRMC ACURO by separate
email. The grant recipient shall promptly provide a copy of the approval to the
Grants Officer and Grants Officer representative. After approval, changes or protocol
amendments must be submitted to and approved by ACURO before implementation.

The grant recipient, or subcontractors as appropriate, shall submit the most recent
U.S. Department of Agriculture Animal Care Inspection Report annually in
accordance with instructions provided.

Non-compliance with any provision of this clause may result in termination of the
grant.

DoD Instruction 3216.01, dated September 13, 2010, provides policy and
requirements for the use of animals in DoD-funded research based on Army
Regulation 40-33. The DoD definition of animal is any live nonhuman vertebrate.
All proposals that involve the use of animals must be in compliance with DoD
Instruction 3216.01 and AR 40-33. DTRA requires that research using animals not
begin or continue until the ACURO has reviewed and approved the proposed animal
use. For animals, the provisions include rules on animal acquisition, transport, care,
handling, and use in: (i) 9 CFR parts 1-4, Department of Agriculture rules that
implement the Laboratory Animal Welfare Action of 1966 (U.S.C. 2131-2156); and
(ii) the “Guide for the Care and Use of Laboratory Animals,” National Institutes of
Health Publication No. 86-23

RESEARCH INVOLVING RECOMBINANT DNA MOLECULES. Any recipient
performing research involving recombinant DNA molecules and/or organisms and viruses
containing recombinant DNA molecules agrees by acceptance of this award to comply with
the National Institutes of Health “Guidelines for Research Involving Recombinant DNA
Molecules,” July 5, 1994 (59 FR34496) amended August 5, 1994 (59 FR40170) amended
April 27, 1995 (60 FR 20726), or such later revision of those guidelines as may be published
in the Federal Register.

COMBATING TRAFFICKING IN PERSONS. The recipient agrees to comply with the
trafficking in persons requirement in Section 106(g) of the Trafficking Victims Protection
Act of 2000 (TVPA), as amended (22 U.S.C. 7104(g)) as implemented by 2 CFR 175.

Page 10 of 14


o Incorporated the Arabic version of surveys to
ODK.

o The project team developed field data sheets
and input into the Airtable data management
software, and reviewed accessibility and overall
design for improvements in conjunction with
their review of the field and laboratory protocols.

o The project team has created specific QR codes
on ODK Collect for each of the team members
to access their own survey to practice using the
tablets for when sampling begins. ai A

o For real-time quality control of data collection Figure 9. A mock example of the QR
and entry, the project team has begun coding codes that each team member received,
and developing a series of automated reports
using R Studio Connect to be in place for when data collection begins. The intention
is that field and laboratory staff serve an active role in data cleaning as they can
review data on a biweekly basis and work with EHA data management staff to identify
any issues in real time.

o The data management team has developed a
draft project dashboard for full project
progress, to be shared amongst partners to 3
share team and project progress at-a-glance.

The dashboard ensures the full team can see

the project on all fronts — laboratory, surveys, .
and human and animal sampling. The
dashboard differs from the automated
sampling reports, in that the sampling reports
are highly detailed accounts of each effort
tailored to the sub-team doing that sampling
for data cleaning purposes, while the
dashboard summarizes the full team effort. a

2 3

R

; Figure 10. Project Dashboard with mock data
Subtask 5.7 Conduct presentations/meetings at times

and places specified

© Project kick off for HDTRA1-20-1-0029 — Jordan was held on January 13", 2021.
The kick-off meeting was a success and the team had a positive collaboration
with DTRA and Jordan partners.

o Project staff also attended a meeting on March 7" at Jordan Armed Forces GHQ
(JAF) with LTC. Mohammad Malkawi, Arms Control & International
Organizations branch, Directorate of Planning & Organization (DPO), JAF
followed-up with letters to the Ministry of Agriculture, Ministry of Health, and
Ministry of Environment to form a Focal point committee to the project. Letters
were also sent to RSS, Badia Fund, WHO, FAO, USAID, AOAD, and RVC to
join the committee. This committee will be updated as part of our projects team
ensuring a One Health approach is successfully employed.

10


Objective: Test new ML approaches - transfer learning by

partial pooling, and hybrid mechanistic-ML models, for

predicting impacts of novel biothreats at early, data-sparse

stage of outbreak.

Method:

- Quantify gain in prediction by training models to learn
information across diseases (partial pooling)

- Assess variables most predictive of epidemic impacts at
early stages outbreak.

- Quantify gain in power from using model to predict
parameters of mechanistic compartmental model.

- Determine point in epidemic growth where hybrid models
become more accurate than pure ML models.

Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
Dr. Noam Ross, EcoHealth Alliance, HDTRA1-14-24c-FRCWMD-BAA

Data from Pathogen, Source, Current

i i Outbreak Hybrid
previous Environmental & Curbreak | __fHvbrid._.
outbreaks Socioeconomic Data ¥! Approach H

Hierarchical Partial
Pooling H

Tree/Spline ML Algorithm|-- 22"...
Approach :

Benefits of Technology: Disease forecasts in early stages

of outbreaks, especially novel outbreaks, require strong

assumptions in highly structured models. Hierarchical partial

pooling learns what factors from previous outbreaks may be

transferred to apply to new predictions, improving early-stage

assessments of severity and risk.

Challenges:

- Missing data in early-stage outbreak conditions - Learn
most important variables to prioritize data collection

- ML models non-interpretable and poor at extrapolation >
Use hybrid approach with ML parameterizing structured
mechanistic model capable of simulating scenarios

Readiness: TRL1

| rs Se :
! Mechanistic =! H
---4 Epidemiological -«-----
1 Model '
Milestones:

- ¥1Q2: Engineer project database & test infrastructure

- ¥1Q4: Build, fit, and compare ML model structures to

- OY1Q2: Extend models to incorporate genetic data

- OY1Q4: Compare pure ML models to ML-mechanistic hybrids

Funding Profile:
$246,211 Year 1, $248,402 Option Year

Contact information:
Noam Ross, Senior Research Scientist,
ross@ecohealthalliance.org, 1.212.380.4471



Revised 14 September 2020 EXHIBIT B

3) Prior approval is not required to transfer amounts budgeted for indirect costs to absorb
increases in direct costs, or vice versa.

4) Prior approval is not required to carry forward an unobligated balance to a subsequent
period of performance under this award.

6. Payments.

The 2 CFR 200 governs responsibilities concerning payments, with the following
clarifications:

Recipients shall submit requests for payment using Invoicing, Receipt, Acceptance, and
Property Transfer GRAPT) at https://wawfeb.mil/. Any request for advance payments must
be approved by the Administrative Grants Office shown in Block 6 of the award. The
request shall be submitted to the Administrative Office identified in Block 6 of the Research
Grant by entering the following routing codes:

1) Pay Office DoDAAC: See Block 12 (Code) on the first page of the Grant.
2) Invoice Type: Non Procurement Instrument (NPI) Voucher.

3) Issue By DoDAAC: See Block 5 (Code) on the first page of the Grant.

4) Admin DoDAAC: See Block 6 (Code) on the first page of the Grant.

5) Grant Approver: Same as Admin DoDAAC (Leave Ext. blank).

Payments will be made by the Defense Finance and Accounting Service (DFAS) office
specified in the Research Grant (Block 12).

A foreign awardee must have a U.S. bank account and be signed up for electronic payments
(electronic funds transfers (EFT)).

7. Funding Increments and/or Options.

The recipient is advised that the Grantor's obligation to provide funding for increments
and/or options included in the Grant is contingent upon satisfactory performance in the
judgment of the DTRA Scientific Officer/Technical Monitor and the availability of funds.
Other factors will be considered before options will be exercised (for example, expenditure
rate and current programmatic objectives). Accordingly, no legal liability on the part of the
Grantor exists unless or until funds are made available to the Grantor and notice of such
availability is confirmed in writing to the recipient. Refer to the Funding Profile in Section G
of the Grant for additional incremental funding planned, but not currently obligated for the
Grant.

Funding Increments — In no event is the Government obligated to reimburse the recipient for
expenditures in excess of the total funds allotted by the Government to this agreement.
Recipients should note that low expenditure rates reported on payment requests may be cause
for deferral of future increments. The Government anticipates unilateral modifications for
funding increments.

Options — If the agreement contains Option(s), the Government reserves the right to exercise
the Option(s) unilaterally.

Page 3 of 14


require transport to the airport $150 RT is included. In total, we are requesting $1,576 in Y1 and
$5,996 in OY 1 for domestic travel

International Travel

One international trip for two team members to present at the EEID conference held in
Montpelier, France, in 2021 requested for Y1. Flights are estimated at $1500 and the federal per
diem rates of $196 for lodging and $155 for meals in Montpelier. $25 per day is estimated in taxi
costs plus $150 RT trips to airport is included. In total we are requesting $7,088 in YL.

E. Participant/Traince Support Costs
No funds are requested for participant or trainee support costs.

F. Other Direct Costs

Materials and Supplies

PI Ross and Research Scientist Mendelsohn will both need a new computer, equipped with
Microsoft software for which we request a total of $4,000, in only Y1.

Cloud Computing and Storage Services

We will make use of cloud computer services including spot server rental for compute-intensive
jobs, data storage, continuous integration and testing of computer code. We request $2,000 per
year in Y1-OY1.

Team Communication and Collaboration Software and Services
We request $1,000 per year for subscriptions for software and services for document sharing,
chat, voice, and video communications.

Publication Costs
To facilitate the dissemination of project findings, $1,500 per year in only Y1-OYI is requested
for open access publication costs in international peer-reviewed journals.

H. Indirect Costs

We are requesting the EcoHealth Alliance federally approved indirect cost rate of 35.84% on all
applicable direct costs. The USA Department of Defense’s Department of the Navy has approved
this rate on 01 April 2020. We request S $64,986.52 in Y1 and $65,538.46 in OY1.


From: Aleksei Chmura

To: exe)

cc Noam Ross:(by(6)
boxe) | Joe Riccardi

Subject: [Non-DoD Source] Re: Notification of DTRA Grant Award -- HDTRA12110023 -- "Predicting Biothreat Impacts
from Early-Stage Data via Transfer Learning

Date: Monday, July 19, 2021 4:32:15 PM

All active links contained in this email were dis

bbled. Plcase verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

1 confirm receipt and we are delighted to revive this Grant award!

Please note our address may need to be updated, since it now is:
LcoHcalth Alliance

520 Kighth Avenue

Suite 1200

New York, NY 10018

‘Thank you!

Alcksci Chmoura, PhD
Chief of Staff

EcoHealth Alliance
520 Kighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 > (office)
Po } (mobile)
Caution-www.ccohcalthalhance.org < Caution-http:!/wwsw.ccohealthalliance.org >

LcoHcalth Alliance develops science-based solutions to prevent pandemics and promote conservation.

