Attachment A
Statement of Work and Milestone Payment Schedule(Incorporated as of Modification No. 03; changes are indicated in bold italics.)
Project Proposal: MTEC-20-12-Covid19-Diagnostics-023
Organization: The Regents of the University of California, San Francisco
Title: Leveraging Wearable Ring Data for Early SARS CoV-2 Detection
ACURO and/or HRPO Approvals Needed: HRPO approvals needed.
Introduction/Background: The COVID-19 pandemic has highlighted the urgent need, across both militaryand civilian populations, to develop solutions that identify individuals who may be in the earliest stages of atransmittable disease to target rapid testing that will guide isolation and contract tracing procedures aimed atinterrupting transmission. On March 20, 2020, PI Mason launched TemPredict, which enrolled 45,455
participants to provide ambulatory data from Oura smartrings. Oura smartring metrics include temperature,heart rate, heart rate variability, and respiration rate. This sample includes participants who possessed an Ouraring prior to joining the study (n ~43,000) as well as healthcare workers whom TemPredict is providing rings to(2,700 of 3,400 deployed). At present the dataset includes more than 4,000 frontline responders, including
those whom TemPredict recruited (existing Oura smartring owners) and those who TemPredict provided Oura
smartrings to. The current proposal aims to leverage the initial data collected in TemPredict to perform
algorithm development using physiological data from a wearable device (Oura ring) for early detection ofCOVID-19 disease. A secondary aim is to test use of the physiological data for detection of other illnesses.
Scope/Project Objective: This proposal focuses on wearable technologies for early illness detection, and isbroken into 3 major aims:
1. Leverage initial TemPredict data by performing SARS CoV-2 antibody testing in ~10,000 TemPredict
participants to provide accurate determination of infection status, including asymptomatic infection foruse in algorithm development.
2. Use the initial TemPredict data with antibody testing results to develop algorithms, based on
physiological signals (temperature, HR, HRV, and RR), that identify individuals who may have early
SARS CoV-2 disease;
3. Develop protocols for, and to conduct initial deployment and testing of, Aim 2 algorithms using the Ouradevice to trigger: (a) early SARS CoV-2 PCR testing and discriminative influenza testing and (b) early
isolation in civilian populations.
Requirements: All necessary facilities for the proposed work are in action already.
Data gathering. TemPredict has deployed ~2,700 smartrings to front line medical personnel, and ~42,000
additional Oura users have joined TemPredict and are filling out (b) (4)questionnaires. UCSF has anestablished infrastructure for data collection for surveys, enrollment data, and participant tracking for the study,
including University-licensed (b) (4)and HIPAA-compliant research data cloud storage systems.
Data management/storage: All data models will be managed at (b) (4)through (b) (4)(b) (4) (b) (6)and (b) (6). Enhanced labeling through serology (Aim 1) will augment these efforts whensuch data become available.
1. House data at (b) (4) Data pipelines to be established between: a) Oura (b) (4)and (b) (4)(b) (4)) for repeated pushes (2x week for data updates); b) UCSF and (b) (4)(b)(4)
survey data andc) (b) (4) and MIT LL.
2. Integrating Aim 1 serology data. This will require completion of the Serology database framework,
accumulation of the serology test result data, and establishing a pipeline between (b) (4)and theserology results database.

Antibody testing capabilities: Core research laboratory facilities for this project are based at Vitalant ResearchInstitute (VRI, formerly Blood Systems Research Institute), a division of Vitalant (a non-profit blood banksystem) and is housed within the Vitalant blood center in San Francisco, CA. VRI has an affiliation with UCSF.
VRI has state of the art equipment to support research related to blood and blood product safety in the areas ofmolecular biology, immunology, virology, tissue culture, cell processing and epidemiology. VRI staff has 7734
sq. ft. of labs (including a biosafety level 3 lab), and a 3164 square foot freezer room with capacity to store
more than one million specimens. This project will utilize two VRI core laboratories, the Core Immunology
Laboratory (CIL) and the Viral Reference and Repository Core (VRLRC). This project will also utilize clinicallaboratory resources at Creative Testing Solutions (CTS). CTS is the largest nonprofit blood donor testinglaboratory organization in the US, and is owned by Vitalant, American Red Cross, and OneBlood, and is a
CLIA certified laboratory with high throughput testing capabilities, which include support for research study
clinical laboratory testing.
Algorithm development. (b) (4) researchers, together with MIT LL researchers, who have both domain
expertise in time series disease modeling, will lead algorithm development efforts for the detection andprogression monitoring of COVID-19. (b) (4)has suitable computational resources (b) (4)(b) (4)for managing and analyzing the 500 terabytes of time series and accompanying data. Algorithm
development will continue throughout the funding period. Final results (maximum specificity and sensitivity) are
unknowable ahead of effort, and so versions will be released as they are developed, with iterative releases
improving on specificity and sensitivity. Algorithm development will be initiated as soon as data are gatheredfrom Oura and UCSF (b) (4)as opposed to being delayed until the completion of serology data gatheringfrom Aim 1. Algorithm development is a broad, flexible task which requires upfront investment for hiring
postdoctoral researchers who will have 100% effort on these goals. Deliverables will come in the form ofalgorithms accompanied by manuscripts for scientific publication.
1. Algorithm development between (b) (6)at(b) (4)and MIT LL. Algorithm development
proceeds along three parallel paths, all using a framework of ROC curve comparison to evolve higher
specificity and sensitivity:
a. Individualized illness onset detection;b. Individualized severity projection;c. Individualized recovery confirmation.
2. Hire postdoctoral researcher for dedicated algorithm development and data management.
3. Data model development with (b) (6)at (b) (4). While algorithm development will
begin as soon as data gathering to (b) (4) is complete, the analysis of these data will become
substantially more efficient with the development of appropriate data models framing these data toallow faster chunking for computation and querying.
4. Hire postdoctoral researcher for dedicated model development and management.
Milestone Deliverable tasks: Below are milestone deliverable tasks that we include in the milestone payment
schedule
1) UCSF IRB Submission for Aims 1 & 2
2) HRPO Submission for Aims 1 & 2
3) Aim 1: Validation of dried blood spot antibody testing for SARS CoV-2 antibody detection using wellcharacterized sera.
4) Aim 1: Assessment of the accuracy of antibody assay methods for estimation of the SARS CoV-2
infection period using serial sera with known dates of symptom onset.
5) Aim 1: Determination of SARS CoV-2 infection status of initial 3,000 participants first DBS specimen.

