Abstract
Cholera, an acute waterborne diarrheal disease, remains a major global health challenge. Despite being curable and preventable, it can be fatal if left untreated, especially for children. Bangladesh, a cholera-endemic country with a high disease burden, experiences two peaks annually, during the dry pre-monsoon spring and the wet post-monsoon fall seasons. An early warning system for disseminating cholera risk, which has potential to reduce the disease burden, currently does not exist in Bangladesh. Such systems can raise timely awareness and allow households in rural, riverine areas like Matlab to make behavioral adjustments with water usage and around water resources to reduce contracting and transmitting cholera. Current dissemination approaches typically target local government and public health organizations; however, the vulnerable rural populations largely remain outside the information chain. Here, we develop and evaluate the accuracy of an early warning system-CholeraMap that uses high-resolution earth observations to forecast cholera risk and disseminate geocoded risk maps directly to Matlab's population via a mobile smartphone application. Instead of relying on difficult to obtain station-based environmental and hydroclimatological data, this study offers a new opportunity to use remote sensing data sets for designing and operating a disease early warning system. CholeraMap delivers monthly, color-coded geospatial maps (1 km × 1 km spatial resolution) with household and community cholera risk information. Our results demonstrate that the satellite-derived local-scale risk model satisfactorily captured the seasonal cholera pattern for the Matlab region, and a detailed high-resolution picture of the spatial progression of at-risk areas during outbreak months.