![](media/image1.wmf){width="1.198611111111111in"
height="0.9923611111111111in"}

Evaluation Report for ArboNET/Plague Surveillance System

NASA GSFC / GIMMS in partnership with the Centers for Disease Control
and Prevention

Acknowledgments

This work was supported by NASA Science Mission Directorate, Earth-Sun
System Division under the Applied Sciences Program, Public Health
program Element.

+-----------------------------------+-----------------------------------+
| ArboNET/PSS Team                  |                                   |
+-----------------------------------+-----------------------------------+
| *Compton J. Tucker--Team Leader*  |                                   |
+-----------------------------------+-----------------------------------+
| [NASA/GIMMS/SSAI]{.underline}     | [CDC]{.underline}                 |
|                                   |                                   |
| Jorge E. Pinzon                   | Kenneth L. Gage                   |
|                                   |                                   |
|                                   | Russell E. Enscore                |
|                                   |                                   |
|                                   | Rebecca J. Eisen                  |
+-----------------------------------+-----------------------------------+

**© National Aeronautics and Space Administration**

This document is not a formal publication of NASA, and all rights are
reserved by the Administration. The document may, however, be freely
reviewed, abstracted, reproduced and translated, in part or in whole,
but not for sale nor for use in conjunction with commercial purposes.
The views expressed in documents by named authors are solely the
responsibility of those authors. The mention of specific companies or
specific manufacturers\' products does no imply that they are endorsed
or recommended by the National Aeronautics and Space Administration in
preference to others of a similar nature that are not mentioned.

# Table of Contents {#table-of-contents .Heading-NoTOC}

[**Table of Contents iii**](#table-of-contents)

[Tables 4](#tables)

[Figures 4](#figures)

[Executive Summary 5](#executive-summary)

[1.0 Introduction 1](#introduction)

[1.1 Epidemiology and Distribution of Plague
1](#epidemiology-and-distribution-of-plague)

[1.2 The Surveillance/Observational Data Needs
2](#the-surveillanceobservational-data-needs)

[1.3 Developing a Prototype: Region, Decision and Data Context
3](#developing-a-prototype-region-decision-and-data-context)

[1.4 NASA Mission Traceability 4](#nasa-mission-traceability)

[1.5 Systems Engineering Approach 5](#systems-engineering-approach)

[2.0 Overview of ArboNET/PSS 5](#overview-of-arbonetpss)

[2.1 Integrated Plague Surveillance and Response
5](#integrated-plague-surveillance-and-response)

[2.2 ArboNET/PSS: Technical Guidelines
7](#arbonetpss-technical-guidelines)

[2.3 ArboNET/PSS: Functional & Technical Needs
8](#arbonetpss-functional-technical-needs)

[3.0 Consideration of NASA Inputs 9](#consideration-of-nasa-inputs)

[3.1 Matching NASA Inputs against ArboNET/PSS Requirements
9](#matching-nasa-inputs-against-arbonetpss-requirements)

[3.1.1 Description of Inputs Using NASA Earth-Sun Science Results
11](#description-of-inputs-using-nasa-earth-sun-science-results)

[3.1.1.1 NDVI AVHRR 11](#ndvi-avhrr)

[3.1.1.2 MODIS standard products (NDVI, temperature)
11](#modis-standard-products-ndvi-temperature)

[3.1.1.3 TRMM precipitation 11](#trmm-precipitation)

[3.1.1.4 Landsat and AVHRR Global Land Cover Characterization
12](#landsat-and-avhrr-global-land-cover-characterization)

[3.1.1.5 Bare Earth Elevation 13](#bare-earth-elevation)

[3.2 NASA ESSD Models 14](#nasa-essd-models)

[3.3 Potential NASA Inputs 14](#potential-nasa-inputs)

[3.4 Integrated System Solution 15](#integrated-system-solution)

[4.0 Conclusions 16](#conclusions)

[4.1 Findings 17](#findings)

[4.2 Recommendations 17](#recommendations)

[4.3 Next Steps 17](#next-steps)

[5.0 References 18](#references)

# Tables {#tables .Heading-NoTOC}

[Table 1. Integrated Plague Surveillance.
6](#__RefHeading___Toc125221238)

[Table 2. ArboNET/PSS Requirements. 9](#__RefHeading___Toc125221239)

[Table 3. ArboNET/PSS requirement/NASA input match.
10](#__RefHeading___Toc125221240)

[Table 4. Potential NASA Inputs 15](#_Ref125223884)

#  {#section .Heading-NoTOC .unnumbered}

# Figures {#figures .Heading-NoTOC}

[Figure 1 World distribution of plague. Plague remains endemic in
natural foci around the world: large parts of Africa, Central and South
Asia, South America and South West of US, particularly in the Four
Corners states. Source WHO. 1](#__RefHeading___Toc125226885)

[Figure 2. Area of the four corners states where is developed a
prototype methodology to integrate NASA ESSD results to enhance CDC's
ArboNET/PSS. The image combines DEM and water bodies of the region.
Source USGS EROS Data Center. 4](#__RefHeading___Toc125226886)

[Figure 3. Climate/Plague linkages. 7](#__RefHeading___Toc125226887)

[Figure 4. MODIS surface temperature. 11](#__RefHeading___Toc125226888)

[Figure 5. A revised 1-km resolution global land cover characteristics
data base for use in the characterization of regions that are a natural
foci of plague. Researchers at the University of California used Landsat
to establish the accuracy of the classification by. Source USGS EROS
Data Center. 13](#__RefHeading___Toc125226889)

[Figure 6. Improved accuracy of global elevation data with SRTM30.
SRTM30 provides an accurate synoptic view of Earth's elevation between
60° North and 54° South covering all regions where plague remains
endemic. Source USGS. 14](#__RefHeading___Toc125226890)

[Figure 7. Integrated system solution diagram for incorporation of
Earth-Sun Research results to enhance the Plague Surveillance System
component of CDC's ArboNET. 16](#__RefHeading___Toc125226891)

#  {#section-1 .Heading-NoTOC .unnumbered}

# Executive Summary {#executive-summary .Heading-TOC}

Over the last few centuries, the concept of "environmental medicine" has
been developed based on the simple logic that climate can affect
infectious disease patterns because disease agents and their vectors are
clearly sensitive to temperature, moisture, and other ambient
environmental conditions. In modern times, our increasing capabilities
to detect and predict climate and ecological variations and the
increasing availability of remote-sensing technologies have fueled the
growing interest in understanding better these linkages and applying
these results to develop prototypes of early-warning systems for
infectious diseases. Effective plague prevention and control programmes
require up-to-date information on the incidence and distribution of the
disease. The best means of gathering this information is through a
surveillance programme that collects, analyses, and interprets clinical,
epidemiological, and epizootiological data on plague. A surveillance
system is a key element of decision support within the Centers for
Disease Control and Prevention (CDC). Surveillance should identify cases
and epizootics as quickly as possible so that steps can be taken to
control disease spread.