6) Aim 1: Determination of SARS CoV-2 infection status in additional 7,000 participants first DBS
specimen
7) Aim 1: Determination of SARS CoV-2 infection status of second DBS specimen in 10,000 participants.
8) Aim 2: Hire postdoctoral researchers9) Aim 2: Data de-identified, aligned, and curated at (b) (4) Oura and (b) (4)data pushed to (b) (4)(b) (4); aligned, de-identified, and moved into (b) (4)asshareable research object
10) Aim 2: Initiate development of full data model for algorithm. MIT LL confirms access to data model
housed on (b) (4) coordinated with (b) (6)and (b) (6)11) Aim 2: Initial algorithm developed for early detection of COVID-19 disease.
12) Aim 3: IRB Approval for Aim 313) Aim 3: HRPO Approval for Aim 314) Aim 3: complete for Aim 315) Aim 3: Data collection prepared for Aim 3: Questionnaires finalized and programmed for participants
and specimen collection and testing protocols16) Aim 3: Data system in place for working with Oura ring data in real time to identify participants withelevated risk of COVID-19 infection.
17) Aim 3: Initial 1,600 participants enrolled for aim 3, from existing TemPredict participant pool (50%)18) Aim 3: Complete enrollment of Aim 3 participants (up to 3,200 total)
19) Aim 3: Complete follow-up of Aim 3 participants, including laboratory tests (initial 1,600 participants)
20) Aim 3: Complete follow-up of Aim 3 participants, including laboratory tests (remaining participants)
21) Aim 3: Analysis of sensitivity and specificity of algorithm for Aim 3 participants.
22) Prepare initial draft of analyses for initial algorithm submission to a peer-reviewed journal outlet
23) Submit initial algorithm publication to a peer-reviewed journal outlet
24) Develop auto-encoder based dynamic baseline classification models to classify individuals into
"types" based on complex physiological features
25) Complete analysis of metagenomic sequencing data26) Develop algorithm 2.0, competing best approaches from algorithm 1.0, autoencoder-determinedbaselines, and other machine learning approaches beside the random forest used within
algorithm 1.027) Split algorithm 2.0 by sex, age, significant confounding conditions (e.g. diabetes), and re-train
to quantify increased efficacy per demographic28) Quantify demographic coverage gaps in support of future efforts to cover diversity
appropriately for algorithms to be deployed across more diverse populations
Milestone Payment Schedule:
Task
MTEC
Milestone # #Significant Event /Accomplishments

Due
Date

Government CostFunds
Share

Total Funding

1-Project Kickoff

7/13/20$40,000$40,000

21

Submission for UCSF IRB Approval,Aims 1 & 2

7/22/20$30,000$30,000

32

Submission for HRPO Approval,Aims 1 & 2

7/27/20$30,000$30,000

23

18

Complete enrollment of additional
participants for Aim 3, including
providing Oura rings (up to 3,200total)

1/2/21$350,00024-

Report #3

12/25/20 N/A

N/A

25

-

Report #4

2/25/21

N/A

N/A

26

19

Complete follow-up of Aim 3
participants, including laboratory
tests (initial 1,600 participants)

4/1/21350,000$350,00027-

Report #5

4/25/21

N/A

N/A

28

20

Complete follow-up of Aim 3
participants, including laboratory
tests (remaining participants)

5/1/21$350,000$350,00029

21

Analysis of sensitivity and specificityof algorithm for Aim 3 participants

5/20/21$68,360$68,360

30-

Report #6

6/25/21

N/A

N/A

31

22

Prepare initial draft of analyses for 7/1/21
initial algorithm submission to apeer-reviewed journal outlet$20,000$20,000

32-

Report #8

8/25/21

N/A

N/A

33

23

Submit initial algorithm
publication to a peer-reviewed
journal outlet

10/1/21$20,000$20,000

34

24

Develop auto-encoder based
10/22/21 $20,000
dynamic baseline classification
models to classify individuals into"types" based on complex
physiological features$20,000

35-

Report #9

10/25/21 N/A

N/A

36

25

Complete analysis of
metagenomic sequencing data

37

26

Develop algorithm 2.0, competing
best approaches from algorithm
1.0, autoencoder-determinedbaselines, and other machinelearning approaches beside therandom forest used within
algorithm 1.0

12/20/21 $20,000$20,000

38-

Report #10

12/25/21 N/A

N/A

39

27

Split algorithm 2.0 by sex, age,
1/25/21significant confounding conditions(e.g. diabetes), and re-train to$20,000$350,000$20,000quantify increased efficacy per
demographic
4028

Quantify demographic coverage
2/25/22
gaps in support of future efforts tocover diversity appropriately foralgorithms to be deployed acrossmore diverse populations$20,000$20,000

41-Final Business and Technical Report 2/28/22

N/A

N/A

Total

$5,156,561$5,156,561