Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models

  • Guo-Jing Yang Jiangsu Institute of Parasitic Diseases, Meiyuan Yangxiang 117, Wuxi, China.
  • Qi Gao Jiangsu Institute of Parasitic Diseases, Meiyuan Yangxiang 117, Wuxi, China.
  • Shui-Sen Zhou National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
  • John B. Malone Pathobiological Sciences, Skip Bertman Drive, Louisiana State University, Baton Rouge, LA, China.
  • Jennifer C. McCarroll Pathobiological Sciences, Skip Bertman Drive, Louisiana State University, Baton Rouge, LA, China.
  • Marcel Tanner Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Penelope Vounatsou Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Robert Bergquist Ingerod, Brastad, Switzerland.
  • Jürg Utzinger Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Xiao-Nong Zhou | ipdzhouxn@sh163.net National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.

Abstract

The purpose of this study was to deepen our understanding of Plasmodium vivax malaria transmission patterns in the People’s Republic of China (P.R. China). An integrated modeling approach was employed, combining biological and statistical models. A Delphi approach was used to determine environmental factors that govern malaria transmission. Key factors identified (i.e. temperature, rainfall and relative humidity) were utilized for subsequent mapping and modeling purposes. Yearly growing degree days, annual rainfall and effective yearly relative humidity were extracted from a 15-year time series (1981-1995) of daily environmental data readily available for 676 locations in P.R. China. A suite of eight multinomial regression models, ranging from the null model to a fully saturated one were constructed. Two different information criteria were used for model ranking, namely the corrected Akaike’s information criterion and the Bayesian information criterion. Mapping was based on model output data, facilitated by using ArcGIS software. Temperature was found to be the most important environmental factor, followed by rainfall and relative humidity in the Delphi evaluation. However, relative humidity was found to be more important than rainfall and temperature in the ranking list according to the three single environmental factor regression models. We conclude that the distribution of the mosquito vector is mainly related to relative humidity, which thus determines the extent of malaria transmission. However, in regions with relative humidity >60%, temperature is the major driver of malaria transmission intensity. By integrating biology-driven models with statistical regression models, reliable risk maps indicating the distribution of transmission and the intensity can be produced. In a next step, we propose to integrate social and health systems factors into our modeling approach, which should provide a platform for rigorous surveillance and monitoring progress towards P. vivax malaria elimination in P.R. China.

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Published
2010-11-01
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Original Articles
Keywords:
Plasmodium vivax, malaria transmission, environmental factor, biology-driven model, statistical model, People’s Republic of China
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How to Cite
Yang, G.-J., Gao, Q., Zhou, S.-S., Malone, J. B., McCarroll, J. C., Tanner, M., Vounatsou, P., Bergquist, R., Utzinger, J., & Zhou, X.-N. (2010). Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models. Geospatial Health, 5(1), 11-22. https://doi.org/10.4081/gh.2010.183

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