A better understanding of climate/ecosystems/disease linkages will lead
us towards complementing a "traditional surveillance and response" with
"prediction and prevention" strategies. NASA contributes to these
efforts by making their Earth-Sun Science Division (ESSD) research
results more readily available to the widest possible user community
defined by 12 National priorities. Among them, the Public Health Program
Element within the Applied Science program aims to extend their research
results into partners' decision support tools for public health,
medical, and environmental health issues. The CDC's National Center for
Infectious Diseases (NCID) has adopted the ArboNET/Plague surveillance
system (PSS) as a national strategy for early detection and efficacious
response to plague outbreak . In conjunction with the CDC, NASA is
evaluating the potential for products from NASA's ESSD missions to add
value to ArboNET/PSS's decision support tools. NASA is using a systems
engineering approach to evaluate the potential enhancement of
ArboNET/PSS's decision support system (DSS). This report documents the
evaluation phase of the systems engineering process and involves the
understanding of the components of the system architecture, operations,
and input requirements, and the technical feasibility of NASA products
that may be integrated as system inputs for addressing DSS's needs and
may improve the accuracy, quality, or efficiency of its outputs.

The ArboNET/PSS is defined by guidelines issued by the World Health
Organization in collaboration with the CDC. The primary goal of the
ArboNET/PSS is to identify changes in incidence either in the form of an
acute outbreak or in a change in long-term trends promptly. At present,
plague is one of only three infectious diseases subject to the
International Health Regulations, which stipulate that all confirmed
cases of human plague be investigated and reported through appropriate
authorities to the World Health Organization. Whenever clinical symptoms
or laboratory results suggest that a patient is infected with *Yersinia
pestis*, the suspect case should be reported immediately. This will
allow public health authorities to: (1) advise on treatment and
management of human plague cases; (2) initiate efforts to identify the
source of infection; (3) determine the extent of any epizootic activity;
(4) assess the potential for additional human cases; (5) disseminate
information on plague to health care personnel; and (6) implement
emergency prevention and control measures. Thus, ArboNET/PSS uses in
essence "surveillance and response" strategies.

CDC defines the success of a surveillance system in terms of a number of
factors including simplicity, flexibility, data quality, acceptability,
sensitivity, predictive value positive, timeliness representativeness,
and stability. Clearly, sensitivity and predictive value are important
to measure the usefulness of the system. The other factors are also
critical since they can affect the performance of the ArboNET/PSS by
data reporting cost, level of integration with other systems, legal
assurance of privacy and confidenciality, staff training, or legal
requirements for data collection. The ArboNET/PSS includes human,
rodent, and vector surveillance since the appearance of early cases of
plague in a population is the most commonly used tool to prevent further
human infections. These "surveillance and response" approach provides
fairly high predictive certainty of plague occurrence but often leaves
public health authorities with little advance notice for mobilizing
actions to prevent further spread of the disease. In contrast,
ecological observations and climate forecasts can potentially be used in
efforts to predict the appearance of a pathogen and thus open the
opportunity to minimize its transmission. The ArboNET/PSS relies on
meteorological data, such as precipitation and temperature, to feed the
various modeling routines that are central to the surveillance system.
Much of this information is collected from systems that were implemented
long before the current ESSD missions were deployed by NASA.

A systematic review of the entire ESSD suite of measurements against the
ArboNET/PSS requirements revealed a number of current or planned ESSD
products that may add value as input requirements of the ArboNET/PSS.
Many of these matches are products derived from the Moderate Resolution
Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua
satellites of the EOS and from the Advanced Very High resolution
Radiometer (AVHRR) processed by the Global Inventory Modeling and
Mapping Studies (GIMMS) group at NASA Goddard Space Flight Center.
Several potential NASA inputs that have been identified as high priority
by CDC have already been planned for integration into ArboNET/PSS: NDVI
AVHRR products, MODIS Rapid Response products, Tropical Rainfall
Measuring Mission (TRMM) precipitation-based products. Other potential
inputs from ESSD missions include products from the Shuttle Radar
Topography Mission (SRTM), daily product from GOES, as well as
additional MODIS products still in development (temperature and relative
humidity forecasts).

The results of this evaluation demonstrate that NASA ESSD assets could
make important contributions to ArboNET/PSS. The GIMMS group, using ESSD
results, has implemented a progressive "prediction and prevention"
approach that could be used to issue an alert that environmental
conditions are conducive to plague outbreak which in turn can trigger
surveillance efforts for the area in question. This watch/warning
approach is analogous to the multi-stage early warning system used by
the U.S. National Weather Service. Although, it is likely to have much
lower predictive value, a benefit of this multi-stage approach is that
response plans can gradually rear up as forecast certainty increases and
as surveillance data confirm earlier projections. The active
participation of the system's end users helps ensure that forecast
information is provided in a useful manner and that effective response
measures are developed. The input of CDC's stakeholders will ensure that
data and products produced for the NASA science community meet the data
delivery and continuity requirements of ArboNET/PSS, so that response
plans can be developed with realistic expectations. Finally, concerns
about data continuity could be mitigated in the long-term by focusing
integration efforts on ESSD systems that have operational follow-on
(such as the National Polar-orbiting Operational Environmental Satellite
System) and planned system continuity (such as the Global Precipitation
Measurement mission).

This evaluation report underpins the assumption made by both agencies
when entering into this partnership -- that ESSD systems have the
potential to enhance and complement the plague "surveillance&response"
system used by the CDC with "prediction and prevention" strategies. The
overall ArboNET/PSS enhancement effort should continue to be managed
through a systems engineering approach so that the projects and products
selected for implementation will have passed through a rigorous
verification and validation process to ensure that ArboNET/PSS
requirements are being met and that the resulting improvements in the
system can be effectively benchmarked.

# Introduction

## Epidemiology and Distribution of Plague

Plague is one of three epidemic diseases still subject to the
International Health Regulations and notifiable to the World Health
Organization (WHO). The pathogen causing the disease *Yersinia pestis*
circulates in animal reservoirs, particularly in rodents, in the natural
foci of infection found on all continents except Australia. The natural
foci of plague are situated in a broad belt in the tropical and
sub--tropical latitudes and the warmer parts of the temperate latitudes
around the globe between the parallels 55 degrees North and 40 degrees
South (Figure 1). Plague remains endemic in natural foci around the
world: large parts of Africa, Central and South Asia, South America and
South West of US, particularly in the Four Corners states. However,
within these limits many areas are free of natural foci of plague, such
as desert areas with few or no rodents and large areas of continuous
forest, particularly in the tropics and high glacier--covered mountain
ranges. Plague is transmitted between rodents and to other animals via
wild rodent fleas, cannibalism or (possibly) contaminated soil. *Wild
plague* exists in its natural foci independent of human populations and
their activity.

![](media/image2.jpeg){width="4.298611111111111in"
height="2.9583333333333335in"}

[]{#__RefHeading___Toc125226885 .anchor}Figure 1 World distribution of
plague. Plague remains endemic in natural foci around the world: large
parts of Africa, Central and South Asia, South America and South West of
US, particularly in the Four Corners states. Source WHO.

