92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012: 2:30 PM
Estimating the Risks of Vectorborne Infectious Diseases and Acute Respiratory Infections Using Satellite Data
Room 333 (New Orleans Convention Center )
Richard Kiang, NASA, Greenbelt, MD; and R. Soebiyanto

The role of environment and climate in propagating infectious disease has long been recognized since the fifth century. The effect is particularly evident in vector-borne diseases such as malaria where temperature, precipitation and humidity influence the lifecycle of the pathogens and mosquitoes. At a time when environmental conditions are favorable for pathogens and vectors, disease outbreaks typically become more intense. Consequently, changes in the ecosystem whether it is man-made or due to global climate change would alter the spatiotemporal spread of the disease. In spite of advance of modern medicine, vector-borne and zoonotic infectious diseases remain a serious threat to human health. The appearance of new viral strains and drug-resistant micro-organisms present a difficult challenge for prevention and treatment.

Likewise, the transmissions of seasonal influenza, influenza-like illnesses and acute respiratory infections are often associated with climatic factors. As the epidemic pattern varies geographically, the roles of climatic factors may not be unique. Previous in vivo studies revealed the direct effect of winter-like humidity on air-borne influenza transmission that dominates in regions with temperate climate, while influenza in the warm regions is more effectively transmitted through direct contact. Influenza virus inherently undergoes rapid mutation that has the potential to bring about pandemic at any time. Hence understanding transmission pattern and capabilities to accurately project influenza cases can contribute to reducing the disease burden, as well as facilitating the preparedness effort.

In this paper, we will illustrate how we use satellite data for modeling the risks of malaria, dengue, avian influenza and seasonal influenza. Satellite data used for analyses and modeling include ASTER, MODIS, TRMM, SRTM and Ikonos.

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