On Jul 19, 2021, at 15:56 [08
Caution{b)6) > wrote:

Dr. Ross and Dr. Chmura,

Congratulations! Attached for your records
questions about the attached document. FYSA - al

the subject Grant award. Please let me know if you have any
ce’ed to this email is the GOR for this cffort. Dr. Batni.

Thank you,


>; Joe Riccardi <riccardi@ecohealthalliance.org < Caution-mailto:riccardi@ecohealthalliance.org >
rdi@ecohealthalliance.org > > < Caution-

<Caution-mailto:riccardi@ecohealthalliance.org < Caution-mailtos
ardi(@ecohealthalliance.org < Caution-mailto:riccardi(@ecohealthalliance.org > > < Caution-
Caution-Cantion-mailto rdi(@ecohealthalliance. org < Caution-mailto:riccardi(@ecohealthalliance.org > <
Caution-Cantion-mailto:riccardi(@ecohealthalliance.org < Caution-mailto:riccardi@ecohealthalliance.org > > > <
Caution-Cantion-Caution-Caution-mailto:riccardi@ecohealthalliance.org <

rdi(@ecohealthalliance.org > < Caution-Caution-mailto:riccardi@ecohealthalliance.org <
rdi(@ecohealthalliance.org > > < Caution-Caution-

rdi(@ecohealthalliance.org < Caution-mailto:riccardi(@ecohealthalliance.org > < Caution-

adi @ecohealthalliance org < Caution-mailto:riccardi@ecohealthalliance org > > >_> >;

Caution-mailto:

Caution-mailto:ri

Caution-maill

Caution-maill

Caution-maill

a)

Subject: Re: |Non-DoD Source] FRBAA 14-6-2-0454

All active links contained in this email were disabled. Please verify the identity of the sender, and
confirm the authenticity of all links contained within the message prior to copying and pasting the address to a Web
browser.

DearfPX6)

I confirm receipt. Thank you for this notice and the additional clarification requests. We are working
through the requested details and will get them hack to as swiftly as possible.

Many thanks!

-Aleksei

Aleksei Chmura
Chief o tt &
Authorized Organizational Representative

EcoHealth Alliance
520 Eighth Avenue, Suite 1200
New York, NY 10018-4182

+1.212.380.4473 < tel: 1.212.380.4469 < tel: 1.212.380.4469 > < tel:1.212.380.4469 <
tel: 1.212.380.4469 > > <tel:|.212.380.4469 < tel: 1.212.380.4469 > <tel:1.212.380.4469 < tel:1.212.380.4469 > >
< tel: 1.212.380.4469 < tel: 1.212.380.4469 > > > <tel:1.212.380.4469 < tel: 1.212.380.4469 > <
tel: 1.212.380.4469 < tel: 1.212.380.4469 > <tel: 1.212.380.4469 < tel:1.212.380.4469 > > > < tel:1.212.380,4469 <
tel: 1.212.380.4469 > < tel: 1.212.380.4469 < tel:1.212.380,.4469 > > > > > (office)

+1.917.385.5267 < tel: 1.646.413.3437 < tel: 1.646.413.3437 > < tel: 1.646.413.3437 <



Revised 14 September 2020 EXHIBIT B

2) For

Page 9

2) if the study involves collection and/or analysis of personal/private information
about an individual, or if material used in the study contains links to such
information.

Written approval to begin research or subcontract for the use of human subjects under
the proposed protocol will be provided in writing from the DTRA ROB, through the
Grants Officer. A copy of this approval shall be maintained by both the Grantee and
the government. Any proposed modifications or amendments to the approved
protocol or consent forms must be submitted to the local IRB and the DTRA ROB for
review and approval. Examples of modifications/ amendments to the protocol
include but are not limited to:

* achange of the PI;

* changes in duration or intensity of exposure to some stimulus or agent;

« changes in the information requested of volunteers, or changes to the use of
specimens or data collected; or

« changes in perceived or measured risks or benefits to volunteers that require
changes to the study.

Research pursuant to such modifications or amendments shall not be initiated without
IRB and ROB approval except when necessary to eliminate apparent and immediate
hazards to the subject(s).

Research projects lasting more than one year require IRB review at least annually, or
more frequently as required by the responsible IRB. ROB review and approval is
required annually. The Grantee or subcontractor must provide documentation of
continued IRB review of protocols for ROB review and approval in accordance with
these Terms and Conditions. Research must not continue without renewed ROB
approval unless necessary to eliminate apparent and immediate hazards to the
subject(s).

Non-compliance with any provision of this clause may result in withholding of
payments under the grant pursuant to the grant’s payments clause(s) and/or grant
termination pursuant to the grant’s termination clause(s). The government shal] not
be responsible for any costs incurred for research involving human subjects prior to
protocol approval by the ROB.

animals:

Adhere to the requirements for protection of animal subjects per the DoD level terms
and conditions as well as the following DTRA requirements:

DTRA local clause 252.235-9001 — Prohibition of Use of Laboratory Animals (Jul
2010). The full text of this clause is as follows:

The grant recipient shall obtain approval from the US Army Medical Research and
Material Command (MRMC), Animal Care and Use Review Office (ACURO) prior
to conducting research on Jive nonhuman vertebrates. Studies involving non-human
primates, dogs, cats, or marine mammals will require a site visit by an ACURO
laboratory animal veterinarian as a condition of approval. DoD may also conduct site
visits involving research on other animals when deemed appropriate. The animal

of 14


Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

o Objectives Day One

fe}
fe}
fe}

Refresher on ethics in research with human populations
Review of key principles of research design and preparation
Discussion of strategies of how to design surveys with strong questions

o Objectives Day Two

fe}

oa00

Application of lessons learned from Day One in drafting real survey questions
Introduction to the concept of risk

Introduction to the concept of risk communication

Practice key message development

Practice risk communication skills through examples

Biological Threats Reduction Program


Revised 14 September 2020 EXHIBIT B

decrees, labor standards and regulations of such country or countries during the performance
of the grant. U.S. Government funds may not be used in support of a project which is
prohibited by law in the country or countries in which it is undertaken. DTRA does not
assume responsibility for the recipient's compliance with the laws and regulations of the
country or countries in which the activities are to be conducted.

17. Inconsistency between English Version and Translation of Grant.

The foreign recipient shall ensure that all contract correspondence that is addressed to the
U.S. Government is submitted in English or with an English translation. In the event of
inconsistency between the terms of the grant and any translation thereof into another
language, the meaning in the English language shall control.

18. Value Added Tax (VAT) and Other Taxes

During implementation of grant activities, the recipient will notify DTRA as soon as they
become aware of any VAT or other taxes, exceeding $500.00 per transaction, not identified
in the grant proposal and outside those considered VAT costs associated with travel,
including and limited to lodging, meals, and transportation. A “transaction” is defined as a
single purchase by the recipient and transactions may not be deliberately split in order to
avoid compliance with the $500.00 limit. DTRA approval in writing with documentation of
extraordinary circumstances is required prior to the recipient using any DTRA funds for
VAT or other taxes exceeding $500.00 per transaction, and a grant modification may be
required. The recipient understands that in the event that DTRA is unable to secure approval
to use DTRA funds for VAT or other taxes exceeding $500.00 per transaction, the purchase
of applicable items may not proceed. For those instances where the recipient has received
written approval to use DTRA funds to pay VAT or other taxes on any item(s) exceeding
$500.00 per transaction, the recipient will include this information in its financial reports
(e.g., SF 425) to DTRA.

19. Prohibition on the Use of Cooperative Threat Reduction Funds in the Russian
Federation

Recipients of grants or cooperative agreements in Thrust Area 6 are prohibited from using
DTRA funds for activities in the Russian Federation.

Page 14 of 14


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

pure ML model, excluding early time-series data. The model be structured to have high
flexibility under small number of parameters — i.e. SIR-type model with variable contact
structure and latent/infectious periods. This model will then be fit to the time series data for the
outbreak under the constraint of these prior parameters. Using this approach (see figure), we will
determine whether the structural knowledge embedded in the epidemiological model improves
outbreak predictions or hinders them due to assumptions. As with the pure ML approach, we
will test the hybrid model approach under within-disease, all-disease, and partial-pooled
approaches.

Pathogen Data: Source Data: Environmental Socioeconomic Current
symptoms, point of origin, Data: climatic Data: transport, Outbreak
transmission reservoir host conditions, health care Dynamic |”
mode, genome, ... species, ... ecosystem, .. system ... Data
l l J |
Data from . ===
previous Hierarchical Partial =—
outbreaks

Tree-based machine-learning algorithm aan

Pure ML Hybrid t '

Approach Approach
a ~

All
Approaches

Figure: Schematic of machine-learning approaches. ML algorithm ingests biothreat condition data
and makes predictions based on training from previous outbreak data. In pure ML approach, current
outbreak dynamics are used as ML predictors of outbreak impacts. In hybrid approach, ML algorithm
predicts parameters of an epidemiological model, which predicts impacts using parameters and
current outbreak dynamics.

Outbreak
Impact Pedictons: |~«--
Size, Duration

Pooling '

--1 Epidemiological
1 Model

Model variables will be analyzed at both model-wide and individual case level to determine their
contribution to performance. First, to evaluate model-wide predictive performance, we will re-fit
models successively dropping each predictor to determine which provide the greatest
improvement in performance. At the individual case level, we will calculate Shapley Additive
Explanation (SHAP) scores" for all variables. This will allow for inspection of the role of
variables across major categories (pathogen type, continent), and determine how accuracy varies
across these contexts. Clusters of similar SHAP scores will also allow us to identify clusters of
diseases or conditions that have similar behavior or performance accuracy. Such information
will ultimately support decision-making in selecting models or variables to select for prediction
based on the context of new outbreaks.

To measure the capacity of algorithms to predict new, emerging diseases, we will score using
structured cross-validation in which no disease outbreak prediction is based on an algorithm


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: Noam Ross

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:
Expanding Software Peer Review

Source of Support: Sloan Foundation

Total Award Amount: $677,784 Total Award Period Covered: 09/01/2019 - 09/30/2021

Location of Project: EcoHealth Alliance, subcontract from UC Berekeley (prime)

Person-Months Per Year Committed to the Cal: 3 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DHS Science and Technology Directorate

Total Award Amount: $566,274.26 Total Award Period Covered: 09/05/2019-09/04/2021

Location of Project: EcoHealth Alliance

Person-Months Per Year Committed to the Cal: 3 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Software Containers for Science

Source of Support: Chan-Zuckerbug Foundation

Total Award Amount: $75,000 Total Award Period Covered: 01/01/2020 — 12/31/2020

Location of Project: Berkeley, CA and New York, NY

Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Understanding Risk of Zoonotic Virus Emergence in EID Hotspots of Southeast Asia

Source of Support: NIH

Total Award Amount: $7,573,721.35 Total Award Period Covered: 3/1/20 — 2/28/25

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Reducing the threat of Rift Valley Fever through Ecology, Epidemiology and Sacio-Economics

Source of Support: DTRA

Total Award Amount: $4,988,526 Total Award Period Covered: 06/01/2019 - 05/31/2024
Location of Project: USA, South Africa
Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY


EQUIPMENT (NOTE 1) Prime 1 (Fill:

Type of Equipment
Will the equipment special test
Description of cacy ‘sp . , a
; ‘ be included as part | equipment, special , Supporting | Supporting
Item | Equipment (include i unitof | . 8 P
of adeliverable item| tooling, general | Qty Unit Price | Total Price | Vendor/Source | Documentation| —_ Rationale
(Note 2} | model number) ‘ Issue
(vote 1) under the award? _| purpose equipment, (Note 4) (Note 5)
(van) or plant equipment)
(Note 3)
Base
30
$0
‘Option |
90
Instructions:
+ Fill out all columns on the Equipment Tab for each item,
* Add addtional lines if needed!
+ Ensure that descriptions and vendors listed on the Equipment worksheet match information provided in the backup.
Note 1; Contractors are normally required to furnish all equipment andlor facilities necessary to perform Government contracts (see FAR 45,102(a)) The Government may allow
Note 2: Include a separate section in the above table for the Base and each Option. Add additional lines labeled with Options ifneeded.

