Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea

  • Penny Masuoka | pmasuoka@usuhs.mil Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
  • Terry A. Klein 65th Medical Brigade/US Army MEDDAC-Korea, Unit 15281, APO, United States.
  • Heung-Chul Kim 5th Medical Detachment, 168th Multifunctional Medical Battalion, 65th Medical Brigade, Unit 15247, APO, United States.
  • David M. Claborn Center for Homeland Security, Missouri State University, Springfield, MO, United States.
  • Nicole Achee Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
  • Richard Andre Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
  • Judith Chamberlin Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
  • Jennifer Small Biospheric Sciences Branch, NASA Goddard Space Flight Center, Code 614.4, Greenbelt, MD, United States.
  • Assaf Anyamba Biospheric Sciences Branch, NASA Goddard Space Flight Center, Code 614.4, Greenbelt, MD, United States.
  • Dong-Kyu Lee Department of Health and Environment, Kosin University, Busan, Korea, Republic of.
  • Suk H. Yi 65th Medical Brigade/US Army MEDDAC-Korea, Unit 15281, APO, United States.
  • Michael Sardelis National Center for Medical Intelligence, Fort Detrick, MD, United States.
  • Young-Ran Ju Department of Arboviruses, Center for Immunology and Pathology, Korea National Institute of Health, Korea, Republic of.
  • John Grieco Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.

Abstract

Over 35,000 cases of Japanese encephalitis (JE) are reported worldwide each year. Culex tritaeniorhynchus is the primary vector of the JE virus, while wading birds are natural reservoirs and swine amplifying hosts. As part of a JE risk analysis, the ecological niche modeling programme, Maxent, was used to develop a predictive model for the distribution of Cx. tritaeniorhynchus in the Republic of Korea, using mosquito collection data, temperature, precipitation, elevation, land cover and the normalized difference vegetation index (NDVI). The resulting probability maps from the model were consistent with the known environmental limitations of the mosquito with low probabilities predicted for forest covered mountains. July minimum temperature and land cover were the most important variables in the model. Elevation, summer NDVI (July-September), precipitation in July, summer minimum temperature (May-August) and maximum temperature for fall and winter months also contributed to the model. Comparison of the Cx. tritaeniorhynchus model to the distribution of JE cases in the Republic of Korea from 2001 to 2009 showed that cases among a highly vaccinated Korean population were located in high-probability areas for Cx. tritaeniorhynchus. No recent JE cases were reported from the eastern coastline, where higher probabilities of mosquitoes were predicted, but where only small numbers of pigs are raised. The geographical distribution of reported JE cases corresponded closely with the predicted high-probability areas for Cx. tritaeniorhynchus, making the map a useful tool for health risk analysis that could be used for planning preventive public health measures.

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Published
2010-11-01
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Original Articles
Keywords:
Culex tritaeniorhynchus, geographical distribution, ecological niche modeling, Japanese encephalitis virus, Republic of Korea.
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How to Cite
Masuoka, P., Klein, T. A., Kim, H.-C., Claborn, D. M., Achee, N., Andre, R., Chamberlin, J., Small, J., Anyamba, A., Lee, D.-K., Yi, S. H., Sardelis, M., Ju, Y.-R., & Grieco, J. (2010). Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea. Geospatial Health, 5(1), 45-57. https://doi.org/10.4081/gh.2010.186