| Summary: | <p>Abstract</p> <p>Background</p> <p>Malaria incidence in China's Hainan province has dropped significantly, since Malaria Programme of China Global Fund Round 1 was launched. To lay a foundation for further studies to evaluate the efficacy of Malaria Programme and to help with public health planning and resource allocation in the future, the temporal and spatial variations of malaria epidemic are analysed and areas and seasons with a higher risk are identified at a fine geographic scale within a malaria endemic county in Hainan.</p> <p>Methods</p> <p>Malaria cases among the residents in each of 37 villages within hyper-endemic areas of Wanning county in southeast Hainan from 2005 to 2009 were geo-coded at village level based on residence once the patients were diagnosed. Based on data so obtained, purely temporal, purely spatial and space-time scan statistics and geographic information systems (GIS) were employed to identify clusters of time, space and space-time with elevated proportions of malaria cases.</p> <p>Results</p> <p>Purely temporal scan statistics suggested clusters in 2005,2006 and 2007 and no cluster in 2008 and 2009. Purely spatial clustering analyses pinpointed the most likely cluster as including three villages in 2005 and 2006 respectively, sixteen villages in 2007, nine villages in 2008, and five villages in 2009, and the south area of Nanqiao town as the most likely to have a significantly high occurrence of malaria. The space-time clustering analysis found the most likely cluster as including three villages in the south of Nanqiao town with a time frame from January 2005 to May 2007.</p> <p>Conclusions</p> <p>Even in a small traditional malaria endemic area, malaria incidence has a significant spatial and temporal heterogeneity on the finer spatial and temporal scales. The scan statistics enable the description of this spatiotemporal heterogeneity, helping with clarifying the epidemiology of malaria and prioritizing the resource assignment and investigation of malaria on a finer geographical scale in endemic areas.</p>
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