Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis
Abstract Background Clonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors. Methods We compiled i...
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2025-07-01
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| Series: | International Journal of Health Geographics |
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| Online Access: | https://doi.org/10.1186/s12942-025-00404-y |
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| author | Jin-Xin Zheng Hui‐Hui Zhu Shang Xia Men‐Bao Qian Robert Bergquist Hung Manh Nguyen Xiao‐Nong Zhou |
| author_facet | Jin-Xin Zheng Hui‐Hui Zhu Shang Xia Men‐Bao Qian Robert Bergquist Hung Manh Nguyen Xiao‐Nong Zhou |
| author_sort | Jin-Xin Zheng |
| collection | DOAJ |
| description | Abstract Background Clonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors. Methods We compiled infection occurrence data from systematic literature reviews and national surveys conducted between 2000 and 2018. Environmental and climatic predictors were obtained from long-term raster datasets. Machine learning models, including logistic regression and tree-based ensemble methods, were used to assess associations between predictor variables and C. sinensis presence. Partial dependence plots were employed to refine predictor selection and explore marginal effects. Results Raw freshwater fish consumption was identified as the most influential predictor. In Guangxi, 54.9% of counties reported raw fish consumption, compared to 31.7% in Vietnam. Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. High-risk zones were concentrated along the China–Vietnam border, suggesting the need for geographically targeted interventions. Conclusion The findings underscore the combined influence of ecological and behavioral factors on C. sinensis transmission. The predictive modeling framework offers valuable insights for surveillance planning and cross-border disease control, reinforcing the role of ecological epidemiology in guiding parasitic disease prevention strategies. Graphical Abstract |
| format | Article |
| id | doaj-art-463adcf63867438f80bbed93edda1edf |
| institution | Kabale University |
| issn | 1476-072X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Health Geographics |
| spelling | doaj-art-463adcf63867438f80bbed93edda1edf2025-08-20T03:46:16ZengBMCInternational Journal of Health Geographics1476-072X2025-07-0124111310.1186/s12942-025-00404-yEcological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysisJin-Xin Zheng0Hui‐Hui Zhu1Shang Xia2Men‐Bao Qian3Robert Bergquist4Hung Manh Nguyen5Xiao‐Nong Zhou6School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of MedicineNational Institute of Parasitic Diseases at Chinese Centre for Disease Control and Prevention (Chinese Centre for Tropical Diseases Research), National Health Commission of the People’s Republic of China (NHC) Key Laboratory of Parasite and Vector Biology, World Health Organization (WHO) Collaborating Centre for Tropical Diseases, National Centre for International Research On Tropical Diseases of the Chinese Ministry of Science and TechnologyNational Institute of Parasitic Diseases at Chinese Centre for Disease Control and Prevention (Chinese Centre for Tropical Diseases Research), National Health Commission of the People’s Republic of China (NHC) Key Laboratory of Parasite and Vector Biology, World Health Organization (WHO) Collaborating Centre for Tropical Diseases, National Centre for International Research On Tropical Diseases of the Chinese Ministry of Science and TechnologyNational Institute of Parasitic Diseases at Chinese Centre for Disease Control and Prevention (Chinese Centre for Tropical Diseases Research), National Health Commission of the People’s Republic of China (NHC) Key Laboratory of Parasite and Vector Biology, World Health Organization (WHO) Collaborating Centre for Tropical Diseases, National Centre for International Research On Tropical Diseases of the Chinese Ministry of Science and TechnologyIngerod, Brastad, Sweden/formerly with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), World Health OrganizationInstitute of Biology, Vietnam Academy of Science and TechnologySchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of MedicineAbstract Background Clonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors. Methods We compiled infection occurrence data from systematic literature reviews and national surveys conducted between 2000 and 2018. Environmental and climatic predictors were obtained from long-term raster datasets. Machine learning models, including logistic regression and tree-based ensemble methods, were used to assess associations between predictor variables and C. sinensis presence. Partial dependence plots were employed to refine predictor selection and explore marginal effects. Results Raw freshwater fish consumption was identified as the most influential predictor. In Guangxi, 54.9% of counties reported raw fish consumption, compared to 31.7% in Vietnam. Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. High-risk zones were concentrated along the China–Vietnam border, suggesting the need for geographically targeted interventions. Conclusion The findings underscore the combined influence of ecological and behavioral factors on C. sinensis transmission. The predictive modeling framework offers valuable insights for surveillance planning and cross-border disease control, reinforcing the role of ecological epidemiology in guiding parasitic disease prevention strategies. Graphical Abstracthttps://doi.org/10.1186/s12942-025-00404-yClonorchis sinensisSocio-cultural factorEnvironmental healthMachine learningEcological epidemiologySpatial prediction |
| spellingShingle | Jin-Xin Zheng Hui‐Hui Zhu Shang Xia Men‐Bao Qian Robert Bergquist Hung Manh Nguyen Xiao‐Nong Zhou Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis International Journal of Health Geographics Clonorchis sinensis Socio-cultural factor Environmental health Machine learning Ecological epidemiology Spatial prediction |
| title | Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis |
| title_full | Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis |
| title_fullStr | Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis |
| title_full_unstemmed | Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis |
| title_short | Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis |
| title_sort | ecological epidemiology insights into clonorchiosis endemicity in guangxi china and vietnam a comprehensive machine learning analysis |
| topic | Clonorchis sinensis Socio-cultural factor Environmental health Machine learning Ecological epidemiology Spatial prediction |
| url | https://doi.org/10.1186/s12942-025-00404-y |
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