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Comprehensive Risk Assessment of Schistosomiasis Epidemic Based on Precise Identification of Oncomelania hupensis Breeding Grounds—A Case Study of Dongting Lake Area

Author

Listed:
  • Jun Xu

    (College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China)

  • Xiao Ouyang

    (Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha 410205, China)

  • Qingyun He

    (College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China)

  • Guoen Wei

    (College of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

Abstract

Spatio-temporal epidemic simulation, assessment, and risk monitoring serve as the core to establishing and improving the national public health emergency management system. In this study, we investigated Oncomelania hupensis breeding grounds and analyzed the locational and environmental preferences of snail breeding in Dongting Lake (DTL), Hunan, China. Using geographic information systems and remote sensing technology, we identified schistosomiasis risk areas and explored the factors affecting the occurrence and transmission of the disease. Several key conclusions were drawn. (1) From 2006 to 2016, the spatial change of potential O. hupensis breeding risk showed a diminishing trend from the eastern and northern regions to southwest DTL. Environmental changes in the eastern DTL region resulted in the lakeside and hydrophilic agglomerations of the O. hupensis populations. The shift in snail breeding grounds from a fragmented to centralized distribution indicates the weakening mobility of the O. hupensis population, the increasing independence of solitary groups, and the growing dependence of the snail population to the local environment. (2) The spatial risk distribution showed a descending gradient from west Dongting area to the east and an overall pattern of high in the periphery of large lakes and low in other areas. The cold-spot areas had their cores in Huarong County and Anxiang County and were scattered throughout the peripheral areas. The hot-spot areas had their center at Jinshi City, Nanxian County, and the southern part of Huarong County. The areas with increased comprehensive risks changed from centralized and large-scale development to fragmented shrinkage with increased partialization in the core area. The risk distribution’s center shifted to the northwest. The spatial risk distribution exhibited enhanced concentricity along the major axis and increased dispersion along the minor axis.

Suggested Citation

  • Jun Xu & Xiao Ouyang & Qingyun He & Guoen Wei, 2021. "Comprehensive Risk Assessment of Schistosomiasis Epidemic Based on Precise Identification of Oncomelania hupensis Breeding Grounds—A Case Study of Dongting Lake Area," IJERPH, MDPI, vol. 18(4), pages 1-20, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1950-:d:500870
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    References listed on IDEAS

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    1. Yingnan Niu & Rendong Li & Juan Qiu & Xingjian Xu & Duan Huang & Qihui Shao & Ying Cui, 2019. "Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis," IJERPH, MDPI, vol. 16(12), pages 1-17, June.
    2. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    3. Jun Yang & Jinxing Zhou & Jing Jin & Qixiang Sun, 2020. "The Stakeholders’ Views on Planting Trees to Control Schistosomiasis in China," IJERPH, MDPI, vol. 17(3), pages 1-14, February.
    4. Chester Kalinda & Moses Chimbari & Samson Mukaratirwa, 2017. "Implications of Changing Temperatures on the Growth, Fecundity and Survival of Intermediate Host Snails of Schistosomiasis: A Systematic Review," IJERPH, MDPI, vol. 14(1), pages 1-18, January.
    5. Xiao Ouyang & Zhenbo Wang & Xiang Zhu, 2019. "Construction of the Ecological Security Pattern of Urban Agglomeration under the Framework of Supply and Demand of Ecosystem Services Using Bayesian Network Machine Learning: Case Study of the Changsh," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
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