长江流域日本血吸虫中间宿主蜗牛的侵染风险:通过空间再评估和随机森林方法改进结果。

IF 4.8 1区 医学 Q1 INFECTIOUS DISEASES
Jin-Xin Zheng, Shang Xia, Shan Lv, Yi Zhang, Robert Bergquist, Xiao-Nong Zhou
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引用次数: 0

摘要

背景:日本钉螺(Oncomelania hupensis)是日本血吸虫的唯一中间宿主,其分布是血吸虫病监测的重要指标。本研究探讨了采用空间距离加权的随机森林算法对中国长江流域血吸虫病分布进行风险预测的可行性,旨在通过减少钉螺调查点的数量,在不损失预测准确性的前提下,为国家血吸虫病防治计划提供更精确的参考:方法:收集了2018年安徽、湖南、湖北、江西和江苏等省的有螺和无螺记录。开发了基于一组环境和气候变量的机器学习随机森林算法,以预测日本蜗牛的中间宿主O. hupensis的繁殖地。比较了六边形网格系统的不同空间大小,以估计所需蜗牛采样点的需求。通过计算 Kappa 和曲线下面积(AUC)估算了蜗牛采样点之间地理距离的预测准确性:结果:5 千米距离权重的准确度最高(AUC = 0.889,Kappa = 0.618)。与 O. hupensis 出没概率相关性最强的五个因子是(1)湖泊距离(48.9%);(2)河流距离(36.6%);(3)等温线(29.5%);(4)平均日温差(28.1%);(5)海拔(26.0%)。风险图显示,蜗牛肆虐的区域主要分布在长江沿线,其中安徽长江中下游的分水岭、缓流河段的蜗牛肆虐概率最高,其次是中国两大湖泊--湖南和湖北的洞庭湖以及江西的鄱阳湖附近地区:结论:应用随机森林算法的机器学习方法可以精确预测钉螺感染概率,从而提高中国血吸虫监测系统的灵敏度。重新设计长江流域钉螺监测系统,对钉螺侵染进行空间偏差校正,将需要调查的地点从 2369 个减少到 1747 个。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach.

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach.

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach.

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach.

Background: Oncomelania hupensis is only intermediate snail host of Schistosoma japonicum, and distribution of O. hupensis is an important indicator for the surveillance of schistosomiasis. This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China, with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.

Methods: The snail presence and absence records were collected from Anhui, Hunan, Hubei, Jiangxi and Jiangsu provinces in 2018. A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the O. hupensis intermediated snail host of S. japonicum. Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites. The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve (AUC).

Results: The highest accuracy (AUC = 0.889 and Kappa = 0.618) was achieved at the 5 km distance weight. The five factors with the strongest correlation to O. hupensis infestation probability were: (1) distance to lake (48.9%), (2) distance to river (36.6%), (3) isothermality (29.5%), (4) mean daily difference in temperature (28.1%), and (5) altitude (26.0%). The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River, with the highest probability in the dividing, slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui, followed by areas near the shores of China's two main lakes, the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.

Conclusions: Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability, an approach that could improve the sensitivity of the Chinese schistosome surveillance system. Redesign of the snail surveillance system by spatial bias correction of O. hupensis infestation in the Yangtze River Basin to reduce the number of sites required to investigate from 2369 to 1747.

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来源期刊
Infectious Diseases of Poverty
Infectious Diseases of Poverty Medicine-Public Health, Environmental and Occupational Health
CiteScore
16.70
自引率
1.20%
发文量
368
审稿时长
13 weeks
期刊介绍: Infectious Diseases of Poverty is a peer-reviewed, open access journal that focuses on essential public health questions related to infectious diseases of poverty. It covers a wide range of topics and methods, including the biology of pathogens and vectors, diagnosis and detection, treatment and case management, epidemiology and modeling, zoonotic hosts and animal reservoirs, control strategies and implementation, new technologies, and their application. The journal also explores the impact of transdisciplinary or multisectoral approaches on health systems, ecohealth, environmental management, and innovative technologies. It aims to provide a platform for the exchange of research and ideas that can contribute to the improvement of public health in resource-limited settings. In summary, Infectious Diseases of Poverty aims to address the urgent challenges posed by infectious diseases in impoverished populations. By publishing high-quality research in various areas, the journal seeks to advance our understanding of these diseases and contribute to the development of effective strategies for prevention, diagnosis, and treatment.
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