CHINESE JOURNAL OF PARASITOLOGY AND PARASITIC DISEASES >

2020 , Vol. 38 >Issue 1: 80 - 87

DOI: https://doi.org/10.12140/j.issn.1000-7423.2020.01.012

ORIGINAL ARTICLES

Detection of schistosomiasis transmission risks in Yunnan Province based on ecological niche modeling

  • HU Xiao-kang ,
  • HAO Yu-wan ,
  • XIA Shang ,
  • GUO Yun-hai ,
  • XUE Jing-bo ,
  • ZHANG Yun ,
  • WANG Li-fang ,
  • DONG Yi ,
  • XU Jing ,
  • LI Shi-zhu
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  • 1 National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai 200025, China;
    2 Yunnan Institute of Endemic Diseases Control and Prevention, Dali 671000, China

Received date: 2019-09-18

  Online published: 2020-03-19

Supported by

Supported by the National Special Science and Technology Project for Major Infection Diseases of China (No. 2016ZX10004222-004 and No. 2018ZX10101002-002), and the Three-Year Public Health Action Plan of Shanghai, China (No. GWIV-29)

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Abstract

Objective To predict the transmission risks of schistosomiasis based on ecological niche modeling and identify high-risk areas, in order to provide scientific evidence for the formulation of monitoring and control measures in Yunnan Province. Methods Village-level schistosomiasis epidemic data and 13 environmental variables such as climatic, geographical and socioeconomic factors were collected from 18 endemic counties in Yunnan Province from 2004 to 2015. BIOCLIM, DOMAIN and MaxEnt models were used to predict the schistosomiasis transmission risks in Yunnan Province, and the accuracy of prediction was assessed with the receiver operating characteristic area under curve (AUC). The model with best performance was used to analyze the importance of environmental variables and predict the distributions of schistosomiasis transmission risks in Yunnan Province. Results All the three models had good performance in predicting the distributions of schistosomiasis transmission risks in Yunnan Province, with the MaxEnt model having the highest prediction accuracy (AUC, 0.96 ± 0.01), followed by DOMAIN (AUC, 0.93 ± 0.04) and BIOCLIM (AUC, 0.88 ± 0.01) (P < 0.05 among three). The MaxEnt model revealed the annual average precipitation as the most significant environmental factor influencing the distributions of schistosomiasis (contribution value, 1.52), followed by gross domestic product and population density (contribution values 1.06 and 1.03, respectively). As predicted by the MaxEnt model, the transmission risk area, which was located mainly in the northwest, accounted for 3.1% of the area of Yunnan Province, comprising 2.7% of middle- and low-risk areas and 0.4% of high-risk areas. The high-risk areas were mainly distributed in northern Heqing County, eastern Eryuan County, central Dali City, northeastern Weishan County and northern Midu County. Conclusion It is feasible to predict distributions of schistosomiasis transmission risks based on the MaxEnt model. There still remain risks of schistosomiasis transmission in Yunnan Province, and the distributions of high-risk regions show a pattern of clustering. Therefore, targeted monitoring and control is needed.

Key words： Ecological niche modeling; Schistosomiasis; Transmission risk

Cite this article

HU Xiao-kang , HAO Yu-wan , XIA Shang , GUO Yun-hai , XUE Jing-bo , ZHANG Yun , WANG Li-fang , DONG Yi , XU Jing , LI Shi-zhu . Detection of schistosomiasis transmission risks in Yunnan Province based on ecological niche modeling[J]. CHINESE JOURNAL OF PARASITOLOGY AND PARASITIC DISEASES, 2020 , 38(1) : 80 -87 . DOI: 10.12140/j.issn.1000-7423.2020.01.012

