GIS and Remote Sensing

Biography

Biography: Samuel H. Hundessa

Abstract

Statement of the problem: 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. Few studies have reported spatial and temporal clustering of malaria in some provinces, but limited evidence is available at nationwide. A better understanding of the spatial and temporal characteristics of malaria is essential for consolidating the disease elimination programme. The purpose of this study is to understand the spatial and spatiotemporal distribution of Plasmodium vivax and Plasmodium falciparum malaria in China during 2005-2009. Methodology: Global Moran’s I statistics was applied in the Geographic Information System Software (ArcGIS) to explore spatial autocorrelation of county-level P. falciparum and P. vivax malaria. Spatial and space-time scan statistics were applied to detect spatial and spatiotemporal clusters, respectively and mapped in ArcGIS. Findings: 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 southwest to the southeast 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.