stephen r. yool, ph.d. associate professor geography and regional development...
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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
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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
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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
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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.
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Mapping/Modeling Needs Map Span a Large Geographic Areas
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Arizona’s Valley Fever Epidemic
Reported Arizona Coccidioidomycosis Cases
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1990 1992 1994 1996 1998 2000 2002 2004
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Coccidioides Life Cycle
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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
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Predicted
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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.)
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Late Summer MSI: Monsoonal Rains Promote Fungal Growth
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Arid Foresummer MSI: The Southwest is Dry, promoting
endosporulation
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Sample Moisture Stress Map
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Tucson length of moisture stress
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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.
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We deploy spaceborne sensors, such as this Advanced Very High Resolution Radiometer (AVHRR), which produces 1km pixels we use to map surface moisture dynamics
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What can the spectrum of vegetation tell us about surface
moisture?
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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)
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But how can you use an NDVI time series to measure moisture stress in highly diverse settings?
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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?
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• 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.