ArticleInfectious diseases of poverty2021
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.
Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Spatio-temporal disparities of Clonorchis sinensis infection in animal hosts in China: a systematic review and meta-analysis.Infectious diseases of poverty · 2023Pooled it
- Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019.Infectious diseases of poverty · 2023Pooled it
- Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand.Scientific reports · 2025Article
- Schistosoma japonicum in Ethiopia: Is there a need for tuning schistosomiasis surveillance in Africa?PLoS neglected tropical diseases · 2025Article
- Risk Prediction of Emerging Sites Infested with Schistosome-Transmitting Oncomelania hupensis in Shanghai, China.The American journal of tropical medicine and hygiene · 2025Article
- Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.Infectious Disease Modelling · 2024Article
- Article
- Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.PLoS neglected tropical diseases · 2024Article
- Natural variables separate the endemic areas of Clonorchis sinensis and Opisthorchis viverrini along a continuous, straight zone in Southeast Asia.Infectious diseases of poverty · 2024Article
- Predicting the potential nationwide distribution of the snail vector, Oncomelania hupensis quadrasi, in the Philippines using the MaxEnt algorithm.Parasitology research · 2023Article
- Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study.Parasites & vectors · 2023Article
- The patterns and driving forces of dengue invasions in China.Infectious diseases of poverty · 2023Article
- Short-, Mid-, and Long-Term Epidemiological and Economic Effects of the World Bank Loan Project on Schistosomiasis Control in the People's Republic of China.Diseases (Basel, Switzerland) · 2022Article
- Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China.Pathogens (Basel, Switzerland) · 2022Review
- Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China.Parasites & vectors · 2021Article
- Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.Frontiers in pharmacology · 2021Article
- Interpretable machine learning for predicting chronic kidney disease progression risk.Digital healthArticle
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6 authors at 4 institutions in 4 countries.
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Abstract
backgroundOncomelania 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.
methodsThe 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).
resultsThe 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.
conclusionsApplying 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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