Estimating cholera risk from an exploratory analysis of its association with satellite-derived land surface temperatures

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Occurrence and growth of Vibrio cholerae, the causative agent of cholera, is linked to modalities of elevated temperatures and heavy precipitation. Previous studies have employed temperature- and satellite-derived precipitation data to determine the risk of cholera, but predictions were limited because of the coarse spatial resolution of temperature data (about 50 km). Cholera estimation has a severe impact on those in vulnerable regions with marginal civil infrastructure and those suffering additional damage after a natural disaster. In this study, a new remote-sensing data-based algorithm is proposed that includes a pathway to associate coarse-resolution cholera prediction with high-resolution land surface temperature (LST) dataset. The algorithm allows identification and prediction of regions with elevated risk of cholera at least four weeks in advance. Additionally, it employs a hierarchical structure comprising long-term anomalous LST values to determine hot spots of potential Vibrio cholerae. The algorithm was tested in five cholera epidemic regions of Sub-Saharan Africa (Mozambique, Central African Republic, Cameroon, South Sudan, and Rwanda), with realistic accuracy in demarcating regions where human cholera cases had been reported.

Publication Title, e.g., Journal

International Journal of Remote Sensing