{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:43:22Z","timestamp":1773344602590,"version":"3.50.1"},"reference-count":35,"publisher":"American Society of Tropical Medicine and Hygiene","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,3]]},"abstract":"<jats:title>ABSTRACT.<\/jats:title>\n<jats:p>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 <jats:italic>Schistosoma haematobium and Schistosoma mansoni<\/jats:italic>, 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 (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>) of 0.84 in predicting heavy-intensity prevalence for <jats:italic>S. mansoni<\/jats:italic>; and an MAE of 0.02, an RMSE of 0.04, and an <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> value of 0.81 for <jats:italic>S. haematobium<\/jats:italic>. 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.<\/jats:p>","DOI":"10.4269\/ajtmh.23-0751","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T15:00:28Z","timestamp":1716303628000},"page":"73-79","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030"],"prefix":"10.4269","volume":"111","author":[{"given":"Xinyue","family":"Chen","sequence":"first","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxu","family":"Le","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[{"name":"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, 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