*Domestic plague* is intimately associated with rodents living with
humans and can produce epidemics in both human and animal populations.
Humans are extremely susceptible to plague and may be infected either
directly or indirectly. Indirect transmission through the bite of a flea
is the most common route of transmission between plague--infected
rodents and humans. Human infection most frequently occurs when an
epizootic develops among synanthropic rats in centres of human
population, following contact with infected wild rodents. Commensal rat
fleas, including plague--infected fleas, leave the bodies of rats killed
by plague seeking a blood meal from another host and may bite human
beings. Humans who contract the disease may subsequently become
infective to other people. Less common is human infection following the
death of rodents during an epizootic in a natural focus. The fleas can
accumulate at the entrance to and the ground surface around burrows and
-as the fleas are not strictly species--specific parasites of their
rodent hosts- bite and infect humans with plague. People can be infected
directly from a plague--infected rodent or other animal while handling,
skinning or cutting up the meat. The plague agent penetrates the human
organism through skin lesions or through the mucous membranes of the
mouth, nose or eyes.

Plague is only occasionally transmitted between humans, either through
the bites of human fleas (*Pulex irritans*) infected after biting
patients in the septicaemic stage, or through direct contact between a
healthy person with an infected person. When primary bubonic plague
develops into secondary pneumonic plague, airborne transmission of the
infective agent may take place via the respiratory route, leading to
primary pneumonic plague among close contacts. Infection through direct
contact with objects contaminated with sputum from pneumonic plague
patients can lead to the development of bubonic plague.

Many natural foci of plague have been identified and measures of
prophylaxis and control developed which make it possible to prevent
plague outbreaks. Effective treatment methods enable almost all plague
patients to be cured if diagnosed in time. The use of these measures has
led to a sharp reduction in the epidemicity of plague throughout the
world. Today the distribution of plague coincides with the geographical
distribution of its natural foci. It is unlikely that all the primary
foci of plague have been discovered. Accordingly, close examination
should be given to any rural area in which repeated cases of human
plague occur. The prolonged absence of human cases in the vicinity of a
natural focus does not in itself mean that plague has disappeared. If
there is no evidence that plague has come from outside sources, the
disease must be sought among local wild rodents.

On every continent, primary natural foci of plague are connected with
particular types of landscape in which climatic conditions are
favourable for a high and stable number of rodent reservoirs and flea
vectors of *Y. pestis*. Most natural foci of plague, including the
mountains, are found where there is low annual precipitation, or where
dry seasons inhibit the growth of thick woody vegetation and lead to the
formation of deserts, semi--deserts and steppes (savannas, prairies,
pampas and so on). Human cases of plague are relatively sparse in
natural foci. Cases occur among people who come in contact with wild
rodents in the course of their work, hunting, or camping. The risk of
human infection increases significantly when plague penetrates
populations of domestic rodents, particularly rats of the genus
*Rattus*.

## The Surveillance/Observational Data Needs 

The U.S. federal agencies entrusted with guarding the nation's health,
such as Centers for Disease Control and Prevention (CDC), adopted an
integrated disease surveillance as a national strategy for early
detection and efficacious response to priority communicable diseases.
While these diseases present a large threat to public health, there are
well-known interventions that are available for controlling and
preventing them. As one of the oldest identifiable diseases known to
man, plague remains endemic in many natural foci around the world. It is
widely distributed in the tropics and subtropics and in warmer areas of
temperate countries. Plague pandemics of past centuries illustrate how
quickly plague can spread through human populations when medical
services and control measures are inadequate. Essentially a disease of
wild rodents, plague is spread from one rodent to another by flea
ectoparasites and to humans either by the bite of infected fleas or when
handling infected hosts. Recent outbreaks have shown that plague may
reoccur in areas that have long remained silent. Untreated, mortality,
particularly from pneumonic plague, may reach high levels. When rapidly
diagnosed and promptly treated, plague may be successfully managed with
antibiotics, reducing mortality from 60% to less than 15%. Although no
one expects to again see the massive deaths observed during past
pandemics, plague continues to pose a threat to human health in certain
regions of the world where natural foci still exist. Effective plague
prevention and control programmes require up-to-date information on the
incidence and distribution of the disease.

The best means of gathering this information is through a surveillance
programme that collects, analyses, and interprets clinical,
epidemiological, and epizootiological data on plague. Surveillance
should identify cases and epizootics as quickly as possible so that
steps can be taken to control disease spread. Surveillance systems for
infectious diseases are used for early detection of outbreaks, analysis
of trends . Continuous surveillance of rodent and vector populations is
important even during periods when no human cases are reported since
many years may elapse between the occurrence of isolated cases or
epidemics. Systematic collection of surveillance information over many
years will provide information that can be used to:

1.  predict areas where future human cases and rodent epizootics may
    occur;

2.  identify the most common zoonotic sources of human infection;

3.  identify the most important rodent and flea species maintaining a
    given focus of *Yersinia. pestis*;

4.  indicate the hosts and flea species that should be targets for
    control measures;

5.  assess the effectiveness of plague prevention and control measures;

6.  identify local ecological factors or human activities that may
    result in increased plague exposure risks for humans; and

7.  detect trends in the epidemiology and epizootology of plague in a
    given region.

Currently, most public health systems rely primarily on "surveillance
and response" approaches to controlling infectious diseases. In
contrast, recent progress in climate forecasting and the availability of
remote-sensing technologies can potentially be used in efforts to
provide early warnings of conditions conducive to disease outbreak.
Disease agents and their vectors each have particular environments that
are optimal for growth, survival, transport, and dissemination. Factors
such as precipitation, temperature, humidity, land cover, vegetation,
and altitude are part of that environment. Each of these environmental
factors can have markedly different impacts on the epidemiology of
various infectious diseases. It might seem a simple task to use NASA
Sun-Earth observations and models to create predictive disease models.
Unfortunately, the mathematical relationships between climate and
disease are neither so obvious neither so simple. In fact, infectious
disease patterns are affected by many factors other than climate, and
the relationship between climatic variations and disease outbreaks is
often mediated by ecological, biological, or societal changes. However,
a better understanding of climate/ecosystems/disease linkages should
lead us towards complementing "traditional surveillance and response"
with "prediction and prevention" strategies. In the development of these
early warning systems, the input of stakeholders is essential so
response plans can be developed with realistic expectations.

