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BioMed Central, Malaria Journal, 1(15), 2016

DOI: 10.1186/s12936-016-1646-2

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Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract Background Despite the declining burden of malaria in China, the disease remains a significant public health problem with periodic outbreaks and spatial variation across the country. A better understanding of the spatial and temporal characteristics of malaria is essential for consolidating the disease control and elimination programme. This study aims to understand the spatial and spatiotemporal distribution of Plasmodium vivax and Plasmodium falciparum malaria in China during 2005–2009. Methods Global Moran’s I statistics was used to detect a spatial distribution of local P. falciparum and P. vivax malaria at the county level. Spatial and space–time scan statistics were applied to detect spatial and spatiotemporal clusters, respectively. Results Both P. vivax and P. falciparum malaria showed spatial autocorrelation. The most likely spatial cluster of P. vivax was detected in northern Anhui province between 2005 and 2009, and western Yunnan province between 2010 and 2014. For P. falciparum, the clusters included several counties of western Yunnan province from 2005 to 2011, Guangxi from 2012 to 2013, and Anhui in 2014. The most likely space–time clusters of P. vivax malaria and P. falciparum malaria were detected in northern Anhui province and western Yunnan province, respectively, during 2005–2009. Conclusion The spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Contrary to P. vivax , the high-risk areas for P. falciparum malaria shifted from the west to the east of China. Further studies are required to examine the spatial changes in risk of malaria transmission and identify the underlying causes of elevated risk in the high-risk areas.