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This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV\u2009+\u2009LR, IV\u2009+\u2009RF, IV\u2009+\u2009GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5\u00a0km from the waterway, annual average temperature of 11.5\u201319.0\u00a0\u00b0C, annual average rainfall of 1000\u20131550\u00a0mm. IV\u2009+\u2009GBM had the highest prediction effect (accuracy\u2009=\u20090.878, AUC\u2009=\u20090.902, F1\u2009=\u20090.920) compared with the other six models. The results of IV\u2009+\u2009GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphic Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s40249-021-00874-9","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T13:02:27Z","timestamp":1624798947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt"],"prefix":"10.1186","volume":"10","author":[{"given":"Yan-Feng","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling-Qian","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin-Long","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Juan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing-Bo","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3172-5218","authenticated-orcid":false,"given":"Shi-Zhu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"issue":"10","key":"874_CR1","doi-asserted-by":"publisher","first-page":"e1414","DOI":"10.1016\/S2214-109X(19)30346-8","volume":"7","author":"EY Li","year":"2019","unstructured":"Li EY, Gurarie D, Lo NC, Zhu X, King CH. 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