Note 3: Definitions:

performing a contract. It consists of Items or assemblies of equipment including standard or general purpose items or components that are interconnected and interdependent
for installing special test equipment, and which are of such a specialized nature that without substantial modification or alteration their use is limited fo the development or
Plant equipment means personal property of a capital nature (incliding equipment, machine tools, test equipment, furniture, vehicles, and accessory and auxiliary items) for

Note 4: Provide the location (section, page) in the proposal's Cost Narrative/Supporting Documentation (BAA para 3.5.2.2.1) used to support your price hasis (e.g, copy of quote, page from catalog, recent invoice or PO),

Note 5: Provide the location (section, page) in the proposal's Technical Basis of Estimate (BAA para 3.

2} of the rationale for the necessity of the proposed materials/supplies



Quarterly Report: HDTRA12010029 - Year 1, Quarter 4 (06/30/21 9/29/21)

Presentations and Publications

1. Presentations and Other Conterence Submissions:
o Dr. Ehab Abu-Basha was invited to attend WHO
3rd Quarterly Meeting “Strengthening One Health

in Jordan” which was hold on July 15", 2021, in
Amman. Dr. Abu-Basha gave a presentation about
Strengthening Regional Disease Surveillance
MERS CoV and AI in Jordan. He also discussed

the methodology and the goals of the current
project. Figure 16. Dr. Ehab Abu-Basha at the

WHO's 3" Quarterly Meeting.

o Dr. Ehab Abu-Basha was invited as keynote
speaker to the World Zoonoses Day 2021 on July
6" by the Turkish Veterinary Association and gave
a presentation about “One Health in Action”. He
addressed the importance of One Health in action,
strengthening collaboration for detection and
surveillance of zoonotic diseases, establishment of

a national One Health committee, capacity vA Nanking H
building and community trust and taising
awareness, wer BY
2. Publications: All Institutes Figure 17, World Zoonoses Day 2021

conference was virtual on Zoom.

No project scientific publications to report as of yet.

Biological Threats Reduction Program ll


Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD
for whether they pose a greater risk of infection with MERS-CoV, avian influenza, brucellosis,
or leptospirosis. These potential modifiable risk factors will be determined through
epidemiologic analysis of the questionnaire data (including time-varying data) paired with
individual laboratory results (e.g., detection of viral RNA, bacterial DNA, or antibodies). Using
quarterly, longitudinal data collected in Tasks 1-3, we will examine any major temporal
dynamics that may be associated with elevated transmission risk (e.g. seasonality, animal
birthing periods), Causal inference techniques within the potential outcomes framework will be
employed to solidify statistical associations as concretized biological/clinical/environmental
pathways by operationalizing exposures as well-defined interventions and ensuring
exchangeability is maintained through confounder control informed by directed acyclic graphs of
the conceptual exposure-disease pathways. The awardee will examine multiple zoonotic
pathogens (as above) to inform general threat reduction guidelines. Identification of modifiable
risk factors will inform intervention development that may interrupt future zoonotic transmission
of the four pathogens in this study, Findings will be shared with USG/DTRA and the Jordanian
Ministry partners at the annual stakeholders meetings, the threat reduction workshop, scientific
presentations and publications as specified in Task 5, This information will provide a strong
basis for potential policy guidance and the generation of solutions at the threat reduction
workshop in OY2.
Y¥3,6.1-02.6.1 Conduct causal inference analyses bases on Tasks 1, 2, 3.
Y3.6.2-02.6.2 Share results of analyses at annual partners and stakeholders meeting.
Y¥3,6.3 & 02.6.3 Submit written report to DTRA and local partners and stakeholders,

Performance Schedule:




Email -

For any purpose other than ta evaluate the white oaoer'propasal this data shall not be disclased autside the
Government and shall net be duplicated used ar disclosed in whole ori oart provided that if an award is made to the
afferar as a result of or in connection with the submission of this data the Government shall have the right to duplicate

use of disclose the data to the
ment to use information contained in the data if it is obtained fram another source without restriction The data

ent provided in the agreement This restriction daes nat limit the right of the

vect to this restriction is Contained on the pages listed selow

Proprietary
Information (list page
numbers}

List a maximum of 8
Key Words or
phrases, separated by
commas. that
describe the Project.

Technical Abstract

(Limit your abstract to

200 words with no

classified or

Proprietary

information}

Knowingly and villfully making any false fictitious or fraudulent statements or representations may be a felany under
the Federal Criminal False Staterrent Act 113 USC Sec 1001: punishable by a fine of up to $10 900 up to five y
prison or both

ears in



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Ieple seer nina 5 $ emo 7 icc § Ron § sonce $ soc 6 0029 b aos
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MATERIALS - SUPPLIES {Note 1)

Prime 1 (Fill-in)

sei Supporting
ttem Description of Material {include model number} aty Unit Price Total Price | Competitive | Vendor/Source | 1 mentation | SUPPerting Rationale
(Note 2) {Sole Source | {If known) (Note 4)
{Note 3)
Base
1 ___ Laptop computers 2 32,000.00 $4,000.00
Option!

Instructions:

Note 1:

Note 2:

Note 3:

Note 4:

+ Fill oul all columns on the Materials/Supplies Tab for each item.

+ Add additional lines if needed.

+ Ensure that descriptions and vendors listed on the Materials/Supplies worksheet malch information provided in Ihe backup.

Material is property that may be incorporated into or attached to a deliverable end item or that may be consumed or expended in performing a contract. It
includes assemblies, components, parts, raw and processed materials, and small tools and supplies that may be consumed in normal use in performing a
contract. Material should be proposed separately from Equipment (see following spreadsheet and definitions).

Include a separate section in the above table for the Base and each Option.

Provide the location (section, page) in the proposal’s Cost Narrative/Supporting Documentation (BAA para 3.

1) of the documentation used to support

your price basis (e.g. copy of quote, page from catalog, recent invoice or PO). If the supporting documentation is located on a website, then please provide a

screen shot.

Provide the location (section, page) in the proposals Technical Basis of Estimate (BAA para 3.5.2.1.2) for the necessity of the proposed materials/supplies



Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has: none
Investigator: Emma Mendelsohn

Support: 2 Current (1 Pending LJ Submission Planned in Near Future (_] *Transfer of Support
Project/Proposal Title:

Investigating Impact of Nutritional Deficiencies on Pandemics

Source of Support: Eleanor Crooks Foundation

Total Award Amount: $70,000 Total Award Period Covered: 05/01/2020 — 10/31/2020

Location of Project: Global

Person-Months Per Year Committed to the Cal: 2 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
REPEL: Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DHS

Total Award Amount: $566,274.26 Total Award Period Covered: 09/05/2019-09/04/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 6 Acad: Sumr:

Support: L] Current L] Pending LJ Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:

Location of Project:

Person-Months Per Year Committed to the Cal: Acad: Sumr:

Support: C1] Current L] Pending LJ Submission Planned in Near Future (_] *Transfer of Support

Project/Proposal Title:

Source of Support:

Total Award Amount: Total Award Period Covered:
Location of Project:
Person-Months Per Year Committed to the Cal: Acad: Sumr:

*If this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS

NECESSARY
AAI



3. For all proposed travel which includes a conference registration cost, please provide the name of
conference and the cost of registration.

4. For all proposed materials and supplies for which the budget justification refers to quotes or
previous purchases, please provide any documentation you have available to support the proposed costs or to
provide additional detail into the basis of estimates for the items.

5. If possible, request that KGMU and KVASU provide a copies of its institutional salary grades to
support its proposed labor?

6. Please provide a breakout of all of HJF's proposed travel. (# of travelers, locations, per diem,
dates, transportation, etc.)

7. Please provide documentation to support HJF's proposed cost for the AKTA service agreement.

8. If possible, request that HJF please provide directly to DTRA an updated copy of its_ provisional
indirect cost and fringe benefit rates for FY20, if available?

9, Please provide documentation/basis of estimate to support KVASU's proposed cost $.1000 per
year is to contribute to organizing the ongoing One Health stakeholder engagement meeting for venue and hosting
costs. (ex. Breakout to support $1,000, what is included in the cost, etc.)

We are hoping to reach resolution of these clarifications (and those previously submitted) by mid-
month, if possible, in order to meet a prospective award date of 31 August. Should your team run into any issues,
please let us know. Thank you very much and we look forward to continuing to work with you and your team! Have
a wonderful afternoon!

Very Respectfully,

Kx)

Contract Specialist

Defense Threat Reduction Agency
pe

<Cautioifo\©)
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Enter awards, honors, promotions, or graduations that occurred during the reporting period for the personnel contributing to this DTRA project.

Instructions

Award #

Exomaie

Select the impact
(hono#] type fram the
rap dawn list, Select

Nothing to Report’ if
there were na impacts.

