Prediction of hookworm prevalence in southern India using environmental parameters derived from Landsat 8 remotely sensed data

Abstract

Soil-transmitted helminth (STH) infections propagate poverty and slow economic growth in low-income countries. As with many other neglected tropical diseases, environmental conditions are important determinants of STH transmission. Hence, remotely sensed (RS) data are commonly utilized in spatial risk models intended to inform control strategies. In the present study, we build upon the existing modelling approaches by utilizing fine spatial resolution Landsat 8 RS data in combination with topographic variables to predict hookworm prevalence in a hilly tribal area in southern India. Hookworm prevalence data collected from two field surveys were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from two RS images acquired during dry and rainy seasons. A variable buffer radius (100–1,000 m) was applied to the point-prevalence locations in order to integrate environmental conditions around the village centroids into the modelling approach and understand where transmission is more likely. Elevation and slope were the most important variables in the models, with lower elevation and higher slope correlating with higher transmission risk. A modified normalized difference water index was among other recurring important variables, likely responsible for some seasonal differences in model performance. The 300 m buffer distance produced the best model performance in this setting, with another spike at 700 m, and a marked drop-off in R 2 values at 1,000 m. In addition to assessing a large number of environmental correlates with hookworm transmission, the study contributes to the development of standardized methods of spatial linkage of continuous environmental data with point-based disease prevalence measures for the purpose of spatially explicit risk profiling.