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dc.contributorMateu, Jorge
dc.contributorCosta, Ana Cristina
dc.contributorPadgham, Mark
dc.contributor.authorAgbor, Ayuk Sally
dc.contributor.otherUniversitat Jaume I. Departament de Matemàtiques
dc.date.accessioned2016-04-18T09:18:41Z
dc.date.available2016-04-18T09:18:41Z
dc.date.issued2014-02
dc.identifier.urihttp://hdl.handle.net/10234/158849
dc.descriptionTreball Final del Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2013-2014ca_CA
dc.description.abstractGlobally, Cholera has been a major infectious disease due to its intercontinental, environmental and cultural factors. This study focused on evaluating the climatic and fresh water proximity factors influencing Cholera epidemic in Cameroon. To this effect, Cholera and climatic datasets in 2004, 2010, 2011 and 2012 to June 2013 were collected and mapped. Both high and low rainfall and temperature extremes were designated as promoters of V. Cholerae development and the highest cases were identified in the Littoral, Extreme North and Centre regions. Spatial autocorrelation using Local (Anselin) Moran I on Cholera cases revealed a cluster of Low-Low positive autocorrelation in Adamawa region in 2004, a High-High cluster of positive autocorrelation in the Littoral region and a Low-High negative autocorrelation in the South region in 2012, a Low-High negative autocorrelation in the South West region and a High-Low negative autocorrelation in the North West in 2013. Furthermore, using population numbers to count Cholera cases (prevalence) from 2010 to June 2013, Local Moran I results show a Low-Low cluster of positive autocorrelation in the South region, a Low-High negative autocorrelation in the North region and a High-Low negative autocorrelation in the Adamawa region in 2010, a High-Low negative spatial autocorrelation in the North region in 2011, a High-Low negative spatial autocorrelation in the South region in 2012 and a High-Low negative spatial autocorrelation in the North region in 2013. Spatial Poisson Regression analysis allowed concluding that Average Temperature, Distance to Streams, Population Distribution and Latitude are statistically significant predictors of increased Cholera cases, whereas Average Rainfall and Longitude are significant predictors of lower Cholera cases.ca_CA
dc.format.extent74 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherUniversitat Jaume Ica_CA
dc.subjectMàster Universitari Erasmus Mundus en Tecnologia Geoespacialca_CA
dc.subjectErasmus Mundus University Master's Degree in Geospatial Technologiesca_CA
dc.subjectMáster Universitario Erasmus Mundus en Tecnología Geoespacialca_CA
dc.subjectCameroonca_CA
dc.subjectcholeraca_CA
dc.subjectGISca_CA
dc.subjectLocal (Anselin) Moran Ica_CA
dc.subjectSpatial Poisson Regression analysisca_CA
dc.subjectRainfallca_CA
dc.subjectSpatial analysisca_CA
dc.subjectSpatial autocorrelationca_CA
dc.subjectTemperatureca_CA
dc.subjectTime series analysisca_CA
dc.titleUsing GIS to map the spatial and temporal occurrence of cholera epidemic in Cameroonca_CA
dc.typeinfo:eu-repo/semantics/masterThesisca_CA
dc.educationLevelEstudios de Postgradoca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA


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