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Cholera in the Lake Kivu region (DRC): Integrating remote sensing and spatially explicit epidemiological modeling

The risk, loss, and social disruption brought in by cholera outbreaks can hardly be overestimated and theThe risk, loss, and social disruption brought in by cholera outbreaks can hardly be overestimated and theglobal relevance of preventive assessments and controls of cholera spreading is manifest. The recent epidemicsin Haiti, the Congo river basin, Cuba, Sierra Leone, and the Sahel region [Luque Fernandez et al.,2009; Kelvin, 2011; Bompangue et al., 2011; Al-Tawfiq and Memish, 2012; Gaudart et al., 2013] witness theongoing, widespread inadequacy of reliable drinking water supply and sanitation infrastructure all over thedeveloping world. As a result, cholera remains a major cause of morbidity and mortality in developing countrieseven to date, despite all public health policies and humanitarian efforts deployed worldwide. As anexample, according to the World Health Organization, as much as 85% increase in the number of reportedcholera cases has been observed globally in 2011 relative to 2010, with 58 countries involved and a total of589, 854 yearly cases leading to an overall case fatality rate of 1.3% [World Health Organization, 2012].To promote reliable and timely preventive assessments and controls of cholera spreading, and to evaluateemergency management alternatives, two main modeling approaches have been followed. One approachconsists of predictive empirical models relying on environmental drivers which possibly influence the ecologyof Vibrio cholerae [Bouma and Pascual, 2001; Pascual et al., 2002; Lipp et al., 2002; Ruiz-Moreno et al.,2007; Matsuda et al., 2008], often using remotely acquired information [Lobitz et al., 2000; de Magny et al.,2008; Ford et al., 2009; Akanda et al., 2009; Jutla et al., 2010, 2013a, 2013b]. Such methods, suited in particularto regions where cholera is endemic but applied to predict other infectious disease outbreaks as well[Ford et al., 2009], have been shown to relate significant changes in remotely acquired optical signatures tointerannual and annual cyclic patterns of infections [de Magny et al., 2008; Emch et al., 2008; Matsuda et al.,2008; Jutla et al., 2013b]. For cholera, such signatures often consist of chlorophyll a, sea surface temperature.