References

[1] Zhou XN, Bergquist R, Leonardo L, et al. Schistosomiasis japonica: control and research needs[J]. Adv Parasitol, 2010, 72: 145-178.
[2] Zhou XN.Intensifying the precision control to facilitate the progress towards schistosomiasis elimination in mountainous and hilly regions of China[J]. Chin J Schisto Control, 2019, 31(3): 229-230, 279. (in Chinese)
(周晓农. 加强精准防治推动山丘型地区消除血吸虫病进程[J]. 中国血吸虫病防治杂志, 2019, 31(3): 229-230, 279.)
[3] Zhang LJ, Xu ZM, Dai SM, et al. Endemic status of schistosomiasis in People’s Republic of China in 2017[J]. Chin J Schisto Control, 2018, 30(5): 481-488. (in Chinese)
(张利娟, 徐志敏, 戴思敏, 等. 2017年全国血吸虫病疫情通报[J]. 中国血吸虫病防治杂志, 2018, 30(5): 481-488.)
[4] Hao YW, Gao FH, Xue JB, et al. Spatial-temporal clustering analysis of schistosomiasis transmission risk in Yunnan Province from 2004 to 2015[J]. Chin J Schisto Control, 2019, 31(3): 269-274, 279. (in Chinese)
(郝瑜婉, 高风华, 薛靖波, 等. 2004-2015年云南省血吸虫病传播风险时空聚集性分析[J]. 中国血吸虫病防治杂志, 2019, 31(3): 269-274, 279.)
[5] Wang SP, He X, Zhou YF.Demand for and the development of detection techniques for source of schistosome infection in China[J]. Chin J Parasitol Parasit Dis, 2015, 33(6): 456-460. (in Chinese)
(汪世平, 何鑫, 周云飞. 我国血吸虫病传染源快速检测技术的需求与发展[J]. 中国寄生虫学与寄生虫病杂志, 2015, 33(6): 456-460.)
[6] Yang K, Li SZ.Application of big data mining technology in monitoring and early-warning of schistosomiasis[J]. Chin J Parasitol Parasit Dis, 2015, 33(6): 461-465. (in Chinese)
(杨坤, 李石柱. 大数据挖掘技术应用于血吸虫病监测预警研究的探讨[J]. 中国寄生虫学与寄生虫病杂志, 2015, 33(6): 461-465.)
[7] Xu J, Yang K, Li SZ, et al. Surveillance system after transmission control of schistosomiasis in P.R. China[J]. Chin J Schisto Control, 2014, 26(1): 1-5. (in Chinese)
(许静, 杨坤, 李石柱, 等. 我国血吸虫病传播控制后的监测体系[J]. 中国血吸虫病防治杂志, 2014, 26(1): 1-5.)
[8] Zhu GP, Liu GQ, Bu WJ, et al. Ecological niche modeling and its applications in biodiversity conservation[J]. Biodiv Sci, 2013, 21(1): 90-98. (in Chinese)
(朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用[J]. 生物多样性, 2013, 21(1): 90-98.)
[9] Lin XL, Xiao H, Tian HY.The application of ecological niche modeling in the risk prediction of infection disease[J]. Chin J Prev Med, 2013, 47(4): 294-296. (in Chinese)
(林晓玲, 肖洪, 田怀玉. 生态位模型在传染病风险预测中的应用[J]. 中华预防医学杂志, 2013, 47(4): 294-296.)
[10] Busby JR.BIOCLIM: a bioclimate analysis and prediction system[J]. Plant Prot Q, 1991, 6(1): 8-9.
[11] Carpenter G, Gillison AN, Winter J.DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals[J]. Biodivers Conserv, 1993, 2(6): 667-680.
[12] Phillips SJ, Anderson RP, Schapire RE.Maximum entropy modeling of species geographic distributions[J]. Ecol Modell, 2006, 190(3): 231-259.
[13] Xia CC, Lu CF, Li S, et al. Maximum entropy model versus remote sensing-based methods for extract-ing Oncomelania hupensis snail habitats[J]. Chin J Schisto Control, 2017, 29(1): 12-17, 23. (in Chinese)
(夏聪聪, 卢成芳, 李思, 等. 基于最大熵模型和遥感的两种钉螺孳生地提取方法的比较[J]. 中国血吸虫病防治杂志, 2017, 29(1): 12-17, 23.)
[14] Liu C, White M, Newell G.Measuring and comparing the accuracy of species distribution models with presence-absence data[J]. Ecography, 2011, 34(2): 232-243.
[15] Wang YS, Xie BY, Wan FH, et al. Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models[J]. Biodiv Sci, 2007, 15(4): 365-372. (in Chinese)
(王运生, 谢丙炎, 万方浩, 等. ROC曲线分析在评价入侵物种分布模型中的应用[J]. 生物多样性, 2007, 15(4): 365-372.)
[16] Escobar LE, Craft ME.Advances and limitations of disease biogeography using ecological niche modeling[J]. Front Microbiol, 2016, 7: 1174.