## Developing a Prototype: Region, Decision and Data Context

247 human plague cases were reported in the United States during
1980--1997, the highest of any 18--year period since the epidemic years
in the early part of the century. Thirty--seven patients died, a case
fatality rate of 15. The number of cases by year during this 18--year
period ranged from one indigenous case in 1990 to highs of 40 in 1983
and 31 in 1984. Natural foci of plague infection among rodents and their
fleas are widespread in the western United States and plague epizootics
among susceptible rodent species occur frequently throughout the West.
Human cases occur with greatest frequency in two regions: the
south--western region that includes the four corners states:
northeastern Arizona, southern Colorado, southern Utah all of northern
and part of southern New Mexico; and the Pacific region that includes
much of California, southern Oregon and western Nevada. Human cases
outside these two regions have been few and scattered, and have usually
been acquired through direct contact with plague--infected animals
rather than by flea--bite. A recent CDC report on human plague in the
United States emphasizes the importance of two related trends in the
epidemiology of human plague: (1) increased peridomestic transmission
and (2) the role of domestic cats as a source of human infection,
including primary pneumonic plague. It recommends a systematic
collection of surveillance information in the four corners region (). In
the United States, rapid sub--urbanisation has resulted in increasing
numbers of people living in or near active plague foci. During each
successive decade from 1944 to 1993, the number of states reporting
plague cases increased: from 3 in 1944--1953 to 13 in 1984--1993.
Surveillance of plague in rodent and rodent--consuming carnivore
populations indicates that plague spread eastward in the 1990s to areas
believed to have been disease--free since extensive animal surveillance
began in the 1930s. This prototype aims to integrate NASA ESSD results
into the CDC's ArboNET/PSS within the four regions, providing a better
understanding of climate/ecosystems/disease linkages and complementing
ArboNET/PSS "traditional surveillance and response" tools with
"prediction and prevention" strategies. In the development of this early
warning system for plague, the input of stakeholders is
![](media/image3.jpeg){width="6.15625in"
height="4.405555555555556in"}essential so response plans can be
developed with realistic expectations.

[]{#__RefHeading___Toc125226886 .anchor}Figure 2. Area of the four
corners states where is developed a prototype methodology to integrate
NASA ESSD results to enhance CDC's ArboNET/PSS. The image combines DEM
and water bodies of the region. Source USGS EROS Data Center.

## NASA Mission Traceability

Ideally, public health officials would have current, complete, and 100%
accurate information on the incidence and distribution of plague so that
steps can be taken to control disease spread as early as possible. The
impracticality of achieving this with traditional public health
surveillance systems which rely primarily on a "surveillance and
response" approach, encourages interdisciplinary collaboration and
efforts towards "predictive and prevention" strategies that integrate
information derived from remote sensing methods and provide capabilities
for early warnings and policy decisions. NASA ESSD suite has an
important role to play providing the data and the models necessary for
the development of a prototype that further our understanding of
climate/disease linkages and enhance ArboNET/PSS. Given the flexibility
of ArboNET/PSS to import and analyze a large variety of synthesized
data, new sensors provide great promise for providing inputs that are
more current, complete, and accurate. Concerns about continuity could be
mitigated in the long-term by focusing integration efforts on ESSD
systems that have operational follow-on (such as the National
Polar-orbiting Operational Environmental Satellite System) and planned
system continuity (such as the Global Precipitation Measurement
mission).

## Systems Engineering Approach

This evaluation is based on the systems engineering approach followed by
NASA , which entails three phases: evaluation, verification and
validation (V&V), and benchmarking of the ArboNET/PSS. In terms of this
approach, the purpose of this report is to identify functional and
technical needs of the ArboNET/PSS, connect them up with NASA ESSD data
and models that provide these capabilities and enhance the system, and
integrate them into the ArboNET/PSS to set in motion planning for the
V&V phase.

As expected, this process has not been strictly linear. It uses a
Reverse Engineering Paradigm to identify what measurements are important
and why, what NASA ESSD missions or ground systems provide these
measurements, what NASA ESSD models or computational technologies are
available. This process inherently requires interdisciplinary
collaboration and must progress in a coordinated fashion. It involves
active participation of the ArboNET/PSS system's end users to ensure
that all factors that affect the success of the system - sensitivity,
predictive value positive, timeliness, acceptability , simplicity,
flexibility, data quality, stability, continuity, and representativeness
-- are for improvement of socioeconomic benefits and adaptation or
adoption by the CDC.

In preparing this report, we draw heavily on three documents that
describe many of the elements being used in the integration of NASA ESSD
into the ArboNET/PSS. A paper and a poster written by one of GIMMS' own
analysts that describes a progressive multi-stage watch/warning approach
that is being used to issue an alert that environmental conditions are
conducive to a disease outbreak which in turn can trigger surveillance
efforts for the area in question (Pinzon et al., 2005a,b). And two
documents written by members of the CDC/NCID team - a chapter "plague
surveillance" in the WHO's plague manual (Gage,1999) and a national
update paper of modeling relationships between climate and frequency of
human plague cases in the Southwestern U.S. (Enscore et al., 2002).

# Overview of ArboNET/PSS

## Integrated Plague Surveillance and Response

This section describes a comprehensive plague surveillance programme
that includes human, rodent and vector surveillance. The unique needs
and resources of each country will determine the actual organization of
national surveillance programmes. Plague is one of three epidemic
diseases still subject to the International Health Regulations and
notifiable to the World Health Organization (WHO). WHO recommends that
health facilities use the following surveillance case definition for
reporting suspected cases of plague - any person with painful swelling
under the arms or in the groin area; or in an area known to have plague,
any person with cough, chest pain and fever. For plague, a single
suspected case is a suspected outbreak and the case-based information
should be reported immediately to the district following the standard
forms. Summary reports are due monthly, even in those periods where zero
cases were confirmed. Table 1 summarizes the surveillance case
definition for reporting suspected/confirmed cases of plague.

[]{#__RefHeading___Toc125221238 .anchor}Table 1. Integrated Plague
Surveillance.

