基于生态位模型的云南省血吸虫病传播风险探测研究

胡小康; 郝瑜婉; 夏尚; 郭云海; 薛靖波; 张云; 王丽芳; 董毅; 许静; 李石柱

doi:10.12140/j.issn.1000-7423.2020.01.012

中国寄生虫学与寄生虫病杂志 >

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

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

论著

基于生态位模型的云南省血吸虫病传播风险探测研究

胡小康 ,
郝瑜婉 ,
夏尚 ,
郭云海 ,
薛靖波 ,
张云 ,
王丽芳 ,
董毅 ,
许静 ,
李石柱

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1 中国疾病预防控制中心寄生虫病预防控制所,国家热带病研究中心,世界卫生组织热带病合作中心,科技部国家级热带病国际联合研究中心,卫生部寄生虫病原与媒介生物学重点实验室,上海 200025;
2 云南省地方病防治所,大理 671000

胡小康（1993-）,男,硕士研究生,从事流行病学研究。E-mail: guyuexihe@163.com

收稿日期: 2019-09-18

网络出版日期: 2020-03-19

基金资助

国家传染病重大专项（No. 2016ZX10004222-004,No. 2018ZX10101002-002）; 上海市公共卫生三年行动计划（No. GWIV-29）

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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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摘要

目的基于生态位模型预测云南省血吸虫病传播风险,识别高风险区分布,为制定监测与防控措施提供依据。方法收集2004-2015年云南省18个血吸虫病流行县以村为单位的血吸虫病疫情资料和气候、地理及社会经济等13个环境变量数据,采用BIOCLIM、DOMAIN和MaxEnt等3种生态位模型构建血吸虫病传播风险探测模型,并利用受试者工作特征曲线下面积（AUC）评价模型精度。选择最佳模型分析环境变量的重要性,并预测云南省血吸虫病传播风险分布情况。结果构建的3种生态位模型均能较好地预测云南省血吸虫病传播风险的分布,其中MaxEnt模型的预测准确度最高（AUC为0.96 ± 0.01）,其次为DOMAIN模型（AUC为0.93 ± 0.04）,再次为BIOCLIM模型（AUC为0.88 ± 0.01）,三者间差异有统计学意义（P < 0.05）。采用MaxEnt模型分析结果显示,影响血吸虫病分布的最主要环境因子为年平均降水量（贡献值为1.52）,其次为国内生产总值和人口密度（贡献值分别为1.06和1.03）。采用MaxEnt模型预测云南省血吸虫病传播风险分布结果显示,传播风险区占云南省面积的3.1%,主要集中在西北部地区,其中低风险区和中风险区占2.7%,高风险区面积占0.4%,主要分布在鹤庆县北部、洱源县东部、大理市中部、巍山彝族自治县东北部和弥渡县北部等地区。结论基于MaxEnt生态位模型预测血吸虫病传播风险分布是可行的。云南省血吸虫病传播风险仍存在,高风险区分布较为集中,需有针对性地开展监测和防控工作。

关键词： 生态位模型; 血吸虫病; 传播风险

本文引用格式

胡小康 , 郝瑜婉 , 夏尚 , 郭云海 , 薛靖波 , 张云 , 王丽芳 , 董毅 , 许静 , 李石柱 . 基于生态位模型的云南省血吸虫病传播风险探测研究[J]. 中国寄生虫学与寄生虫病杂志, 2020 , 38(1) : 80 -87 . DOI: 10.12140/j.issn.1000-7423.2020.01.012

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

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