Impact Type

Best Poster

Impact Description
Best Geaduate Stadent Poster at the
American Physical Society sinter
conference

Select the personnel

Enter the FULL

type for the individual narne of the

wha received the

wardor honor,

Personnel Type

Groduote Student

individual who
received the award,

Awardee Name

John Savth

Enter the date the
erard or honar
was received in
manth/day/year
format,

Impact Date

9/20/2019

Impact Details/Website

MDTRAI-20-1-0029

Nothing to Report

HDTRAI-20-1-0029

HOTRAL 20 1 0029

HDTRAI-20-1-0029

HOTRAL-20-1-0028

MDTRAI-20-1-0029

HOTRAL-20-1-0028

HOTRAI-20-1.0029

MD IRAL-20-1-0029

HOTRAI-20-1.0029

MDTRAI-20-1-0029

HOTRAI-20-1-0029

MDTRAI-20-1-0029

HDTRAI-20-1-0029

HOTRAL 20 1 0029

HDTRAI-20-1-0029

HOTRAL-20-1-0028

MDTRAI-20-1-0029

HOTRAL-20-1-0028

HOTRAI-20-1.0029

MD IRAL-20-1-0029

HOTRAI-20-1.0029

MDTRAI-20-1-0029

[HOTRAI-20-1-0029

Proceed to Intellectual Property



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross

Thrust Area 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

Performance Schedule

Description

Task 1: Develop and deploy project infrastructure

]¥1 01-2 [¥1.93-4 [ov a1-2 [ov a3-4

1.1.1 Layout a provision project network

1.1.2 Set up continious integration pipelines

1.1.3 Implement backup and data verification jobs

1.1.4 Implement differential privacy testing

1.1.5 Write and maintain codebase documentation

1.1.6 Code review, testing and validation

2.1.5 Write and maintain codebase documentation

2.1.6 Perform code review, testing and validation

Task 2: Engineer Project Database

1.2.1 Design database schema

1.2.1 Write pipelines to ingest time-series data

1.2.2 Write pipelines to ingest predictor data

1.2.4 Cross-link data sources via common ontologies

1.2.4 Write QA/QC tests for data and perform data cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors

1.2.6 Update data from live sources and clean and maintain data continuously

2.2.1 Extend database schema te include genetic data

2.2.2 Write pipelines to ingest genetic data

2.2.3 Cross-link data sources via common ontologies

2.2.4 Write QA/QC tests for data and perform data cleaning

2.2.5 Update data from live sources and clean and maintain data continuously

Task 3: ML Model Development and Testing
1.3.1 Implement “practical naive” model baseline

1.3.2 Design BART model structure

1.3.3 Design alternative GAM model structure

1.3.4 Implement within-disease, all-disease, and partial-pooled disease models versions

1.3.8 Measure variable contribution by re-fitting

1.3.6 Analyze prediction-level variable contributions and model performance

1.3.7 lteratively update models to improve performance

2.3.1 Develop genetic predictor variable set via neural-network autoencoding

2.3.2 Modify BART and GAM structures to incorporate genetic variables

2.3.3 Implement within-disease, all-disease, and partial-pooled disease models

2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 4: Hybrid Model Development and Testing

2.4.1 Design mechanistic model structure

2.4.2 Determine mechanistic model prior forms

2.4.3 Madify ML model to predict mechanistic parameters

2.4.4 Implement within-disease, all-disease, and partial-pooled models

2.4.5 Measure variable contribution by re-fitting

2.4.6 Analyze predictionevel variable contributions and model performance

2.4.7 Compare Hybrid and ML performance along outbreak time-series progression

2.4.8 lteratively update models to improve performance

ee Pa

13


Revised 14 September 2020 EXHIBIT B

1) Provisions applicable to a recipient that is a private entity.

a. You as the recipient, your employees, sub-recipients under this award, and sub-
recipients’ employees may not—
« Engage in severe forms of trafficking in persons during the period of time that
the award is in effect;
«© Procure a commercial sex act during the period of time that the award is in
effect; or
© Use forced labor in the performance of the award or subawards under the
award.
b. We as the Federal awarding agency may unilaterally terminate this award, without
penalty, if you or a sub-recipient that is a private entity—
« Is determined to have violated a prohibition in paragraph 1)a. of this award
term; or
« Has an employee who is determined by the agency official authorized to
terminate the award to have violated a prohibition in paragraph L)a. of this
award term through conduct that is either—
© Associated with performance under this award; or
o Imputed to you or the sub-recipient using the standards and due process
for imputing the conduct of an individual to an organization that are
provided in 2 CFR Part 180, "OMB Guidelines to Agencies on
Government-wide Debarment and Suspension (Non-procurement)," as
implemented by our agency at 2 CFR Part 376.

2) Provision applicable to a recipient other than a private entity.

a. We as the Federal awarding agency may unilaterally terminate this award, without
penalty, if a sub-recipient that is a private entity—
« Is determined to have violated an applicable prohibition in paragraph 1)a. of
this award term; or
* Has an employee who is determined by the agency official authorized to
terminate the award to have violated an applicable prohibition in paragraph
1)a. of this award term through conduct that is either—
© Associated with performance under this award; or
© Imputed to the sub-recipient using the standards and due process for
imputing the conduct of an individual to an organization that are provided
in 2 CFR Part 180, "OMB Guidelines to Agencies on Government-wide
Debarment and Suspension (Non-procurement),” as implemented by our
agency at 2 CFR Part 376.

3) Provisions applicable to any recipient.

a. You must inform us immediately of any information you receive from any source
alleging a violation of a prohibition in paragraph La. of this award term.
b. Our right to terminate unilaterally that is described in paragraph 1)b. or 2)a. of this
Article:
« Implements Section 106(g)} of the TVPA, as amended (22 U.S.C. 7104(g)),
and

Page 11 of 14


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EcoHealth Alliance develops science-based solutions to prevent pandemics and promote
conservation.

On Jul 16, 2020, at 11:1 2[6¥6)

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Lares | publications Uloural Articles, Uooss, Yous Chauters, Comerene Pupes, and fhuses/D ssertations} result ng from this DTRA orojeel dusing this ‘eporting peviud
DO NOT ine uve pub icn*ons tha: have rot yet heen sub-nitted

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Proceed to Presentations



Revised 14 September 2020 EXHIBIT B

in the final metrics file are analogous to those of the annual submissions. The final metrics
file shall contain only data from the last annual reporting period until the end of the award’s
funded Period of Performance.

e Email the final Metrics File to dtrabasicresearch@mail.mil (file size must be less than
LOMB). The file name should be ‘Final Metrics’ and the Grant number, e.g. Final
Metrics HDTRA1-12-1-9999.

10. Financial Reporting Requirements.

Federal Financial Reports (SF-425) are due no later than | July of each year with data “as of”
30 May of that year. Grants effective after 31 January will not require a Federal Financial
Report until | July of the following year. All financial reports shall be submitted to the
Administration Office identified in Block 6 of the Research Grant. In addition, the Federal
Financial Report must be submitted electronically as follows:

e Email the Federal Financial Report to dtrabasicresearch@mail.mil (file size must be less
than LOMB). The file name should be the Year, ‘Federal Financial Report’ and the Grant
number, e.g. 2015 Federal Financial Report HDTRA1-12-1-9999.

11. Delegation of Administration Duties.

Certain grant administration duties have been delegated to the Administration Office
identified in Block 6 of the Research Grant. These duties are as follows:

1) Provisionally approve al] Grant and Cooperative Agreement Vouchers.

2) Perform all property administration services except the approval of recipient's requests to
purchase equipment with grant funds. Such approvals must be granted by the DTRA
Grants Officer.

3) Perform all plant clearance functions.

4) Approve requests for Registration for Scientific and Technical Information Services (DD
Form 1540).

5) Obtain all financial report(s) (see Article 10 of this document).

6) Execute administrative closeout procedures, which include the following:

a. Obtain the final Report of Inventions and Subcontracts (DD Form 882).
Obtain final payment request, if any.

c. Obtain final property report and dispose of purchased property and government
furnished equipment (GFE) in accordance with the DODGARs Part 22, Subpart G.

d. Perform a review of final incurred costs and assist the Grants Officer in resolving
exceptions, if any, resulting from questioned costs.

e. Assure that all refunds due the Government are received by the Grantor.

NOTE: This term and condition is not applicable to instrumentation and equipment grant
awards.

12. Security.

As a general rule, PI’s will not need access to classified security information in the conduct
of research supported under this Grant. Should it appear that access to such information is
desirable the recipient shall advise the Grantor and request clearance for the investigator.
Should information be developed during the course of work under this Grant that, in the

Page 7 of 14


Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning
PI: Noam Ross.
Thrust Area 7, J5-A: Machine Learning for Infectious Biological Agent Forecasting

1.2.4 Write quality assurance/quality control (QA/QC) tests for data and perform data
cleaning

1.2.5 Create automatic extraction routines to populate relevant predictors from location
information

1.2.6 Update data from live sources and clean and maintain data continuously

Task 1.3 —- Machine Learning (ML) model development and testing
The awardee shall design, implement, test, and analyze performance of ML model structures and
iteratively improvw them.
Subtasks
1.3.1 Implement “practical naive” model baseline
1.3.2 Design Bayesian additive regression tree (BART) model structure
1.3.3 Design alternative generalized additive model (GAM) structure
1.3.4 Implement within-disease, all-disease, and partial-pooled disease model versions
1.3.5 Measure variable contribution by re-fitting
1.3.6 Analyze prediction-level variable contributions and model performance
1.3.7 Iteratively update models to improve performance

OPTION YEAR:
Task 2.1 — Maintain and document project infrastructure
In the option year, the awardee shall continue to perform maintenance and documentation of
project codebase to ensure reproducibility and reusability of additional components
Subtasks
2.1.1 Write and maintain codebase documentation
2.1.2 Perform code review, testing and validation

Task 2.2 — Extend project database
In the option year, the awardee shall extend the database to include additional predictors and
continuously maintain and update to accumulate more data from upstream sources.
Subtasks
2.2.1 Extend database schema to include genetic data and auto-encoded variables
2.2.2 Write pipelines to ingest genetic data
2.2.3 Cross-link data sources via common ontologies
2.2.4 Write QA/QC tests for data and perform data cleaning
2.2.5 Update data from live sources and clean and maintain data continuously

Task 2.3 — Extend BART model
In the option year, the awardee shall extend the BART model to ingest genetic data and test the
contribution of this data in enhancing or recapitulating pathogen trait data.
Subtasks
2.3.1 Develop genetic predictor variable set via neural-network autoencoding
2.3.2 Modify BART and GAM structures to incorporate genetic variables
2.3.3 Implement within-disease, all-disease, and partial-pooled disease model versions
2.3.4 Compare performance of genetic, pathogen-trait, and combined models

Task 2.4 — Hybrid ML-mechanistic model development and testing


HDTRAI-14-24-FRCWMD-BAA
CBEP = Thrust Area 6 — Cooperative Counter WMD Research with Global Partners

Capacity Building. This project will build capacity by: 1) developing and training local field teams; 2)
transferring diagnostic technologies to partner (including Gol) laboratories and training staff to develop
reagents & analyze data; 3) facilitating exchange of research personnel between US partners (EHA, USU)
and partner labs in India; 4} providing postdoctoral research opportunities for Indian students interested
in One Health, epidemiology, and laboratory diagnostic development; 5) holding One Health meetings to
bring together stakeholders from human, domestic animal, and environmental (wildlife) sectors to
strengthen cooperation and communication around viral disease surveillance; and 6) supporting the
development of the new ICMR National One Health Institute (an MOU between EHA and ICMR is in
process). These activities align with CTRP’s mission to enhance India’s ability to detect and respond to
biological threats posed by zoonotic viruses. The increased capacity at both the field and laboratory level
in human and animal health sectors, including wildlife, will directly improve livelihoods, reduce the
threat from deliberately released agents, and limit the opportunity for zoonotic viruses to emerge
undetected.

