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The Demographic and Health Surveys' Space-Time Analysis of HIV Hotspots and Clusters in Cameroon

Calvin Neel

 From 5.28% in 2004 to 2.8% in 2018, the prevalence of HIV infection in Cameroon has steadily dropped. However, this overall decline in occurrence could mask some inequalities, particularly in terms of location or pattern. Targeting hotspot locations is necessary for effective HIV control and prevention. The purpose of this study was to identify hotspot clusters and assess whether there is a geographical pattern of HIV in Cameroon. Following an accepted request from the MEASURES Demographic and Health Survey Program, HIV biomarker data were combined with Global Positioning System (GPS) location data from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS). Through the R package "D Cluster," the spatial autocorrelation test and the Moran I test were carried out. With SaTScan software 9.4 and solely spatial and space-time analyses, the discrete Poisson model was fitted to scan and find hot-spot clusters based on the Kulldorff test. In order to manipulate maps, the data and discovered clusters were finally loaded into QGIS software version 3.20.2. Despite the decline in HIV prevalence in Cameroon, the study found that hot-spot clusters and a geographical pattern of HIV were present. Public health policy in Cameroon should focus more on the clusters of HIV hotspots identified in this study while maintaining effective control in other areas of the nation that are cold spots as part of its mission to eradicate HIV infection by the year 2030.