{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T10:57:20Z","timestamp":1788173840521,"version":"build-2803163510"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T00:00:00Z","timestamp":1571270400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012483","name":"University of Georgia Research Foundation, Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012483","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000865","name":"Bill & Melinda Gates Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Some villages, labeled \u201cpersistent hotspots (PHS),\u201d fail to respond adequately in regard to prevalence and intensity of infection to mass drug administration (MDA) for schistosomiasis. Early identification of PHS, for example, before initiating or after 1 or 2 years of MDA could help guide programmatic decision making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In a study with multiple rounds of MDA, data collected before the third MDA were used to predict PHS. We assessed 6 predictive approaches using data from before MDA and after 2 rounds of annual MDA from Kenya and Tanzania.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Generalized linear models with variable selection possessed relatively stable performance compared with tree-based methods. Models applied to Kenya data alone or combined data from Kenya and Tanzania could reach over 80% predictive accuracy, whereas predicting PHS for Tanzania was challenging. Models developed from one country and validated in another failed to achieve satisfactory performance. Several Year-3 variables were identified as key predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Statistical models applied to Year-3 data could help predict PHS and guide program decisions, with infection intensity, prevalence of heavy infections (\u2265400 eggs\/gram of feces), and total prevalence being particularly important factors. Additional studies including more variables and locations could help in developing generalizable models.<\/jats:p><\/jats:sec>","DOI":"10.1093\/infdis\/jiz529","type":"journal-article","created":{"date-parts":[[2019,10,11]],"date-time":"2019-10-11T03:49:41Z","timestamp":1570765781000},"page":"796-803","source":"Crossref","is-referenced-by-count":13,"title":["Modeling Approaches to Predicting Persistent Hotspots in SCORE Studies for Gaining Control of Schistosomiasis Mansoni in Kenya and Tanzania"],"prefix":"10.1093","volume":"221","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-0048","authenticated-orcid":false,"given":"Ye","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng-Hsuan","family":"Sung","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, 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