Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030

Xinyue Chen Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China

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Jiaxu Le Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China

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Yi Hu Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China

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The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indicator of schistosomiasis. We introduce several machine learning (ML) algorithms to predict infection intensity categories, using morbidity prevalence, with the aim of assessing the elimination of schistosomiasis in Africa, as outlined by the WHO. We obtained morbidity prevalence and infection intensity data from the Expanded Special Project to Eliminate Neglected Tropical Diseases, which spans 12 countries in sub-Saharan Africa. We then used a series of ML algorithms to predict the prevalence of infection intensity categories for Schistosoma haematobium and Schistosoma mansoni, with morbidity prevalence and several relevant environmental and demographic covariates from remote-sensing sources. The optimal model had high accuracy and stability; it achieved a mean absolute error (MAE) of 0.02, a root mean square error (RMSE) of 0.05, and a coefficient of determination (R2) of 0.84 in predicting heavy-intensity prevalence for S. mansoni; and an MAE of 0.02, an RMSE of 0.04, and an R2 value of 0.81 for S. haematobium. Based on this optimal model, we found that most areas in the surveyed countries have not achieved the target of the WHO road map for 2030. The ML algorithms used in our analysis showed a high overall predictive power in estimating infection intensity for each species, and our methods provided a low-cost, effective approach to evaluating the disease target in Africa set in the WHO road map for 2030.

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Author Notes

Financial support: This work is primarily being funded by the Shanghai New Three-year Action Plan for Public Health [GWVI-11.1-03] and the National Natural Science Foundation of China (81773487).

Disclosure: This study used surveys of Schistosoma infection derived from the ESPEN database. Because the data are aggregated and do not contain identifiable individual-level information, no specific ethics approval was needed for this study.

Authors’ contributions: Y. Hu conceived and designed the experiments, X. Chen analyzed the data, X. Chen and J. Le drafted the manuscript, and Y. Hu critically revised the manuscript for intellectual content. All authors read and approved the final version of the manuscript.

Data availability: All data used in this study are available on request from the corresponding author.

Authors’ addresses: Xinyue Chen, Jiaxu Le, and Yi Hu, Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China, Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China. E-mails: 22211020119@m.edu.fudan.cn, ljx9756@163.com, and huyi@fudan.edu.cn.

Address correspondence to Yi Hu, School of Public Health, Fudan University, No.130, Dong’an Road, Xuhui, Shanghai 200032, China. E-mail: huyi@fudan.edu.cn
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