Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models

Parasites & Vectors

Research

Feng Jiang¹  Jiangfan Yin¹  Yibiao Zhou¹  Yanfeng Gong¹  Qingwu Jiang¹  Ning Xu¹  Junhui Huang¹  Jiamin Wang¹  Yixin Tong¹  Honglin Jiang¹  Yue Chen²  Yun Zhang³  Jing Song³  Chunhong Du³  Yi Dong³ 

[1] Fudan University School of Public Health, 200032, Shanghai, China;Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, 200032, Shanghai, China;Fudan University Center for Tropical Disease Research, 200032, Shanghai, China;School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada;Yunnan Institute of Endemic Disease Control and Prevention, 671000, Dali, Yunnan, China;Yunnan Provincial Key Laboratory of Natural Focal Disease Prevention and Control Technology, 671000, Dali, Yunnan, China;

关键词: Schistosomiasis;    Oncomelania hupensis;    Species distribution;    Ecological niche model;    Climate change;   

DOI  :  10.1186/s13071-023-05952-5

 received in 2023-06-06, accepted in 2023-08-28,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control.MethodsData describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585).ResultsThe RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change.ConclusionsThis study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.Graphical Abstract

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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