{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T15:11:22Z","timestamp":1791040282271,"version":"4.1.0"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2006,3,23]],"date-time":"2006-03-23T00:00:00Z","timestamp":1143072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tropical Med Int Health"],"published-print":{"date-parts":[[2006,4]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p><jats:bold>Objective\u2002<\/jats:bold>To predict the spatial distributions of<jats:italic>Schistosoma haematobium<\/jats:italic>and<jats:italic>S. mansoni<\/jats:italic>infections to assist planning the implementation of mass distribution of praziquantel as part of an on\u2010going national control programme in Tanzania.<\/jats:p><jats:p><jats:bold>Methods\u2002<\/jats:bold>Bayesian geostatistical models were developed using parasitological data from 143 schools.<\/jats:p><jats:p><jats:bold>Results\u2002<\/jats:bold>In the<jats:italic>S. haematobium<\/jats:italic>models, although land surface temperature and rainfall were significant predictors of prevalence, they became non\u2010significant when spatial correlation was taken into account. In the<jats:italic>S. mansoni<\/jats:italic>models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for<jats:italic>S. haematobium<\/jats:italic>than for<jats:italic>S. mansoni<\/jats:italic>. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation.<\/jats:p><jats:p><jats:bold>Conclusion\u2002<\/jats:bold>Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.<\/jats:p>","DOI":"10.1111\/j.1365-3156.2006.01594.x","type":"journal-article","created":{"date-parts":[[2006,3,23]],"date-time":"2006-03-23T11:01:03Z","timestamp":1143111663000},"page":"490-503","source":"Crossref","is-referenced-by-count":161,"title":["Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania"],"prefix":"10.1111","volume":"11","author":[{"given":"Archie C. A.","family":"Clements","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas J. 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