Predicting Schistosoma Haematobium Risk Distribution in Zambia Using Geographic Information System (GIS) and Remote Sensing (RS) Technologies

Abstract

Introduction: This study explores the spatial distribution and climatic influences on schistosomiasis in Zambia using remote-sensed data, specifically focusing on the Normalized Difference Vegetation Index (NDVI) and maximum temperature (Tmax). Material and Methods: By analysing prevalence data over a decade (2002-2012) and employing Geographic Information System (GIS) and Remote Sensing (RS) technologies, we identified regions conducive to the transmission of the disease. Results: The findings revealed significant variability in schistosomiasis prevalence across Zambia. Climatic cut-offs for schistosomiasis transmission were established, highlighting NDVI values between 134 - 153 and Tmax ranges of 19.4 – 27.6°C as optimal for sustaining transmission. Conclusion: The study underscores the pressing need for comprehensive control strategies that extend beyond school-based treatments, addressing the broader community and targeting at-risk populations. Furthermore, the established climatic cut-offs provide a framework for future research and public health initiatives aimed at reducing the burden of schistosomiasis in Zambia. This work emphasizes the intricate relationship between climate, health interventions, and disease dynamics, calling for adaptive strategies in the face of climate change.