{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:23:15Z","timestamp":1790101395414,"version":"4.0.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"Supplement_5","license":[{"start":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T00:00:00Z","timestamp":1578873600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1184344"],"award-info":[{"award-number":["OPP1184344"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit\u2019s elimination status.<\/jats:p>","DOI":"10.1093\/infdis\/jiz554","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T19:18:01Z","timestamp":1571685481000},"page":"S554-S560","source":"Crossref","is-referenced-by-count":53,"title":["Design and Analysis of Elimination Surveys for Neglected Tropical Diseases"],"prefix":"10.1093","volume":"221","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6723-9727","authenticated-orcid":false,"given":"Claudio","family":"Fronterre","sequence":"first","affiliation":[{"name":"Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Amoah","sequence":"first","affiliation":[{"name":"Centre 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