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.