+-------------+--------------------------------------------------------+
| **B         | -   Zoonotic systemic bacterial infection caused by    |
| ackground** |     *Yersinia pestis* (plague                          |
|             |                                                        |
|             | > bacillus) usually transmitted to humans by rodents   |
|             | > and their fleas.                                     |
|             |                                                        |
|             | -   Main disease forms: bubonic, pneumonic, and        |
|             |     septicemic; large-scale                            |
|             |                                                        |
|             | > epidemics may occur in urban or rural settings.      |
|             |                                                        |
|             | -   Incubation period is 1 to 7 days.                  |
|             |                                                        |
|             | -   Case fatality rate (CFR) may exceed 50-60% in      |
|             |     untreated bubonic                                  |
|             |                                                        |
|             | > plague and approaches 100% in untreated pneumonic or |
|             | > septicemic                                           |
|             | >                                                      |
|             | > plague, but is usually \<1% with appropriate         |
|             | > treatment.                                           |
|             |                                                        |
|             | -   Risk factor: rural residence. Exposure to infected |
|             |     populations of wild or                             |
|             |                                                        |
|             | > domesticated rodents and their fleas.                |
+-------------+--------------------------------------------------------+
| **S         | -   Detect outbreaks of plague promptly. Verify        |
| urveillance |     etiology of all suspected non-outbreak-related     |
| Goal**      |     cases and the first 5 to 10 outbreak-related       |
|             |     cases.                                             |
+-------------+--------------------------------------------------------+
| **          | ***Suspected case***:                                  |
| Recommended |                                                        |
| case        | Any person with sudden onset of fever, chills,         |
| d           | headache, severe malaise,                              |
| efinition** |                                                        |
|             | prostration and very painful swelling of lymph nodes,  |
|             | or cough with blood                                    |
|             |                                                        |
|             | stained sputum, chest pain, and difficulty in          |
|             | breathing.                                             |
|             |                                                        |
|             | ***Confirmed case***:                                  |
|             |                                                        |
|             | Suspected case confirmed by isolation of *Yersinia     |
|             | pestis* from blood or                                  |
|             |                                                        |
|             | aspiration of buboes, or epidemiologic link to         |
|             | confirmed cases or outbreak.                           |
+-------------+--------------------------------------------------------+
| **Respond   | **If a single case is suspected:**                     |
| to alert    |                                                        |
| threshold   | -   Report case-based information to the next level.   |
| for**       |                                                        |
|             | -   Collect specimen for confirming the case.          |
| **epi       |                                                        |
| demic-prone | -   Investigate the case.                              |
| diseases**  |                                                        |
|             | -   Treat the patient with streptomycin, gentamicin or |
|             |     chloramphenicol,                                   |
|             |                                                        |
|             | and administer chemoprophylaxis of close contacts with |
|             |                                                        |
|             | tetracycline for seven days from time of last          |
|             | exposure.                                              |
+-------------+--------------------------------------------------------+
| **Respond   | **If the suspected case is confirmed:**                |
| to action** |                                                        |
|             | -   Isolate patients and contacts of pneumonic plague  |
| **threshold |     with precautions                                   |
| for**       |                                                        |
|             | against airborne spread (wear masks, for example)      |
| **epide     | until at least                                         |
| mic-prone** |                                                        |
|             | after 48 hours of appropriate antibiotic therapy.      |
| *           |                                                        |
| *diseases** | -   Mobilize community to enable rapid case detection  |
|             |     and treatment, and to recognize mass rodent        |
|             |     die-off as a sign of possible impending epidemic.  |
|             |                                                        |
|             | -   Identify high risk population groups through       |
|             |     person, place, and time                            |
|             |                                                        |
|             | > analysis.                                            |
|             |                                                        |
|             | -   Reduce sporadic and outbreak-related cases via     |
|             |     improved control or rodent populations (remove     |
|             |     trash, food sources, and rat harborages) and       |
|             |     protect against fleas with insect repellent on     |
|             |     skin and clothing and environmental flea control   |
|             |     (especially in homes and seaports and              |
|             |                                                        |
|             | > airports).                                           |
+-------------+--------------------------------------------------------+
| **Analyze   | **Time:** Graph monthly trends in cases and deaths.    |
| and**       | Construct epidemic curve for                           |
|             |                                                        |
| **interpret | outbreak cases.                                        |
| data**      |                                                        |
|             | **Place:** Plot the location of case households.       |
|             |                                                        |
|             | **Person:** Immediate case-based reporting of cases    |
|             | and deaths for routine                                 |
|             |                                                        |
|             | surveillance. Count weekly cases and deaths for        |
|             | outbreaks. Analyze                                     |
|             |                                                        |
|             | age distribution and assess risk factors to improve    |
|             | control of sporadic                                    |
|             |                                                        |
|             | disease and outbreaks.                                 |
+-------------+--------------------------------------------------------+
| **          | Gage, 1999                                             |
| Reference** |                                                        |
+-------------+--------------------------------------------------------+

## ArboNET/PSS: Technical Guidelines

![](media/image4.jpeg){width="5.4118055555555555in"
height="6.59375in"}Additional epidemiological investigation and
ecological observation should be performed for each human case to
determine the source of infection and the risk of additional human
cases.

[]{#__RefHeading___Toc125226887 .anchor}**Figure 3**. Climate/Plague
linkages.

1.  ***Epidemiological Information***

Reports of these investigations should include: 1) a complete history of
the patients\' activities and travel during the incubation period of the
infection; 2) results of field studies to determine which animal and
flea species are likely sources of infection or pose a continuing threat
to humans, surveillance techniques for rodents and fleas can be found in
(Gage, 1999); 3) proximity of infected rodents and fleas to human
dwellings or workplaces; 4) estimated number of people involved in
activities that place them at high risk of plague infection; and 5)
information on possible exposure to *Y. pestis* infection of patient
contacts (especially important for pneumonic plague cases).

Flea and rodent control programmes implemented as a result of human
plague case investigations should be described with an evaluation of
their success. Rodents are the primary vertebrate reservoirs of plague,
and nearly all human cases are associated with rodent epizootics.
Surveillance programmes that monitor plague activity in susceptible
rodent populations alert public health authorities to increased human
plague risks, thus allowing prevention and control programmes to be
implemented before human plague cases occur. Identification of plague in
rodent populations also serves as a warning that human cases may appear
and require treatment and follow-up.

2.  ***Ecological and Environmental Observations***

Positive correlations are found between winter-spring precipitation and
occurrence human plague cases. ArboNET/PSS uses a proposed trophic
cascade model to predict number of plague incidences -- Increased
precipitation enhances small mammal food resource productivity, which
leads to increases in host populations and thus in plague risk (Figure
3). A basic understanding of the area\'s landscape ecology is thus
useful for predicting the future course of epizootics and identifying
areas of high risk for humans. Information should be collected on
predominant vegetation types and the amount of local land surface
covered by each vegetation type, roads, railways, airports, and
seaports, land use patterns (agricultural, residential, industrial,
other), types of dwellings present and whether these dwellings and
associated food storage areas or other man-made sites provide food and
harbourage for rodents.

## ArboNET/PSS: Functional & Technical Needs

Climate variability is thought to affect a number of infectious
diseases. In particular, the Trophic Cascade Hypothesis (TCH) was
developed to explain changing levels of human risk for zoonotic diseases
associated with climate variability in the US Southwest. The role of
weather and ecological/environmental mechanisms by which human health is
impacted by climate variability presently is not well understood. When
the TCH modeling system is applied to forecast plague outbreak
conditions two different triggering mechanisms are observed. Depending
on the region, a bimodal (May and July) or unimodal (August) peak
dynamics is found. Rodents' movements also play an important role in the
plague dynamics, complicating the system. Rodents can move down slopes,
from higher elevations to lower elevations; plague does the same. Thus,
products derived from Earth observing instruments, technology, and
science information that consider not only vegetation dynamics, but also
elevation, slope, aspect, and land cover diversity in their models will
provide information on plague vector habitats enabling ArboNET to better
characterize outbreak conditions and enhance its PSS. A reverse
engineering paradigm was used in this phase of the systems engineering
approach to define requirements and specifications of the ArboNET/PSS.
The requirements for ArboNET/PSS are not well defined. Some ideas
extracted or inferred from available documentation that relate to
requirements are summarized in Table 2. It is not clear that these
notions represent ArboNET/PSS consensus, and this list may not be
exhaustive.