Partner Institutions and roles: EcoHealth Alliance (EHA): Design and coordinate the study with project
partners; supervise local post-doctoral candidate; ensure coordination between field and lab studies and
communication to Indian government agencies; and collaboratively analyze and publish data. EHA will
be responsible for all contractual obligations with DTRA. King George’s Medical University (KGMU),
Lucknow: Local implementing partner. KGMU will coordinate & conduct field surveillance activities,
sample testing, data analysis and reporting. National Institute of Virology, Pune: The national reference
laboratory for new and emerging viruses, as well as BSL 4 agents. NIV Pune will assist with further
genetic characterization of viruses. Uniformed Services University, Maryland. Study development.
Development and transfer of Luminex reagents to partner labs. Training, data analysis, and reporting.

5-year staged plan: Year One: Formalize local partnerships & update protocols; apply for research
approvals through ICMR/HMSC; transfer PCR protocols, Luminex and reagents to partner labs and
conduct trainings; hire & train local animal field team; develop database for project; community
outreach and begin human & animal sampling. Years 2-3: Continue sampling; coordinate stakeholder
meetings to share results and develop surveillance and intervention strategies; publish/present findings
(conference & journal). Coordinate with Govt. of India, regional One Health networks (e.g. Bat One
Health Research Network) and US CDC India office to disseminate information from project and help
develop regional strategies for risk mitigation of bat borne zoonoses. Optional Years 4 & 5: Continue
longitudinal surveillance in animals and people in UP; initiate activities in Kerala with established state
One Health partners (e.g. Kerala Forest Dept., Kerala Veterinary and Animal Sciences University).

Major Goals and Milestones: 1) Identify bat reservoirs for henipaviruses, coronaviruses and
filoviruses: Establish study protocols; train field teams; implement field studies; transfer diagnostic
technologies to lab partners; 2) Test the hypothesis that spillover has occurred previously in Uttar
Pradesh: Establish community surveillance; integrate questionnaire about animal exposure; screen
human and animal samples. 3) Strengthen One Health systems by improving knowledge and capacity:
Support the development of a One Health platform in India via ICMR’s new One Health Institute.
Establish inter-departmental collaborations; conduct integrated training of local technical personnel &
graduate students. Report findings to local & federal authorities publish and share project findings via
GHSA India meetings & scientific conferences.

Estimated Budget: Y1: $850,000 Y2: $950,000 Y3: $950,000 Y1-3 Total: $2,750,000
OY 4 $850,000 OY 5 $750,000. OY4-5 total: $1,600,000


Quarterly Report: HDTRA 12010029 - Year 1, Quarter 1 (9/29/20 - 12/28/20)

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Reducing the Threat of MERS-Co¥ and Al in Jordan & Strengthening Regional
Disease Surveillance Capacity

Jordan has experienced several important zoonotic diseases in the few past vears
including avian influenza A (AI) strains HIN] and H5N1 and Middle East Respiratory
Syndrome Coronavirus (MERS-CoV). High-
consequence zoonotic pathogens in the Middle East
have resulted in disease epidemics of international
importance, raising the need for health security
enhancements to strengthen the region’s ability to
prevent, detect and respond to multiple zoonotic
disease threats. MERS-CoV is recognized as a threat to
global health security, with designation as a priority
disease under the World Health Organization (WHO)
Research and Development Blueprint. Although most
cases have been reported in the Kingdom of Saudi
Arabia (KSA), the first known human infections
occurred in Jordan in 2012

Camels are the presumptive source of primary human MERS-CoV infections; however,
the exact mechanisms of transmission and the possible role of other livestock species are unclear.
These uncertainties highlight the need to better
understand the causal factors of primary human
infection with MERS-CoV at human-livestock
interfaces given the high livestock-human contact in
camel production systems in Jordan. Avian
influenza viruses are widely distributed all over the
world. In Jordan, the first outbreak of AJ strain
H3N1 was reported in 2006. The development and
wide spread circulation of a new and highly
pathogenic strain of AI could present a serious risk
to both animal and humans and could result in major
socio-economic losses. In fact, there has been a
rapid expansion of the poultry industry in Jordan in
recent years due to increased demand which
increases the risk of AI to public health, locally as
well as regionally and globally. Therefore, identification and characterization of AI subtypes
circulating among poultry across Jordan will provide critical information needed to understand
baseline activity and risks.

Biological Threats Reduction Program Page 5 of 6


3. Characterize causal factors in animal-to-human transmission of these zoonoses

a. Description: Implement behavioral risk survey (Y 1-OY1}; Conduct epidemiological
analyses (Y1-OY2)

b. Status of Effort: Final approval for data collection is pending from HRPO, but data
collection tools have been finalized. To develop the final behavioral risk survey,
several milestones were met, including collaboratively determining the data
analysis plan and the creation of the survey guide. Our causal inference approach
necessitated a literature review of confounders and pathways, construction of
causal diagrams, and a collaborative discussion amongst team members to
determine which human behavioral risk questions would be important variables in
our analysis (the data analysis plan). Once consensus was achieved on variables
to include in our models, the team collaboratively determined the appropriate
formulation of questions, the order of questions, and team responses to typical
potential participant questions (addressed in the survey guide}.

4. Produce geospatial maps of zoonoses in livestock and poultry and of human zoonotic
transmission risk

a. Description: Generate geospatial distribution and risk maps (Y1-OY2)

b. Status of Effort: The data management system was put into place (Airtable for
managing laboratory data) and a draft report written with mock data in order to
ensure the data curation system is in place for the start of data collection. The final
interface sites were selected for the project for the five regions.

a

Strengthen local capacity for detection and reporting of MERS-CovV. Al. and other
zoonoses in Jordan, Iraq, and Lebanon

a. Description: Host workshops (Y1-2, OY1-2)}; Submit reports and publications (Y1-
OY2); Attend presentations/meetings (Y1-OY2

b. Status of Effort: The Project Launch Workshop and Jordan Training Workshop 1
were successfully completed virtually due to the COVID-19 situation, except for
the third day of the Jordan Training Workshop 1 which was safely held in-person
on May 27". Training at the Human Link lab at the National Research Centre in
Egypt is slated to be held in late summer due to the COVID-19 situation in the
region. The team will update and evaluate timing of this training considering team
safety and local travel and government restrictions to be completed at the earliest
possible date.

6. Identify specific modifiable risk factors for human infection with these zoonoses

a. Description: Conduct causal interference analyses (Y3-OY2); Policy
recommendations on modifiable risk factors (Y3-OY2)

b. Status of Effort: The associated tasks are slated for Y3-OY2.

Biological Threats Reduction Program


Original Message--—--
From: Aleksei Chmura <chmura@ecohealthalliance.org <
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>>>

Sent: Wednesday, July 22, 2020 12:47 PM
To: {PVE}

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Te: Dr. Jon Epstein <epstein @ecohealthalliance.org <
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RESEARCH & RELATED Other Project Information OMB Number: 4040-0001
Expiration Date: 12/31/2022

1. Are Human Subjects Involved? (Yes No
1a. If YES to Human Subjects
Is the Project Exempt trom Federal regulations? [] Yes [No
If yes, check appropriate exemption number. (11 (211304015 De C17L1e
If no, is the IRB review Pending? [Yes  []No
IRB Approval Date: |
Human Subject Assurance Number: |

2. Are Vertebrate Animals Used? [Jes XJNo
2.a. If YES to Vertebrate Animals
Is the IACUC review Pending? [_]¥es  [_]No

IACUC Approval Date:| |
Animal Welfare Assurance Number:[|

3. Is proprietary/privileged information included in the application? —_ [XX] Yes [No

4.a. Does this Project Have an Actual or Potential Impact - positive or negative - on the environment? (Yes [IX] No

4b. If yes, please explain: [

4.. If this project has an actual or potential impact on the environment, has an exemption been authorized or an environmental assessment (EA) or
environmental impact statement (EIS) been performed? Clves [[]No

4.d. IFyes, please explain: |

5. ls the research performance site designated, or eligible to be designated, as a historic place? Oo Yes 4 No

5.a. If yes, please explain: [

6. Does this project involve activities outside of the United States or partnerships with international collaborators? Oo Yes Xx No

6.a. If yes, identify countries: [

6.b. Optional Explanation: |

7. Project SummaryiAbstract [_245-3:na-mL_project—Sunmary_2020-054 | ] [Delete Attachment | [ View Attachment |
8, Project Narrative = [1246-DTRA-MT.-Tashnical Propoaat-20204 [ ] [ Delete Attachment | [ View Attachment ]

9. Bibliography & References Cited | ] _Add attachment _| | JI

10. Facilities & Other Resources | | [Add attachment | [ l| ]
11. Equipment | | [Add attachment | [ || |

12, Other Attachments [Add Attachments | | } | } oO

Tracking Number:GRANT13118386 Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Project Sample Coliectian and Testing Workflow

Sample Collection Lab Testing Analysis Dissemination

We will provide protocals and training for sample collection, handling, and storage in the field and
lab, using established biosafety and biosecurity protocols, including those developed under PREDICT.
We will provide a BioRad Bio-Plex 200 machine with computer console to KGMU and coordinate training
activities for serological surveillance and pan-henipavirus and pan-filovirus consensus PCR assays for
molecular analysis using existing PCR capabilities at KGMU’s virology lab. Co-PI Broder and Co-l Laing
(USU) will provide Bioplex / ELISA reagents for all henipa- and filovirus assays to KGMU. Epstein (EHA)
will provide the Bioplex machine and PCR protocols. Drs. Broder and Dr. Laing will run a 7-day training
course for KGMU lab technicians and invite technicians from UP state medical and veterinary diagnostic
labs which currently have ELISA capabilities. To manage and reduce biothreat, a secure, dedicated -80C
freezer will be installed at KGMU for project samples. Commercial software will be used to log and curate
samples in the lab, creating a sample repository at KGMU.

What opportunities for training and professional development has the project provided?

if the research is not intended to provide training and professional development opportunities or there is
nothing significant to report during this reporting period, state “Nothing to Report.”

Use the impact questions below to guide your discussion of opportunities for training and professional
development provided to anyone who worked on the project or anyone who was involved in the activities
supported by the project. Please indicate for each training: participants, objectives, duration, subject
matter, outcomes, and assessment results. For trainings aligned to BTRP Training Domains,
Competencies, and Proficiencies Framework (DCPs), please indicate the competencies associated with
the training according to the matrix. “Training” activities are those in which individuals with advanced
professional skills and experience assist others in attaining greater proficiency. Training activities may
include, for example, courses or one-on-one work with a mentor. “Professional development” activities
result in increased knowledge or skill in one’s area of expertise and may include workshops, conferences,
seminars, study groups, and individual study. Include participation in conferences, workshops, and
seminars not listed under major activities.

Training

- How will the trainings completed through this project contribute to long-term durability of the capabilities
this project reinforces?

