https://doi.org/10.4081/gh.2021.879
Spatial patterns of Plasmodium vivax transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China
Submitted: 9 March 2020
Accepted: 21 August 2020
Published: 12 March 2021
Accepted: 21 August 2020
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase.
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How to Cite
Zheng, J., Shi, B., Xia, S., Yang, G., & Zhou, X.-N. (2021). Spatial patterns of <em>Plasmodium vivax</em> transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China. Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.879
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