The CDC, participating health departments, Department of Defense (DOD)
and the US Geological Survey (USGS) are primary users of ArboNET. Some
of the users need data on a regular basis to anchor response levels. By
convention, the time step for these regular production estimates has
been set at one month. When suspected cases are involved, risk maps may
be needed within two to five days of an emergency to assist decision
makers.

[]{#__RefHeading___Toc125221239 .anchor}Table 2. ArboNET/PSS
Requirements.

  ------------- ---------------- ---------------- ----------------------------
  ARBONET/PSS                    Requirement      Drivers
  Property                                        

  Data          Monthly          10 day, with     Need to establish trends
  Frequency     reporting        15-day summaries within 1-month cycle
  (time step)                                     (seasonal signature of
                                                  incidences)

                Emergency        2 day            Time requirements of
                                                  emergency response planners

  Spatial       Monthly          Synoptic         Better spatial resolution
  Resolution    reporting        analysis: 8 km   than stations
                                 or better        

                                 Field-level      Surveillance of rodents
                                 analysis: 30 m   
                                 or better        

                Emergency        1 km or better   Breeding/resting/feeding
                                                  habitats

  General       Cost             Moderate         Budget constraints by region
  Properties                     resolution       
                                 product should   
                                 be free          

                Format           Simple (web      Easy reporting
                                 based)           

                Accessibility    Web accessible   Continue greater public
                                                  benefit leveraging being
                                                  demonstrated

                Data validation  Validated and    Quality assurance
                                 science team in  
                                 place            

                Data redundancy  Multiple sources Data continuity, convergence
                                                  of evidence

  Parameters    Precipitation    1 degree or      Needed for TCH models;
                                 better           cumulative precipitation of
                                                  stand-alone interest

                Min and max      1 degree or      Needed for crop yield, crop
                temperature      better           calendar, crop stress, and
                                                  soil moisture models

                Vegetation index 8 km or better   Needed for relative
                numbers                           vegetation condition
                                                  assessments

                Bare earth       5 km or better   Needed for rodent movement
                elevation                         model

                Soil moisture    Unknown at this  soil moisture of stand-alone
                                 time             interest in regard to flea
                                                  survival
  ------------- ---------------- ---------------- ----------------------------

# Consideration of NASA Inputs

## Matching NASA Inputs against ArboNET/PSS Requirements

Given the information requirements of ArboNET/PSS, it is important to
inventory relevant NASA inputs as summarized in Table 3. This Table
displays inputs from the NDVI-AVHRR dataset created by the GIMMS group,
which is of particular interest because is the first AVHRR 8-km NDVI
timeseries that covers the period from July 1981 to the present and it
is compatible with the NDVI from more modern instruments, e.g. MODIS and
SPOT Vegetation (Pinzon et al. 2005c). Since most of the plague activity
in the USA has occurred in the 1980s and 1990s, the NDVI-AVHRR data is
essential to study the environmental factors that often set the stage
for optimal conditions for the spread of plague and are important in
determining their transmission, intensity and duration. The integration
of this data set with the higher resolution and better quality NDVI data
from MODIS uses a t^2^-normalized anomaly product that was also
developed at the GIMMS group. It also represents the evolution from the
station and interpolated data currently used in ArboNET/PSS. AVHRR/GIMMS
and MODIS are also important as a transition to the next generation
moderate resolution sensor: the Visible Infrared Imaging Radiometer
Suite (VIIRS) that will be part of the National Polar-orbiting
Operational Environmental Satellite System (NPOESS) and the NPOESS
Preparatory Project (NPP) missions.

+-----------------+--------------+------------------------------------+
| [               |              |                                    |
| ]{#__RefHeading |              |                                    |
| ___Toc125221240 |              |                                    |
| .anchor}Table   |              |                                    |
| 3. ArboNET/PSS  |              |                                    |
| r               |              |                                    |
| equirement/NASA |              |                                    |
| input match.    |              |                                    |
+-----------------+--------------+------------------------------------+
| PSS Required    | NASA Inputs  | Met/Unmet Requirements             |
| Observation &   |              |                                    |
| Predictions     |              |                                    |
+-----------------+--------------+------------------------------------+
| > High/Moderate | ESSD data    | -   MET: 15-day global coverage →  |
| > Resolution    | and products |     MODIS/AVHRR global coverage    |
| > Visible-IR    |              |                                    |
| > Remote        |              | -   MET: 8-km spatial resolution → |
| > Sensing       |              |     MODIS 250 m to 1 km            |
| > Information & |              |                                    |
| > Products      |              | > → AVHRR 8 km                     |
|                 |              |                                    |
|                 |              | -   PLANNED: Modify to 10-day time |
|                 |              |     step                           |
+-----------------+--------------+------------------------------------+
| a.  Moderate    | -   MODIS    | -   UNCERTAIN: Continuity to be    |
|     Resolution  |     NDVI     |     established by back-processing |
|     Vegetation  |              |     to year 1981 data.             |
|     Indices     | -   AVHRR    |                                    |
|                 |              | -   MET: The NASA/GIMMS            |
|                 | NDVI/GIMMS   |     cross-validation work among    |
|                 |              |     the various AVHRR sensors and  |
|                 |              |     MODIS has given confidence in  |
|                 |              |     the long-term NDVI data        |
|                 |              |     quality and important as a     |
|                 |              |     transition to the next         |
|                 |              |     generation moderate resolution |
|                 |              |     sensor. It favors efforts on   |
|                 |              |     the disease/climate linkages.  |
+-----------------+--------------+------------------------------------+
| a.  Temperature | -   MODIS    | -   MET: Measured land surface     |
|     (Min and    |     Land     |     temperature as opposed to      |
|     Max)        |     Surface  |     current interpolated           |
|                 |              |     temperatures                   |
|                 |  Temperature |                                    |
|                 |              | -   UNCERTAIN: Accuracy            |
|                 |              |     requirement                    |
|                 |              |                                    |
|                 |              | -   UNMET: Hourly temporal         |
|                 |              |     resolution                     |
+-----------------+--------------+------------------------------------+
| a.  Humidity    | -   MODIS    | -   UNMET: Project plans to        |
|                 |     soil     |     deliver this MODIS Rapid       |
|                 |     moisture |     Response product using         |
|                 |              |     non-standard algorithms.       |
|                 |              |                                    |
|                 |              | -   UNCERTAIN: Accuracy            |
|                 |              |     requirement                    |
+-----------------+--------------+------------------------------------+
| a.  Topography  | -   SRTM     | -   MET: SRTM30 for North and      |
|                 |              |     South America is available     |
|                 |              |     since 2003.                    |
+-----------------+--------------+------------------------------------+
| a.              | -   TRMM     | -   MET: The radar can determine   |
|   Precipitation |              |     if precipitation is reaching   |
|                 |              |     the ground as opposed to       |
|                 |              |     current interpolated WMO       |
|                 |              |     stations.                      |
|                 |              |                                    |
|                 |              | ```{=html}                         |
|                 |              | <!-- -->                           |
|                 |              | ```                                |
|                 |              | -   UNCERTAIN: Accuracy            |
|                 |              |     requirement                    |
+-----------------+--------------+------------------------------------+
| a.  Land        | -   AVHRR    | -   MET: This may prove useful to  |
|     Surface     |     Land     |     ArboNET/PSS identification of  |
|     type        |     Cover    |     rodent habitat, but there is   |
|                 |              |     no documented requirement for  |
|                 |              |     this MODIS Standard Product.   |
|                 |              |     Uses Landsat for better        |
|                 |              |     interpretation.                |
+-----------------+--------------+------------------------------------+

### Description of Inputs Using NASA Earth-Sun Science Results

#### NDVI AVHRR

As discussed earlier, the NASA/GIMMS NDVI archive is a vital data stream
for ArboNET/PSS. The NASA/GIMMS cross-validation work among the various
AVHRR sensors has given confidence in the long-term data quality and has
allowed the group to focus more efforts on the disease/climate linkages.

