30 ISPRS: Infectious and Vector-borne Diseases II
Tuesday, 27 August 2013
11:00 AM - 3:10 PM
Monroe
PREDICTIVE AID FOR SEASONAL, AVIAN AND PANDEMIC INFLUENZA AND ACUTE RESPIRATORY INFECTIONS USING REMOTE SENSING DATA
Paper number: 30-5
Program: Sessions
Time: 2:10 PM – 2:30 PM
Authors
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Radina P. SoebiyantoUniversities Space Research Association, Greenbelt, MD
Abstract
We used Earth observation data (MODIS, TRMM and ASTER), climate model output (GLDAS), weather station data, as well as epidemiological and socioeconomic data, to model disease transmission and outbreaks. Various mathematical and statistical techniques were used, including ARIMA, neural network, Hilbert-Huang empirical decomposition, and Markov Chain Monte Carlo method. For influenza in temperate and subtropical climates (e.g. United States and Hong Kong), we have shown that temperature is the essential predictor while rainfall is an important one in the subtropics. For avian influenza in Indonesia, proximity to roads, rivers or wet market were found to be important risk factors.
The U.S. National Strategy for Pandemic Influenza developed by the White House Homeland Security Council consists of three pillars -- preparedness and communication, surveillance and detection, and response and containment. The capabilities we are developing contribute to all three. The White House Office of Science and Technology Policy National Biosurveillance Science and Technology Roadmap considers remote sensing the most suitable tool for identifying environmental and climatic precursors to infectious disease outbreaks. Hence our approach is consistent with the recommendations of the Roadmap.