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Stephen R. Yool, Ph.D.Associate Professor
Geography and Regional [email protected]
A Remote Sensing Concept for Mapping Parameters of
Infectious Disease
What do we need to model infectious disease?
• Solid theory or theories of causality
• Data and Methods at causal scale
• Unquenched thirst for knowledge
• Congenital sense of adventure
Do Satellite Data Support Infectious Disease Modeling?
General satellite data characteristics– Collected over long time scales– Collected at fine spatial scales– Collected over large geographic areas
The Valley Fever Example
Valley fever (coccidioidomycosis) is a disease endemic to arid regions in the Western Hemisphere, and is caused by the soil-dwelling fungi Coccidioides immitis and Coccidioides posadasii.
Arizona is currently experiencing an epidemic with almost 4000 cases annually, greatly exceeding other climate-related diseases such hantavirus or West Nile Virus.
Mapping/Modeling Needs Map Span a Large Geographic Areas
Arizona’s Valley Fever Epidemic
Reported Arizona Coccidioidomycosis Cases
0
500
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3500
4000
1990 1992 1994 1996 1998 2000 2002 2004
Coccidioides Life Cycle
Linking Precipitation and Dust to Incidence(Source: Comrie, 2005)
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1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003Year (Seasons)
Incid
en
ce (p
er
100,
000)
Observed
Predicted
The Moisture Stress Index (MSI)• By converting the NDVI value for each pixel into Z-score,
we produce for each pixel a Moisture Stress Index (MSI)—expressing the pixel’s distinctive moisture stress at specific time within the complete time series.
• The Z score represents the distance in standard deviations of a sample from its population mean
Z = [(Xi - XMEAN) / XSD]• Then, MSI = - [(NDVIi,j,t - NDVIMEAN) / NDVISD]So the MSI is a measure at a specific time of the distance in
standard deviations of a pixel’s moisture stress from its mean (average) moisture stress across that pixel’s complete time series.
(The negative sign inverts the values, so pixels with low scores get mapped as bright, moisture-stressed pixels.)
Late Summer MSI: Monsoonal Rains Promote Fungal Growth
Arid Foresummer MSI: The Southwest is Dry, promoting
endosporulation
Sample Moisture Stress Map
Tucson length of moisture stress
The Coccidioidomycosis Model
• Dispersion-related conditions are important predictors of coccidioidomycosis incidence during fall, winter and the arid foresummer.
• Comrie (2005)* reported precipitation during the normally arid foresummer 1.5-2 years prior to the season of exposure is the dominant predictor of the disease in all seasons, accounting for half of the overall variance.
* Comrie, A.C., 2005. Climate factors influencing coccidioidomycosis seasonality and outbreaks. Environmental Health Perspectives,
doi:10.1289/ehp.7786.
We deploy spaceborne sensors, such as this Advanced Very High Resolution Radiometer (AVHRR), which produces 1km pixels we use to map surface moisture dynamics
What can the spectrum of vegetation tell us about surface
moisture?
A Spectral Index of Moisture Stress
• Dry leaves show an increase in the red (Red) wavelengths and a decrease in the near-infrared (NIR) wavelengths
• We can represent this relationship as a Normalized Difference Vegetation Index (NDVI), which we can compute from spaceborne satellite data using this simple equation:
NDVI = (NIR – Red / NIR + Red)
But how can you use an NDVI time series to measure moisture stress in highly diverse settings?
Technology may be the answer, but what was the question?
• Will human societies on our planet promote actively the alliances between the natural and social sciences required to manage infectious disease effectively?
• Remote sensing empowers new and novel views of a world in which natural and human dimensions must co-exist.
• The multi-scale requirements of epidemiology and mapping technology can come together: To perceive unity in diversity, to focus on conflict resolution and consensus building—to move the process of disease hazard management forward.
