Remote sensing based predictive decision support system for assessment of environmental conditions for epidemic cholera

Cholera, a water borne diarrheal disease, is strongly associated with environmental processes. However, quantitative linkages of environmental processes with cholera and climate is still emerging. Linking diseases such as cholera with climate will aid in development of models that can predict spatial and temporal outbreaks in resource constrained regions of the globe. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of the disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera.;Conditions for occurrence of cholera were detectable at least one month in advance in several regions of Africa and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature and precipitation. In order to ascertain the integrity of the hypothesis, there are four epidemic regions were picked in Africa with different outbreak\u27s time: Mozambique, Central African Republic, Cameroon and Rwanda. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over selected areas