#### ![](media/image5.jpeg){width="5.09375in" height="3.75in"}MODIS standard products (NDVI, temperature) 

[]{#__RefHeading___Toc125226888 .anchor}**Figure 4.** MODIS surface
temperature.

The MODIS Standard Products will be delivered through the GES DAAC. The
University of Maryland-led team is responsible for all but one of the
Standard Products. The value added by the project teams is data
processing so that MODIS information is in the proper file/geographic
format for ArboNET's use. Of the types of products being delivered, most
types (NDVI, land surface temperature, Figure 4) are either already in
use by or are closely akin to types already in use. The vegetation
indices and the surface temperature products meet ArboNET/PSS
requirements by upgrading the data quality (radiometric, and spatial)
from the stations that ArboNET/PSS could use.

#### TRMM precipitation

Currently ArboNET/PSS's precipitation information comes from WMO weather
stations or from precipitation estimates. The weather station
information is limited to point estimates, which are sparse in some
regions.

Essentially, the precipitation estimates from AGRMET is a mix of passive
microwave remote sensing and visible/infrared remote sensing estimates
the large areas between stations. Algorithms estimating precipitation
from visible/infrared remote sensing suffer from the weak relationship
of cloud-top characteristics with the intensity or even the existence of
precipitation at the surface. Passive microwave remote sensing is better
because it can sense factors more directly involved in the process of
making rainfall. But precipitation rate estimates derived from passive
microwave remote sensing suffer if not calibrated frequently, because it
is still difficult to determine if precipitation is reaching the ground.

TRMM calibrates its passive microwave sensor, the TRMM Microwave Imager
(TMI) with simultaneous radar: the Precipitation Radar (PR). The radar
can determine if precipitation is reaching the ground; in fact, it gives
three-dimensional information about rain cloud structure. It is not
feasible to achieve broad coverage with radar, but it can serve as a
calibration source for the passive microwave sensor. This calibrated
data can then be cross-calibrated with passive microwave sensors on
other platforms to create a global precipitation measurement
constellation. This constellation is the concept the Global
Precipitation Measurement (GPM) mission for which TRMM is a precursor. A
research group at NASA/GSFC has developed a cross-calibration of the
TRMM's TMI data with other microwave sensors, namely the DMSP's SSM/I
sensors to create a level 3 TRMM product (3B42RT), the "TRMM Real-Time
Multi-Satellite Precipitation Analysis", with some completeness and
consistency. This product is being delivered online at
<http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology/TRMM_analysis.html>

####  Landsat and AVHRR Global Land Cover Characterization

The [U.S. Geological Survey (USGS)](http://www.usgs.gov/), the
[University of Nebraska-Lincoln (UNL)](http://www.calmit.unl.edu/), and
the [European Commission\'s Joint Research Centre
(JRC)](http://www.jrc.cec.eu.int/) have generated a revised 1-km
resolution global land cover characteristics data base for use in a wide
range of environmental research and modeling applications. The global
land cover characteristics database was developed on a
continent-by-continent basis. All continental databases share the same
map projections (Interrupted Goode Homolosine and Lambert Azimuthal
Equal Area), have 1-km nominal spatial resolution, and are based on 1-km
[Advanced Very High Resolution Radiometer
(AVHRR)](http://lpdaac.usgs.gov/1KM/1kmhomepage.html) data spanning
April 1992 through March 1993. Each database contains unique elements
based on the geographic aspects of the specific continent. In order to
provide flexibility for a variety of applications, a core set of derived
thematic maps produced through the aggregation of seasonal land cover
regions are included in each continental database. The continental
databases are combined to make seven global data sets, each representing
a different landscape based on a particular classification legend. The
global data sets are also available in two map projections (Interrupted
Goode Homolosine and Geographic, Figure 5).

![](media/image6.png){width="6.504861111111111in"
height="3.4479166666666665in"}

[]{#__RefHeading___Toc125226889 .anchor}**Figure 5**. A revised 1-km
resolution global land cover characteristics data base for use in the
characterization of regions that are a natural foci of plague.
Researchers at the University of California used Landsat to establish
the accuracy of the classification by. Source USGS EROS Data Center.

It is recommended the use of the global data set to characterize the
land cover of natural foci around the world where plague remains endemic
and facilitate the extension of the prototype being developed for the
Four corners states of U.S. worldwide. Landsat would also provide a high
resolution for better interpretation of the land cover map and increase
its accuracy accordingly.

#### Bare Earth Elevation

For bare earth elevation, data from the Shuttle Radar Topography Mission
(SRTM) represents a significant improvement from previous elevation
data, such as ETOPO-5 which had only a 5 arc minute resolution
(\~10 km), and GTOPO30, which had a 30 arc second resolution (\~1000 m).
These older data sources were assembled from disparate sources of
varying accuracy. A new global dataset based primarily on SRTM data,
SRTM30, has been released. The SRTM30 is a near-global digital elevation
model (DEM) comprising a combination of data from the SRTM mission and
the U.S. Geological Survey's GTOPO30 data set. It can be considered to
be either an SRTM data set enhanced with GTOPO30, or as an upgrade to
GTOPO30 (Figure 6).