- What are the capabilities built, reinforced, and/or improved upon in the partner country through the
efforts of this research project?

- What are the areas of challenge in terms of transitioning all research capabilities to partner countries
and sustaining these efforts?

Conference Attendance/Knowledge Sharing (Network Building)
- How did you leverage conferences, seminars, and stakeholder meetings to support and engage partner
country scientists affiliated with the research?



RESEARCH & RELATED BUDGET - Cumulative Budget

Totals ($)

Section A, Senior/Key Person

Section B, Other Personnel

Total Number Other Personnel

Total Salary, Wages and Fringe Benefits (A+B)
Section C, Equipment

Section D, Travel

1. Domestic
2. Foreign
Section E, Pa

ant/Trainee Support Costs

1. Tuition/Fees/Health Insurance
Stipends

Travel

Subsistence

Other

7 Aa wp

Number of Participants/Trainees

Section F, Other Direct Costs [ace]
Materials and Supplies La, 000.00]

Consultant Services
ADP/Computer Services
Subawards/Consortium/Contractual Costs
Equipment or Facility Rental/User Fees
Alterations and Renovations

Other 1

Other 2

10. Other 3

oe NP ae ww B

Section G, Direct Costs (A thru F)

Section H, Indirect Costs

Section |, Total Direct and Indirect Costs (G + H)
Section J, Fee

Section K, Total Costs and Fee (I+ J)

Tracking Number:GRANT13118386 Funding Opportunity Number:HDTRA1-14-24-F RCWMD-BAA Received Date. May 28, 2020 05:49:37 PM EDT.


understanding of zoonotic diseases to allow differentiation of natural versus nefarious emergence
events; 4) employing responsible bio-risk management best practices; 5) training partner country
researchers to think critically about ethical research and be competitive in soliciting international
funding; and 6) promoting a One Health concept.

Core Team and Project Organization:

« EcoHealth Alliance (EHA, prime), led by Dr. William Karesh (PI) and Dr. Patrick Dawson
(co-P]), will design, implement, and oversee the research project; lead all contractual
obligations with DTRA and project sub-awardees; coordinate travel and organize workshops;
manage the information database; ensure coordination between partners; and collaborate with
our partners in analyzing the data and publishing it.

* Jordan University of Science and Technology (JUST), led by Dr. Ehab Abu-Basha (co-PI),
will conduct fieldwork and laboratory work on the research study. EHA has worked
extensively with JUST and Dr. Abu-Basha on the USAID PREDICT-2 project, conducting
viral surveillance among humans and animals and risk characterization since 2016.

* Partner Laboratories: JUST (Irbid, Jordan) and Jordan Ministry of Agriculture (Amman,
Jordan). These institutions have demonstrated capacities for all required laboratory work,
training workshops, and specimen storage. Additional laboratory partners may be added
following consultation with national partners and DTRA.

Potential Challenges, Solutions, and Statistical Considerations: The main potential challenge
will be a low detection rate of MERS, AI, and other zoonoses among human participants.
However, power calculations to determine an appropriate sample size will be informed using
recent, credible findings from the PREDICT-2 project. Another potential challenge is addressing
cultural sensitivities while conducting work, such as avoiding stigmatization of livestock workers
or working with various Bedouin communities. EHA and JUST have extensive experience in this
domain through PREDICT-2, and have fostered positive relationships with community leaders
and liaisons throughout Jordan. To reduce regional instability risk, we will be work in Jordan, a
stable bedrock in the region, and will invite regional scientists to workshops inside Jordan rather
than traveling to them.

Major Goals and Milestones: |) Determine the presence of MERS, AI, and other zoonoses
among livestock and/or poultry in Jordan. 2) Determine whether (and if so, the extent to which)
humans at human-livestock/poultry interfaces display evidence of infection with these zoonoses.
3) Characterize causal factors in animal-to-human transmission of these zoonoses. 4) Produce
geospatial maps of zoonoses in livestock and poultry and of human zoonotic transmission risk. 5)
Identify specific modifiable risk factors for human infection with these zoonoses. 6) Implement
Luminex serological technology in Jordan for enhanced diagnostic and detection capabilities. 7)
Strengthen local capacity for detection and reporting of MERS, AI, and other zoonoses in
Jordan, Iraq, and Lebanon through workshops.

Proposed Budget (in US$)

Organization Year 1 Year 2 Year 3 oyl oy2
EcoHealth Alliance (Prime) $492,000 $495,000 $498,000 $490,000 $445,000
JUST $487,000 $493,000 $498,000 $500,000 $430,000

TOTALS $979,000 $988,000 $996,000 $990,000 $875,000

EcoHealth Alliance 4


Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD
Background: Jordan has experienced outbreaks of several high-consequence zoonotic diseases,
including MERS-CoV and AT, Since its emergence in 2012, MERS-CoV has resulted in over
2,400 human cases globally and is recognized as a priority disease under the WHO R&D
Blueprint. While the first human cases of MERS-CoV were traced back to Jordan, little is known
about its underlying and ongoing risk of zoonotic disease in animal populations, particularly
around primary transmission risk factors and pathways that precede spread in healthcare settings.
MERS-CoV is one of several zoonoses posing threat to animal and human populations in the
country. Highly Pathogenic Avian Influenza outbreaks, including subtype HSN1, have been
reported, and brucellosis is considered endemic in Jordan, with recurring spillover but inadequate
understanding of specific modes of transmission and poor vaccination coverage, While human
cases of leptospirosis have not been reported in Jordan to date, high rates of certain Leptospira
serovars have been detected in animals. Key questions and capacity gaps hinder understanding of
the presence, distribution and risk factors in the country, leaving the country vulnerable to
zoonotic biothreats, as well as wider regional gaps in detection and reporting. The threat of
zoonotic disease is especially pertinent given the rapidly-growing poultry production industry in
Jordan and camel, livestock, and poultry rearing in the region,

Jordan has recently made recent several notable scientific advances in detection of MERS-
CoV, generating preliminary data that the proposed project will build on, While camels are the
presumptive source of primary human MERS-CoV infections, the exact mechanisms of
transmission and the possible role of other livestock species are unclear. Blood samples collected
from camels and humans in the northern region of Jordan were positive for MERS-CoV, leading
to the first-ever report of this disease to OIE in camels in Jordan in 2016. To date, only few
countries have reported virus-positive MERS-CoV test results to the OIE so this is a significant
and important step toward improving both MERS-CoV detection and reporting in the Middle
East, Studies of zoonotic pathogens in the region to date have largely focused on single sites or
interfaces, cross-sectional sampling events, or select taxonomic groups, limiting understanding
of causal factors. Research is needed to monitor presence and transmission of zoonotic pathogens
spatially, temporally, and by biohazard exposure (e.g. blood, urine, feces, and/or nasal
secretions) and practices and conduct epidemiological analyses to identify modifiable risk factors
for public health intervention. As a key area of stability in the region, it is crucial to support
Jordan’ s biosurveillance capacity, enable understanding of baseline disease risk to allow
differentiation of natural versus nefarious emergence events, and ensure responsible bio-risk
management to ethically monitor and reduce disease threats. The country has also recently
established a One Health platform that indicates the country’s commitment to countering
zoonotic disease threats, Given the volume of migration between Jordan and its neighbors,
including in animal trade, regional coordination in disease monitoring and threat reduction is a
critical component in effectively characterizing and addressing disease risk. This project seeks to
fill current capacity gaps to advance Jordan as a leader in scientific research and zoonotic disease
management and creating new capacity and scientific collaboration pathways for hypothesis-
driven research and zoonotic disease monitoring and threat reduction in the region.

Key references include (additional references can be found in the Project Narrative):
van Doremalen N, Hijazeen ZS, Holloway P, A] Omari B, McDowell C, Adney D, Talatha
HA, Guitian J, Steel J, Amarin N, Tibbo M, Abu-Basha F, Al-Majali AM, Munster VJ,
Richt JA. High Prevalence of Middle East Respiratory Coronavirus in Young Dromedary
Camels in Jordan, Vector Borne Zoonotic Dis, 2017 Feb;17(2):155-159,


F. Other Direct Costs

Funds Requested ($)

Materials and Supplies
Publication Costs
Consultant Services
ADP/Computer Services

Subawards/Consortium/Contractual Costs

Alterations and Renovations

7,000.00}

1,500.00)

J |

1.

2.

3.

4.

5.

6. Equipment or Facility Rental/User Fees
7.

8.

9.
10.

G. Direct Costs

Total Other Direct Costs

Funds Requested ($)

H. Indirect Costs

Total Direct Costs (A thru F) 181,32

Indirect Cost Type

Indirect Cost Rate (%) Indirect Cost Base (S) Funds Requested ($)

wealth Alliance's NICA rate

a2, 324.00] [

Cognizant Federal Agency

Total Indirect Costs

(Agency Name, POC Name, and
POC Phone Number)

IDepasimens of Defense, Deparvmen: of ihe Navy, Skavon Gales, 1.

|. Total Direct and Indirect Costs

Funds Requested ($)

Total Direct and Indirect Institutional Costs (G + H) 246, 320.52

J. Fee Funds Requested ($)

K. Total Costs and Fee Funds Requested ($)
Total Costs and Fee (I + J) 246, 2.0.82

L. Budget Justification

(Only attach one file.) [i232 DTRA ML_Budge. Juez ificarion. pal Delete Attachment View Attachment ]

Tracking Number:GRANT13118386

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: Noam Ross

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:
Expanding Software Peer Review

Source of Support: Sloan Foundation

Total Award Amount: $677,784 Total Award Period Covered: 09/01/2019 - 09/30/2021

Location of Project: EcoHealth Alliance, subcontract from UC Berekeley (prime)

Person-Months Per Year Committed to the Cal: 3 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Rapid Evaluation of Pathogens to prevent Epidemics in Livestock

Source of Support: DHS Science and Technology Directorate

Total Award Amount: $566,274.26 Total Award Period Covered: 09/05/2019-09/04/2021

Location of Project: EcoHealth Alliance

Person-Months Per Year Committed to the Cal: 3 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Software Containers for Science

Source of Support: Chan-Zuckerbug Foundation

Total Award Amount: $75,000 Total Award Period Covered: 01/01/2020 — 12/31/2020

Location of Project: Berkeley, CA and New York, NY

Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Understanding Risk of Zoonotic Virus Emergence in EID Hotspots of Southeast Asia

Source of Support: NIH

Total Award Amount: $7,573,721.35 Total Award Period Covered: 3/1/20 — 2/28/25

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Reducing the threat of Rift Valley Fever through Ecology, Epidemiology and Sacio-Economics

Source of Support: DTRA

Total Award Amount: $4,988,526 Total Award Period Covered: 06/01/2019 - 05/31/2024
Location of Project: USA, South Africa
Person-Months Per Year Committed to the Cal: 1 Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY


Reducing the Threat of Middle East Respiratory Syndrome Coronavirus and Avian Influenza in
Jordan & Strengthening Regional Disease Surveillance Capacity/PI Karesh
Thrust Area 6 - CCWMD Research with Global Partners FRCWMD
during the Ls, 7th, and 13m sampling visits, respectively), blood samples (serum) will be
collected trom 300 humans. Nasopharyngeal and oropharyngeal swabs will be tested for MERS-
CoV and Al using real ime PCR. Serum samples will be tested using serology for MERS-CoV,
AI, brucellosis, and leptospirosis (Years 1-4). Biological sampling will be paired with behavioral
surveillance data as in Task 3.
Y1.2.1 Identify sites and conduct enrollment for human study component.
¥ 1.2.2 Obtain local permissions and approvals to work with human subjects.
Y 1.2.3-O1.2.3 Conduct biological sampling (nasopharyngeal and oropharyngeal swabs,
and/or blood) of in people within the study area.
Y1.2.4-02.2.4 Conduct lab testing for AI, MERS-CoV, brucellosis and leptospirosis.

