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Existing statistical methods to combine prevalence data from multiple diagnostics ignore the potential overdispersion induced by the spatial correlations in the data. To address this issue, we develop a geostatistical framework that allows for joint modelling of data from multiple diagnostics by considering two main classes of inferential problems: (a) to predict prevalence for a gold-standard diagnostic using low-cost and potentially biased alternative tests; (b) to carry out joint prediction of prevalence from multiple tests. We apply the proposed framework to two case studies: mapping Loa loa prevalence in Central and West Africa, using miscroscopy, and a questionnaire-based test called RAPLOA; mapping Plasmodium falciparum malaria prevalence in the highlands of Western Kenya using polymerase chain reaction and a rapid diagnostic test. We also develop a Monte Carlo procedure based on the variogram in order to identify parsimonious geostatistical models that are compatible with the data. Our study highlights (a) the importance of accounting for diagnostic-specific residual spatial variation and (b) the benefits accrued from joint geostatistical modelling so as to deliver more reliable and precise inferences on disease prevalence.<\/jats:p>","DOI":"10.1111\/biom.13142","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T10:01:43Z","timestamp":1566813703000},"page":"158-170","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Geostatistical Framework for Combining Spatially Referenced Disease Prevalence Data from Multiple Diagnostics"],"prefix":"10.1093","volume":"76","author":[{"given":"Benjamin","family":"Amoah","sequence":"first","affiliation":[{"name":"CHICAS Lancaster University Medical School , Lancaster ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter 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