Stephen R. Yool, Ph.D. Associate Professor Geography and Regional Development 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

Coccidioides Life Cycle

Linking Precipitation and Dust to Incidence (Source: Comrie, 2005)

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 = [(X i - X MEAN ) / X SD ] Then, MSI = - [(NDVI i,j,t - NDVI MEAN ) / NDVI SD ] 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 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., Climate factors influencing coccidioidomycosis seasonality and outbreaks. Environmental Health Perspectives, doi: /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.