TASK 3: Y1.3-OY2.3: Characterize causal factors in animal-to-human transmission of
these zoonoses.
The awardee shall monitor behaviors of persons enrolled in the prospective cohort study (as in
Task 2) to identify and characterize causal factors for animal-to-human transmission of MERS-
CoV, AT, brucellosis and leptospirosis, The PREDICT-2 Human Behavioral Risk Questionnaire
will be augmented with animal-specific exposure frequency questions to collect demographic
data, symptom and medical history data, social history data, and specific animal-related
behaviors and practices that are possible risk factors for infection with MERS-CoV, avian
influenza, brucellosis, or leptospirosis. The project team will administer the survey to enrolled
participants at the first sampling visit (Year 1), During all future visits (Year 1- Option Year 1),
enrolled participants will be administered a brief follow-up questionnaire designed primarily to
capture time-varying exposure and covariate data. This longitudinal information will provide
critical information about the context of exposures and pathways to link biological sampling and
provide the wider context of practices and risk factors, Epidemiologic analysis of the
questionnaire data (including time-varying data) in this Task will identify current practices and
exposure pathways and initial modifiable risk factors. Specific animal-related behaviors and
practices that involve exposure to specific animal bichazards (¢.g., blood, urine, feces, and/or
nasal secretions) will be assessed under Task 6 for whether they pose a greater risk of infection
with MERS-CoV, avian influenza, brucellosis, or leptospirosis, Results will be shared via annual
reports and stakeholder meeting: in Task 5.

¥1.3.1 Obtain local permissions and approvals to work with human subjects,

¥ 1.3.2-O1.3.2 Conduct behavioral risk factor surveys in people.

¥1,3.3-02.3.3 Analyze epidemiologic causal inference to identify modifiable risk factors.

TASK 4: Y1.4-OY2.4: Produce geospatial maps of zoonoses in livestock and poultry and of
human zoonotic transmission risk.

The awardee shall map findings from Tasks 1-2 to show the distribution of detected pathogens
across the regions and sampling sites. Geospatial mapping is a highly relevant visual tool to
assist authorities in targeting surveillance and risk management activities. Using QGIS,
laboratory results of human and animal testing for MERS-CoV, avian influenza, brucellosis, i
leptospirosis will be populated and mapped to show the distribution of detected pathogens. Maps
will be generated beginning Y1, Q3 when laboratory findings are available. Geospatial risk maps
will be generated using statistical models to link human behavioral data to laboratory findings
beginning in Y1, Q4. Geospatial maps will be updated quarterly and shared with USG/DTRA
and Jordanian project partners. Training on geospatial analysis will be provided to project team



RESEARCH & RELATED Senior/Key Person Profile (Expanded)

PROFILE - Senior/Key Person 4

Pretix:[>e.

Position’Title: [::

* Street: [é2

“state: [wy: New Yors

] * First Name:[itty ] | Middle Name: |
* Last Name: [xar ]  Sutfix:[ ]
ative VP fer Healtz and Policy Depariment:|
Organization Name: [Ecolicall: Rlliance ] Division: |
2 34h Bt ]

Street2: [sz 17¢1 ]

“City: fnew ors ] County’ Parish: [ ]
] Province: | l

* Country: [11

UNITED STATES

* Zip / Postal Code:

* Phone Number:[12134 86

] Fax Number: [12325324465 ]

*E-M

[kavesh@econeal thal lance org ]

Credential, e.g., agency login: |wkares ]

* Project Role: [ocher profe

onal ] Other Project Role Category:

Senicr Adviser

Degree Type:

Degree Year:

Attach Biographical Sketch
Attach Current & Pending Support [

l[ ] [Delete Attachment | [ View Attachment |
I

] [Delete Attachment | [ View Attachment |

Tracking Number:GRANT13118386

Funding Cpportunity Number: HDTRA1-14-24-F RCWMD-BAA Received Date:May 28, 2020 05:49:37 PM EDT



Predicting Biothreat Impacts from Early-Stage Data via Transfer Learning

PI: Noam Ross.

Thrust Arca 7, J5-A: Machine Leaming for Infectious Biological Agent Forecasting

ecosystem types, transport infrastructure that can be updated and refined with on-the-ground data
if available.

Disease outbreak impact data: We will draw a variety of sources to build a database of several
thousand of outbreak time-series to train our models. First, we will use time series from AIDO,
the most comprehensive such repository, with approximately 700 series across 40 diseases.
Recognizing that AIDO has limits and data gaps, we will supplement this with several other
sources including One Health disease datasets compiled and maintained by EHA:

e COVID-19 Data: Data on cases, hospitalization, and deaths from COVID-19 are uniquely
available at national, sub-national and city scales for a broad swath of countries with varying
conditions and policy responses. While the COVID-19 pandemic is linked across countries,
in our hierarchical modeling framework we will to separate these out into multiple regional
outbreaks, shedding more light on the effects of environmental and socioeconomic factors

under a common host and pathogen.

© Multispecies datasets: We will supplement human disease outbreaks with data on veterinary
disease outbreaks. EHA maintains a database on veterinary disease outbreaks derived from
OIE and FAO data and supplemented by digitized archival reports (extending back to 1992),
with 2900 outbreak records across 122 diseases. This will more than quadruple the available
disease time series for training. Partial-pooling will ensure that only information in relevant
veterinary data sets will be applied to human outbreaks and vice versa. For instance, effects
of population density or health/veterinary capacity are expected to have partial correlation
across all diseases. In addition, this will allows the model to predict impacts from these
diseases, which has direct defense applications given the food supply, trade, and political
stability impacts of veterinary diseases

© Emerging Infections Disease Repository: EHA’s Emerging Infectious Disease Repository
(eidr.ecohealthalliance.org/)'* contains data on an additional 350 zoonotic disease emergence
outbreaks, giving additional insights in particular to new emergence events from zoonotic
sources. Full time-series are incomplete but outbreak size and duration are recorded, allowing
them to be integrated into our framework with multiple imputation.

Predictor variables: Disease dynamics depend on a complex interplay of pathogen, host, and
environmental conditions. We will use data on pathogens (traits, genomes), sources (reservoir
host traits, range, relatedness to humans), environmental context (climate, ecosystems traits), and
socioeconomic context (local population densities, transportation networks, healthcare systems)
as predictors for our models, as well as time-series data of infections from the earliest phase of
the outbreaks. As we expect all data will be available for neither our full training set nor in
common condition of new outbreaks, use multiple imputation!? in all models to allow for
prediction as well as uncertainty assessment under incomplete data.


Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.}

The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.

Other agencies (including NSF) to which this proposal has
Investigator: William Karesh

Support: LJ Current XX] Pending LJ Submission Planned in Near Future — _] *Transfer of Support
Project/Proposal Title:

Reducing the Threat of Middle East Respiratory Syndrome Coronavirus (MERS) and Avian Influenza (Al) in Jordan &
Strengthening Regional Disease Surveillance Capacity (current proposal)

Source of Support: DTRA

Total Award Amount: $4,975,681.29 Total Award Period Covered: 01/01/2020 - 12/31/2024

Location of Project: USA, Jordan

Person-Months Per Year Committed to the Cal: 2 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Understanding the Risk of Bat-Bourne Zoonotic Disease Emergence in Western Asia

Source of Support: DTRA

Total Award Amount: $4,391 ,443.65 Total Award Period Covered: 10/02/2017 - 10/01/2022

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: & Current LJ] Pending LJ Submission Planned in Near Future — _] *Transfer of Support

Project/Proposal Title:
Ground Truth Network

Source of Support: DHS

Total Award Amount: $2,231,114.13 Total Award Period Covered: 09/30/2016 - 09/29/2021

Location of Project: USA

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:

READY: Augmenting Capacity for Humanitarian Emergencies of Infectious Diseases with Epidemic or Pandemic
Potential

Source of Support: USAID

Total Award Amount: $143,605 Total Award Period Covered: 09/25/2018 - 09/30/2021

Location of Project: Global

Person-Months Per Year Committed to the Cal: 1.56 Acad: Sumr:

Support: 2 Current (1 Pending (1 Submission Planned in Near Future [_] *Transfer of Support

Project/Proposal Title:
Emerging Pandemic Threats PREDICT-2

Source of Support: USAID
Total Award Amount: $100,000,000.00 Total Award Period Covered: 10/01/2014 - 09/30/2019
Location of Project: Global
Person-Months Per Year Committed to the Cal: 4.56 Acad: Sumr:

*[f this project has previously been funded by another agency, please list and furnish information for immediately pre-
ceding funding period.

NSF Form 1239 (10/99) USE ADDITIONAL SHEETS AS NECESSARY



Kx)

Aleksei MacDurian <chmura@ecohealthalliance org < Caution imailto:chmura(@ecohealthalliance org >
<Caution-mailto:chmura@ecohealthalliance.org < Caution-mailto:chmura@ecohealthalliance.org > > >; Alison
Andre <andre@ecohealthalliance.org < Caution-mailto-andre(@ecohealthalliance,org >

-<Caution-mallioandre@ecobealthalliance org < Caution-mailto:andre@ecohealthalliance.org > > >{)6) |

Subject: Re: [Non-DoD Source] FRBAA | 4-6-2-0454

All active links contained in this email were disabled. Please verify the identity of the sender, and confirm the
authenticity of all links contained within the message prior to copying and pasting the address to a Web browser.

Yes. I will do that.
Best wishes for the time ahead,

Ava Sullivan
Project Manager and Research Assistant

EcoHealth Alliance

520 Eighth Avenue — Suite 1200

New York, N¥ 10018

607-280-7669 < tel:607-280-7669 < tel:607-280-7669 > > (mobile) Caution-Caution-
www.ecohealthalliance.org < Caution-http://Caution-Caution-www.ecohealthalliance.org >
<Caution-http./Caution-Caution-www_ecohealthalliance.org < Caution-http:/‘Caution-Caution-

www ecohealthalliance.org > > < Caution-Cantion-http:/‘www.ecohealthalliance.org < Caution-
Caution-http:‘www.ecohealthalliance.org > >

EcoHealth Alliance develops science-based solutions to prevent pandemics and promote conservation

On Aug 25, 2020, at 8:26 PM, Rodriguez, Terrie M CIV DTRA AL (USA) 42)

< Caution-mail{O)6)
Caution-mailto}

Caution-mailto}

Good Evening,

Thank you very much for your e-mail and for the update. Kindly request that you please include all DTRA
personnel contained on the "cc" line of this e-mail for all clarification responses, as [ will he out on maternity leave
beginning Monday 8/31.

