Early Prediction of Malaria in Forest Hills of Bangladesh Using AVHRR Based Satellite Data

The control of epidemic malaria is a priority for the international health community and specific target for the early detection and effective control of epidemics. Malaria has reemerged as a major public health problem in the world during past few years. This study attempts to identify the potential factors for malaria epidemic in forest hills in Bangladesh. It estimates the correlation between various environmental factors that contribute to malaria transmission and shows the application of remote sensing data for improved predictions of malaria epidemics in Bandarban district of Bangladesh which has the highest frequency of malaria cases in the country. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 14 years ( 1992-2005) from Advance Very High Resolution Radiometer (AVHRR) data flown on NOAA afternoon polar orbiting satellite. The weekly VH indices were correlated with the epidemiological data. A good correlation was found between malaria cases and TCI characterizing thermal condition during the month of August and September. Following the results of correlation analysis the principal components regression (PCR) method was used 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 the estimates of malaria is less than 10%. Remote sensing therefore is a valuable tool for estimating malaria well in advance thus preventive measures can be taken.