[17] Peterson AT.Mapping disease transmission risk: enriching models using biogeography and ecology[M]. Baltimore: Johns Hopkins University Press, 2014: 36-43.
[18] Elith J, Graham CH, Anderson RP, et al. Novel methods improve prediction of species’ distributions from occurrence data[J]. Ecography, 2006, 29(2): 129-151.
[19] Xia C, Hu Y, Ward MP, et al. Identification of high-risk habitats of Oncomelania hupensis, the intermediate host of Schistosoma japonium in the Poyang Lake region, China: a spatial and ecological analysis[J]. PLoS Negl Trop Dis, 2019, 13(6): e0007386.
[20] Hao YW, Xue JB, Xu JF, et al. Analysis of schistosomiasis prevalence in endemic areas using retrospective space-time permutation scan statistics[J]. Chin J Parasitol Parasit Dis, 2017, 35(1): 68-74. (in Chinese)
(郝瑜婉, 薛靖波, 徐俊芳, 等. 基于回顾性重排扫描统计量的血吸虫病重点流行区时空聚集性分析[J]. 中国寄生虫学与寄生虫病杂志, 2017, 35(1): 68-74.)
[21] Lv C, Zhou LY, Xing XY, et al. Analysis of transmission risk factors of schistosomiasis in a hilly demonstration plot of transmission interruption[J]. Chin J Parasitol Parasit Dis, 2018, 36(4): 333-342. (in Chinese)
(吕超, 周理源, 幸小英, 等.山丘型血吸虫病传播阻断示范区血吸虫病传播高危风险因素分析[J]. 中国寄生虫学与寄生虫病杂志, 2018, 36(4): 333-342.)
[22] Shen MF, Dong Y, Feng XG, et al. Analysis of schistosomiasis endemic situation of national surveillance sites in Yunnan Province from 2005 to 2016[J]. Chin J Schisto Control, 2018, 30(3): 264-268. (in Chinese)
(沈美芬, 董毅, 冯锡光, 等. 2005-2016年云南省国家级血吸虫病监测点疫情分析[J]. 中国血吸虫病防治杂志, 2018, 30(3): 264-268.)
[23] Jin JN, Dang H, Zhang LJ, et al. Endemic situation at schistosomiasis surveillance sites in China in 2016[J]. Chin J Parasitol Parasit Dis, 2017, 35(6): 542-548. (in Chinese)
(金嘉宁, 党辉, 张利娟, 等. 2016年全国血吸虫病监测点疫情分析[J]. 中国寄生虫学与寄生虫病杂志, 2017, 35(6): 542-548.)
[24] Shen MF, Dong Y, Wu MS, et al. Oncomelania hupensis status in national schistosomiasis surveillance sites of Yunnan Province from 2015 to 2017[J]. Chin J Schisto Control, 2018, 30(5): 548-551. (in Chinese)
(沈美芬, 董毅, 吴明寿, 等. 2015-2017年云南省国家级血吸虫病监测点螺情监测结果分析[J]. 中国血吸虫病防治杂志, 2018, 30(5): 548-551.)
[25] Zhang Y, Feng XG, Xiong MT, et al. Investigation of wild feces pollution in schistosomiasis endemic areas in Yunnan Province[J]. Chin J Schisto Control, 2014, 26(4): 428-430. (in Chinese)
(张云, 冯锡光, 熊孟韬, 等. 云南省血吸虫病流行区野粪污染调查[J]. 中国血吸虫病防治杂志, 2014, 26(4): 428-430.)
[26] Shen MF, Feng XG, Zhang Y, et al. Surveillance with sentinel mice in key water areas of schistosomiasis endemic regions in Yunnan Province, 2015[J]. Chin J Schisto Control, 2017, 29(2): 209-211. (in Chinese)
(沈美芬, 冯锡光, 张云, 等. 2015年云南省血吸虫病流行区重点水域哨鼠监测结果分析[J]. 中国血吸虫病防治杂志, 2017, 29(2): 209-211.)
[27] Zhen H, Li SZ, Cao CL, ,et al. Surveilance Surveilance and response for schistosomiasis japonica based on sentinel mice examination for cercariae-infested water in risk region, 2012[J]. Chin J Parasitol Parasit Dis, 2013, 31(6): 428, 432. (in Chinese)
(郑浩, 李石柱, 曹淳力, 等. 2012年基于感染性水体哨鼠监测的血吸虫病风险区域预警[J]. 中国寄生虫学与寄生虫病杂志 , 2013, 31(6): 428, 432.)
[28] Li SZ, Luz A, Wang XH, et al. Schistosomiasis in China: acute infections during 2005-2008[J]. Chin Med J, 2009, 122(9): 1009-1014.
[29] Li SZ, Zheng H, Abe EM, et al. Reduction patterns of acute schistosomiasis in the People’s Republic of China[J]. PLoS Negl Trop Dis, 2014, 8(5): e2849-e2849.
[30] Zhang Y, Feng XG, Wu MS, et al. Current prevalence situation and control strategy of schistosomiasis in Yunnan Province[J]. Chin J Schisto Control, 2015, 27(6): 618-620. (in Chinese)
(张云, 冯锡光, 吴明寿, 等. 云南省血吸虫病流行现状及防控策略探讨[J]. 中国血吸虫病防治杂志, 2015, 27(6): 618-620.)
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