Prediction on the spatial distribution of the seropositive rate of schistosomiasis in Hunan Province, China: a machine learning model integrated with the Kriging method

• Published in: Parasitology research (1987) Vol. 123; no. 9; p. 316
• Main Authors: Xu, Ning, Cai, Yu, Tong, Yixin, Tang, Ling, Zhou, Yu, Gong, Yanfeng, Huang, Junhui, Wang, Jiamin, Chen, Yue, Jiang, Qingwu, Zheng, Mao, Zhou, Yibiao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024; Springer Nature B.V
• Subjects: Animals; Biomedical and Life Sciences; Biomedicine; China; China - epidemiology; gross domestic product; Humans; Immunology; kriging; lakes; Learning algorithms; Machine Learning; Medical Microbiology; Microbiology; Models, Statistical; prediction; Public health; rivers; Schistosomiasis; Schistosomiasis - epidemiology; Schistosomiasis - prevention & control; Seroepidemiologic Studies; seroprevalence; Spatial Analysis; Spatial distribution; surface temperature; wind speed; China; Hunan China; Schistosomiasis; Seropositive Rate; Machine Learning; Geostatistical method; Spatial Distribution
• DOI: 10.1007/s00436-024-08331-w
• ISSN: 0932-0113; 1432-1955

Schistosomiasis remains a formidable challenge to global public health. This study aims to predict the spatial distribution of schistosomiasis seropositive rates in Hunan Province, pinpointing high-risk transmission areas and advocating for tailored control measures in low-endemic regions. Six machine learning models and their corresponding hybrid machine learning-Kriging models were employed to predict the seropositive rate. The optimal model was selected through internal and external validations to simulate the spatial distribution of seropositive rates. Our results showed that the hybrid machine learning-Kriging model demonstrated superior predictive performance compared to basic machine learning model and the Cubist-Kriging model emerged as the most optimal model for this study. The predictive map revealed elevated seropositive rates around Dongting Lake and its waterways with significant clustering, notably in the central and northern regions of Yiyang City and the northeastern areas of Changde City. The model identified gross domestic product, annual average wind speed and the nearest distance from the river as the top three predictors of seropositive rates, with annual average daytime surface temperature contributing the least. In conclusion, our research has revealed that integrating the Kriging method significantly enhances the predictive performance of machine learning models. We developed a Cubist-Kriging model with high predictive performance to forecast the spatial distribution of schistosomiasis seropositive rates. These findings provide valuable guidance for the precise prevention and control of schistosomiasis.