2.3 Detection and monitoring vector-born deceases from AVHRR data

Monday, 24 January 2011: 4:30 PM
4C-2 (Washington State Convention Center)
Felix Kogan, NOAA/NESDIS, Camp Springs, MD; and A. Powell and M. Goldberg

Among vector born diseases several such as malaria, dengue fever, Lymphatic filariasis, and leishmamiasis create diseases burden in the world, because they are deadly and cover large area. For example, malaria occurs in 107 countries with a ½ of world population affected. Every year 300-500 million clinical cases of malaria occur with 1.5-3 million fatalities. Children, old people and pregnant women are the most vulnerable to malaria. Africa is the most affected continent, which contributes 60 % of global malaria cases and 80% of death. Malaria is strongly affected by the environment. Climate and ecosystems determines distribution of vector born diseases and weather affects timing, duration, area and intensity of outbreaks. In general warm and wet weather stimulated mosquitoes hatching, activity and the rate of diseases transmission to people. Such weather parameters as precipitation, temperature and relative humidity serves as the indicators of diseases and their development. However, weather station network is not dense enough especially in Africa to provide a decent tool for diseases monitoring. Therefore, satellite data have been used in recent years to monitor malaria and some other diseases. New Vegetation health (VH), techniques have been developed and applied successfully for early detect and monitor malaria from the operational environmental satellite. VH was developed from reflectance/emission measured by the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA polar-orbiting satellites since 1981. The calibrated measurements were converted to the Normalized Difference Vegetation Index (NDVI) and brightness temperature (BT), which were expressed as a deviation from 30-yeqr climatology. Three indices characterizing moisture (VCI), thermal (TCI) and vegetation health (VHI) conditions were produced and calibrated against in situ data. They were applied to identify malaria, dengue, and Rift Valley fever early enough to mitigate its consequences. These results covering several countries in Africa, Asia and South America will be presented.
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