Spatiotemporal distribution of malaria and the association between its epidemic and climate factors in Hainan, China

• Published in: Malaria Journal
• Main Authors: Li Lang, Wang Guangze, Xu Dezhong, Fang Liqun, Wang Shanqing, Long Yong, Xiao Dan, Cao Wuchun, Yan Yongping
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2010
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic; Armitage
• Online Access: https://doi.org/10.1186/1475-2875-9-185; https://doaj.org/article/55ae7087991e40a3ba383c4f13099384

Abstract Background Hainan is one of the provinces most severely affected by malaria epidemics in China. The distribution pattern and major determinant climate factors of malaria in this region have remained obscure, making it difficult to target countermeasures for malaria surveillance and control. This study detected the spatiotemporal distribution of malaria and explored the association between malaria epidemics and climate factors in Hainan. Methods The cumulative and annual malaria incidences of each county were calculated and mapped from 1995 to 2008 to show the spatial distribution of malaria in Hainan. The annual and monthly cumulative malaria incidences of the province between 1995 and 2008 were calculated and plotted to observe the annual and seasonal fluctuation. The Cochran-Armitage trend test was employed to explore the temporal trends in the annual malaria incidences. Cross correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on malaria transmission and the auto correlation of malaria incidence. A multivariate time series analysis was conducted to construct a model of climate factors to explore the association between malaria epidemics and climate factors. Results The highest malaria incidences were mainly distributed in the central-south counties of the province. A fluctuating but distinctly declining temporal trend of annual malaria incidences was identified (Cochran-Armitage trend test Z = -25.14, P < 0.05). The peak incidence period was May to October when nearly 70% of annual malaria cases were reported. The mean temperature of the previous month, of the previous two months and the number of cases during the previous month were included in the model. The model effectively explained the association between malaria epidemics and climate factors ( F = 85.06, P < 0.05, adjusted R 2 = 0.81). The autocorrelations of the fitting residuals were not significant ( P > 0.05), indicating that the model extracted information sufficiently. There was ...