The SRTM mission, flown aboard Space Shuttle mission STS-99 in February
2000, provided an accurate synoptic view of the Earth's elevation
between 60° North and 54° South which covers natural foci around the
world where plague remains endemic. It is widely distributed in the
tropics and subtropics and in warmer areas of temperate countries. SRTM
elevation data is also globally processed to a 90 m grid and could be an
important data source for rodent movement analysis. Processing at this
level has been completed for North and South America in 2003, and
globally by the end of 2004.![](media/image7.png){width="3.20625in"
height="1.7034722222222223in"}

##  {#section-2 .unnumbered}

## NASA ESSD Models

The GIMMS group, using NASA ESSD results, has implemented a progressive
"prediction and prevention" approach that could be used to issue an
alert that environmental conditions are conducive to plague outbreak
which in turn can trigger surveillance efforts for the area in question.
This watch/warning approach is analogous to the multi-stage early
warning system used by the U.S. National Weather Service. Although, it
is likely to have much lower predictive value, a benefit of this
multi-stage approach is that response plans can gradually rear up as
forecast certainty increases and as surveillance data confirm earlier
projections. The active participation of the system's end users helps
ensure that forecast information is provided in a useful manner and that
effective response measures are developed. The input of CDC's
stakeholders will ensure that data and products produced for the NASA
science community meet the data delivery and continuity requirements of
ArboNET/PSS, so that response plans can be developed with realistic
expectations. The model is part of system that includes the necessary
components for an operational early warning system. *Climatic forecasts*
is combined with information from ongoing *epidemiological surveillance*
and *environmental observations* (NASA ESSD dataset) to be used as input
for predictive models that can generate *alerts* or *warnings* about an
impending disease risk. This information is then coupled with
*vulnerability assessments* to determine which segments of a population
are most likely tob e affected and risk analysis to determine the likely
impact this group will have. The actions required to reduce these
impacts are defined in the *response strategy*. A *public communication
system* facilitates the timely dissemination of this information to
vulnerable groups. An essential part of the whole system is the
epidemiological surveillance.

## Potential NASA Inputs

The AVHRR, MODIS, TRMM, SRTM30, and models discussed above are not the
only NASA resources that might be of benefit to the ArboNET/PSS. More
NASA assets have the potential to provide important required input data
into the surveillance system. Among them are hourly surface temperature
from GOES and an improved 500 m land cover from MODIS, using Landsat to
help interpretation (Table 4).

+--------+---------+-----------+--------------+----------------------+
| Sat    | Sensor  | System    | Product      | Use in ArboNET/PSS   |
| ellite |         | Operator  |              |                      |
+--------+---------+-----------+--------------+----------------------+
| Terr   | MODIS   | N         | Land cover   | This may prove       |
| a/Aqua |         | ASA/GIMMS | type         | useful to            |
|        |         |           |              | ArboNET/PSS          |
|        |         |           |              | identification of    |
|        |         |           |              | rodent habitat, but  |
|        |         |           |              | there is no          |
|        |         |           |              | documented           |
|        |         |           |              | requirement for this |
|        |         |           |              | MODIS Standard       |
|        |         |           |              | Product. Uses        |
|        |         |           |              | Landsat for better   |
|        |         |           |              | interpretation.      |
+--------+---------+-----------+--------------+----------------------+
| GOES   | GOES    | NOAA      | Surface      | Hourly variation.    |
|        | Imager  |           | Temperature  | Project plans to     |
|        |         | N         |              | deliver GOES         |
|        |         | ASA/GIMMS |              | temperature products |
|        |         |           |              | using non-standard   |
|        |         |           |              | algorithms. This may |
|        |         |           |              | prove useful to      |
|        |         |           |              | identify survival of |
|        |         |           |              | fleas                |
+--------+---------+-----------+--------------+----------------------+

[]{#_Ref125223884 .anchor}**Table 4**. Potential NASA Inputs.

## Integrated System Solution

Figure 7 shows the approach for integrating remote sensing information
system being evaluated in this report and it is being used to integrate
NASA ESSD data and models into in CDC's ArboNET/PSS. Essential of this
system is the integration of the epidemiological surveillance provided
by CDC/NCID. The GSFC plague algorithm is being customized according to
the PSS operations to be more in-line with their CDC modeling systems.
Also, a MODIS-AVHRR NDVI datasets integration and other remote sensing
observation products are highlighted as part of the process. This work
has significant overlap with other federal agency decision support tools
and where possible we may readily provide significant support to USGS,
DoD, and NIH.

[]{#__RefHeading___Toc125226891 .anchor}**Figure .**
![](media/image8.png){width="6.604166666666667in"
height="5.135416666666667in"} Integrated system solution diagram for
incorporation of Earth-Sun Research results to enhance the Plague
Surveillance System component of CDC's ArboNET.

# Conclusions

A systematic review of the entire ESSD suite of measurements against the
ArboNET/PSS requirements revealed a number of current or planned ESSD
products that may add value as input requirements of the ArboNET/PSS.

Finally, concerns about data continuity could be mitigated in the
long-term by focusing integration efforts on ESSD systems that have
operational follow-on (such as the National Polar-orbiting Operational
Environmental Satellite System) and planned system continuity (such as
the Global Precipitation Measurement mission).

## Findings

The results of this evaluation demonstrate that NASA ESSD assets could
make important contributions to ArboNET/PSS. The GIMMS group, using ESSD
results, has implemented a progressive "prediction and prevention"
approach that could be used to issue an alert that environmental
conditions are conducive to plague outbreak which in turn can trigger
surveillance efforts for the area in question. The active participation
of the system's end users helps ensure that forecast information is
provided in a useful manner and that effective response measures are
developed. .

## Recommendations

This evaluation report underpins the assumption made by both agencies
when entering into this partnership -- that ESSD systems have the
potential to enhance and complement the plague "surveillance&response"
system used by the CDC with "prediction and prevention" strategies. The
overall ArboNET/PSS enhancement effort should continue to be managed
through a systems engineering approach so that the projects and products
selected for implementation will have passed through a rigorous
verification and validation process to ensure that ArboNET/PSS
requirements are being met and that the resulting improvements in the
system can be effectively benchmarked. The active participation of
ArboNET's stakeholders should continue so that data and products
produced by the NASA science community meet the data delivery and
continuity requirements of ArboNET/PSS. Response plans can be developed
with realistic expectations of timely dissemination of this information
to vulnerable groups. Still, a prudent strategy to reduce people's
overall vulnerability is to continue with strong public health measures
such as epidemiological surveillance and prevention measures
(vaccination programs).

Given the real possibility of a significant data interruption between
TRMM, the NASA/GSFC-led project should clearly define how ArboNET/PSS
would continue to receive optimum and consistent precipitation inputs
through such a period. Also, the GFSC-led project should evaluate the
feasibility of delivering preliminary TRMM precipitation products within
24 hours (almost real time).

## Next Steps

Having characterized ArboNET/PSS and itemized NASA's relevant inputs,
the next part of the systems engineering process is V&V. V&V must take
place for all potential enhancements to ArboNET/PSS. Work will continue
with the active participation of CDC researchers to verify/validate and
benchmark the integration of NASA products into ArboNET /PSS. As it is
recommended by NASA in its implementation of systems engineering
approaches, we will apply the Decision Support System (DSS) V&V paradigm
as a pyramid with the idea that V&V of a DSS must be "from the ground
up." Relevant documents[^1] will be reviewed by the team to implement
"benchmarking" procedures as defined by NASA. In these stages, the
features of simplicity, flexibility, acceptability and timeliness will
be addressed.

# References

Enscore, Rusell E, BJ Biggerstaff, TL Brown, RF Fulgham, PJ Reynolds, DM
Engelthaler, CE Levy, RR Parmenter, JA Montenieri, JE Cheek, RK
Grinnell, PL Ettestad, and KL Gage,. 2002. Modeling relationships
between climate and the frequency of human plague cases in the
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