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• W2921695770 abstract "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." @default.
• W2921695770 created "2019-03-22" @default.
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• W2921695770 date "2019-03-05" @default.
• W2921695770 modified "2023-10-17" @default.
• W2921695770 title "Estimating cholera risk from an exploratory analysis of its association with satellite-derived land surface temperatures" @default.
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• W2921695770 doi "https://doi.org/10.1080/01431161.2019.1577575" @default.
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