Elimination of malaria due to Plasmodium vivax in central part of the People's Republic of China: analysis and prediction based on modelling

Submitted: 9 December 2014
Accepted: 9 December 2014
Published: 1 November 2014
Abstract Views: 3222
PDF: 937
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Five provinces in central People's Republic of China (P.R. China) have successfully reduced the burden of malaria due to Plasmodium vivax in the last 7 years. The results of the Action Plan of China Malaria Elimination (APCME) that com- menced in 2010 are analysed against the background of the progress reached by the national malaria control programme (NMEP) that was launched in 2006. We examined the epidemiological changes in the number of autochthonous cases over time and discuss the feasibility of achieving the goal of malaria elimination by 2020. There was a total decline of 34,320 malaria cases between 2006 and 2012 arriving at an average annual incidence of 0.04 per 10,000 people by 2012. At the same time, the number of counties reporting autochthonous cases declined from 290 to 19. Spatial autocorrelation and Bayesian modelling were used to evaluate the datasets and predict the spatio-temporal pattern in the near future. The former approach showed that spatial clusters of P. vivax malaria existed in the study region during the study period, while the risk prediction map generated by the Bayesian model indicates that only sporadic malaria cases will appear during in the future. The results suggest that the initial NMEP approach and the follow-up APCME strategy have played a key role in reducing the threat of malaria in central P.R. China. However, to achieve the goal of malaria elimination by the end of the current decade, interven- tion plans must be adjusted with attention paid to those endemic counties still at risk according to the prediction map.

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Chen, Z., Shi, L., Zhou, X.-N., Xia, Z.-G., Bergquist, R., & Jiang, Q.-W. (2014). Elimination of malaria due to Plasmodium vivax in central part of the People’s Republic of China: analysis and prediction based on modelling. Geospatial Health, 9(1), 169–177. https://doi.org/10.4081/gh.2014.14

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