Using Remotely Sensed Data to Examine Reported 2011 Malaria Cases for Children under 5 in India

The goal of this research was to determine whether climatic and environmental variables had a relationship with reported 2011 malaria cases for children under 5 in India using remotely sensed data and spatial statistics. Past research has identified temperature, vegetation, and rainfall as factors that influence malaria incidences. This research utilized remotely sensed data to derive land surface temperature (LST), normalized difference vegetation index (NDVI), evapotranspiration (Eta), rainfall, and elevation data to examine the distribution of reported 2011 malaria cases for children under 5 in India. Statistical analysis methods such as ordinary least squares (OLS), Spatial Lag, Spatial Error, and Geographically Weighted Regression (GWR) were conducted to determine relationships between the remotely sensed and malaria incidences data. The results indicated that the selected climatic and environmental variables could explain the variability of reported 2011 malaria cases for children under 5 in Eastern, upper Northeastern, and eastern Central India regions, which are high malaria transmission regions. The local coefficients of the GWR model showed that NDVI was the strongest predictor of malaria incidences. However, the results from the GWR model revealed that NDVI was the only variable that had a positive relationship with malaria incidences in Northeastern India, while as LST, Eta, rainfall, and elevation had positive relationships with malaria incidences in Eastern and eastern Central India regions. The results suggest that different high malaria transmission regions in India had different explanations for reported 2011 malaria cases for children under 5 based on climatic and environmental variables. The findings from this research reinforce the idea that malaria cases are influenced by environmental and climatic factors.