Very Respectfully,

Contract Specialist
Defense Threat Reduction Agency

a)



Revised 14 September 2020 EXHIBIT B

e Impact
e Changes/Problems

Templates and specific instructions will be provided each year in advance of the submission
deadline. All files must be submitted via email to dtrabasicresearch@mail.mil (individual
file size must be less than 1OMB). A copy of the RPPR should also be provided to the
Administrative Office identified in the Grant. The file names should be as follows:

¢ RPPR: Year Annual Report Grant Number, e.g. 2017 Annual Report HDTRAIL-12-1-
9999.

e Metrics: Year Metrics Grant Number, e.g. 2017 Metrics HDTRAI-12-1-9999.

Quad Chart. An updated quad chart must be submitted annually. A template will be
provided each year in advance of the submission deadline. All files must be submitted via
email to dtrabasicresearch@miail.mil (individual file size must be less than LOMB). The file
name should be as follows:

® Quad Chart: Year Quad Chart Grant Number, e.g. 2017 Quad Chart HDTRAI-12-1-
9999,

Annual Technical Review. At least one representative (preferably the PI) for each award is
expected to attend and present at an annual technical program review meeting, unless
otherwise exempted by DTRA in writing. For planning purposes reviews will typically be
for two days in Northern Virginia during the spring or summer months.

Final Technical Report. A comprehensive final technical report is required: the draft
document is required forty-five (45) days prior to the end of the Period of Performance and
the final document is required ninety (90) days after the expiration or termination of the
award.

The purpose of the final report is to document and to transition the results of the effort into
the DTRA and DoD applied research community. The final report will always be sent to the
Defense Technical Information Center (DTIC) and unclassified reports may be made
available to the public through the National Technical Information Service (NTIS).

The final report is more than an extension of previous annual reports. The final report shall
be a comprehensive technical summary of the significant work accomplished. The final
report, where it is not readily accessible in published form should, where applicable:

e Clearly describe and illustrate the experimental equipment, setup, and procedures;
© Characterize and tabulate collected/computed data in an appendix;

e = Sufficiently describe computational codes so they can be reproduced. Include a listing of
the code in an appendix if possible and appropriate; and

© When the research effort culminates in the production of one or more student theses or
dissertations, in these cases, the most significant advancements and conclusions
(equations, figures, relationships, etc.) should be included in an executive summary. The
theses or dissertations should be attached as appendices only if they are not readily
available. If they are, clearly reference them and how they can be obtained. Also include
in the executive summary, cumulative lists of people involved in, and publications

Page 5 of 14


Grant Training DCP Matrix

1.6.1-0Y4.6.1 x x x x x x
Hospital staff
identified and
trained on
study protocols

Instructions: this matrix will help track and capture the alignment of BAA research training and professional development activities to BTRP’s
Domains, Competencies, and Proficiencies Framework. Please note: alignment is only required to the competency level definitions shown below.

J. in the left-hand column of the above chart, list every training activity to be performed on a separate line, using their task or sub-task
number (ex. Task 2; subtask 2.1; subtask 2.1.1)

2. place an x or tally against the competencies that align to the training activity. Competency decisions can be made by determining using
the definitions listed below and determining if a) the training subject matter corresponds to the defined competency or b) if the training
outcome corresponds to the defined competency. Please note: training activities can align to multiple competencies across the &
domains. (Note: if completing an annual report, stop at step 2; if drafting a Full Proposal, continue to step 3)

3. once completed, list the aligned competencies under its corresponding training activities in the grant proposal.

Domain Domain Definition Competency Competency Definition

1, Disease 1,1 Understanding of 1.1.1 Disease characteristics and Disease characteristics and epidemiology of

recognition and emerging and high- impact especially high consequence pathogens

prevention of spread | consequence pathogens (EHCPs) and the diseases they cause, including
(EHCP) in the following etiology, signs and symptoms, morbidity,
realms: morbidity, mortality, risk factors, incubation period,
mortality, transmission, period of communicability, mode of



Lares | publications Uloural Articles, Uooss, Yous Chauters, Comerene Pupes, and fhuses/D ssertations} result ng from this DTRA orojeel dusing this ‘eporting peviud
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Proceed to Presentations



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Robert J. Young
520 Eighth Avenue, Ste. 1201, New York, NY 10018
E-mail: young@ecohealthalliance.org

Professional Preparation

University of Minnesota Computer Science BS 1999
University of New Mexico Computer Science MS 2007
Appointments

Senior Research Software Engineer, EcoHealth Alliance, New York, NY 2020 - present
Bioinformatics Programmer, Institute for Genomic Medicine, CUMC 2016 - 2020
Computer Analyst, Laboratory for Computational Vision, HHMI 2007 - 2016
Research Assistant, CTI-II/NESSI Project, University of New Mexico 2005 - 2007
Research Assistant, University of New Mexico 2004 - 2005
Bioinformatics Specialist, Visual Neuroscience Laboratory, HHMI 2000 - 2002

Publications and Presentations

Healy, M.J., Olinger, R. D., Young, R.J., Taylor, $.E., Caudell, T., Larson, K.W., (2009) Applying
Category Theory to Improve the Performance of a Neural Architecture, Neurocomputing,
72(13-15):3158-3173.

Young, R.J., Ritthaler, M., Zimmer, P., McGraw, J., Healy, M.J., Caudell, T.P., (2007) Comparison
of Adaptive Resonance Theory Neural Networks for Astronomical Region of Interest Detection
and Noise Characterization, International Joint Conference on Neural Networks, 2123-2128.

Young, R.J., Ritthaler, M., Healy, M., Caudell, T.P., Zimmer, P. and McGraw, J. (2006) Adaptive
Resonance Theory Neural Networks for Astronomical Region of Interest Detection and Noise
Characterization, Astronomical Data Analysis Software and Systems (ADASS) XVI, ASP
Conference Series, Vol. XXX, Phoenix, AZ (Poster).

Ritthaler, M., Luger, G. F., Young, R., McGraw, J. and Zimmer, P. (2006) Bayesian Belief
Networks for Astronomical Object Recognition and Classification in CTI-II, Astrenemical Data
Analysis Software and Systems XVI, ASP Conference Series, Vol XXX, Phoenix, AZ

(Poster).

Young, R.J. & Williams, L.R., Psychophysical Demonstration of (An)isotropy in Illusory
Surface Computation, Second Annual CS UNM Student Conference (CSUSC 2006),
Albuquerque, NM (Poster).

Healy, M.J., Olinger, R., Young, R.J., Caudell, T.P., and Larson, K., (2005) Modification of the
ART-1 Architecture Based on Category Theoretic Design Principles, Special Session in the
INNS/IEEE International Joint Conference on Neural Networks, 457-462.

Young, R.J. & Williams, L.R., (2005) Psychophysical Measurement of Illusory Contour Shape,
First Annual CS UNM Student Conference (CSUSC 2005), Albuquerque, NM (Poster).


Revised 14 September 2020 EXHIBIT B

«Is in addition to all other remedies for noncompliance that are available to us
under this award.

c. You must include the requirements of paragraph L)a. of this award term in any
subaward you make to a private entity.

4) Definitions. For purposes of this award term:

a. "Employee" means either:
e =An individual employed by you or a sub-recipient who is engaged in the
performance of the project or program under this award; or
« Another person engaged in the performance of the project or program under
this award and not compensated by you including, but not limited to, a
volunteer or individual whose services are contributed by a third party as an
in-kind contribution toward cost sharing or matching requirements.

b. “Forced labor" means labor obtained by any of the following methods: the
recruitment, harboring, transportation, provision, or obtaining of a person for labor or
services, through the use of force, fraud, or coercion for the purpose of subjection to
involuntary servitude, peonage, debt bondage, or slavery.

c. "Private entity":

« Means any entity other than a State, local government, Indian tribe, or foreign

public entity, as those terms are defined in 2 CFR 175.25.

«Includes:

o A non-profit organization, including any non-profit institution of higher
education, hospital, or tribal organization other than one included in the
definition of Indian tribe at 2 CFR 175.25(b).

o A for-profit organization.

d. "Severe forms of trafficking in persons,” “commercial sex act," and "coercion" have
the meanings given at Section 103 of the TVPA, as amended (22 U.S.C. 7102).

PROHIBITION ON USING FUNDS UNDER GRANTS AND COOPERATIVE
AGREEMENTS WITH ENTITIES THAT REQUIRE CERTAIN INTERNAL
CONFIDENTIALITY AGREEMENTS. The recipient agrees to comply with the
requirements in section 743 of the Financial Services and General Government
Appropriations Act, 2015 (Division E of the Consolidated and Further Continuing
Appropriations Act, 2015, Pub. L. 113-235):

1) The recipient may not require its employees, contractors, or sub-recipients seeking to
report fraud, waste, or abuse to sign or comply with internal confidentiality agreements or
statements prohibiting or otherwise restricting them from lawfully reporting that waste,
fraud, or abuse to a designated investigative or law enforcement representative of a
Federal department or agency authorized to receive such information.

2) The recipient must notify its employees, contractors, or sub-recipients that the
prohibitions and restrictions of any internal confidentiality agreements inconsistent with
paragraph 1) of this award provision are no longer in effect.

Page 12 of 14


Grant Training DCP Matrix

statistical data software tools.

3.1.3 Dissemination of findings and
outreach to stakeholders

Knowledge, understanding and proficiency in
the dissemination of research findings through
peer-reviewed scientific journal publications,
conference presentations and public outreach
activities, including engagement with
appropriate stakeholders and partners at
national, regional, and international levels.

3.1.4 Resource mobilization and
management

Knowledge, understanding and proficiency in
the planning, acquisition, mobilization,
allocation, management and tracking of
financial and non-financial resources for
research projects, including proposal
development and grant writing.

4, Management,
leadership, and
advocacy

4,1 Critical skills, knowledge
and behaviors necessary to
effectively manage projects
and programs, lead teams,
champion solutions, inspire
action, and sustain
programs.

4.1.1 Plan, implements, and
evaluates a project or program

Knowledge, understanding and proficiency in
program planning, implementation,
monitoring and evaluation, including
development and measurement of
appropriate indicators, and advocacy, or
management/oversight of the process.

4.1.2 Apply program management
techniques throughout the project or
program

Knowledge, understanding and proficiency in
program management, including timeline and
deliverables management, team building, and
generating high-level stakeholder support.