Use of Vegetation Health Data and PCR method for forecasting malaria in Gujarat, India

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Wednesday, 5 February 2014: 11:30 AM
Room C213 (The Georgia World Congress Center )
Mohammad Nizamuddin, NOAA/Coastal Resilience Networks, New York, NY; and K. A. Akhand, L. Roytman, K. Felix, and M. Goldberg

Malaria mortality is a major public health challenge in India, accounting for sizeable morbidity, mortality and economic loss. The control of epidemic is a priority for the international health community and specific target for the early detection and effective control of epidemics. Advanced Very High Resolution Radiometer (AVHRR)-based Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) have been used in detecting and monitoring malaria in Gujarat, India. This work analyzes a correlation between malaria cases and vegetation health indices derived from satellite remote sensing for a period of 11 years (1997-2007). Correlation analysis showed that years with a high Temperature Condition Index (TCI) tended to be high malaria incidences. Principal Component Regression (PCR) method was performed to construct a model to predict malaria as a function of the TCI computed for this period. The simulated results were compared with observed malaria statistics showing that the error of estimation of malaria is small. Remote sensing therefore is a valuable tool for forecasting malaria well in advance thus preventive measures can be taken.