01614nas a2200217   4500000000100000008004100001260003400042653005700076653001700133653002400150653002100174100001400195700001200209700001700221700001300238700001400251245010300265856010400368520089900472022002501371       2021                            d  bOxford University Press (OUP)10aPublic Health, Environmental and Occupational Health10aParasitology10aInfectious Diseases10aGeneral Medicine1 aDiggle PJ1 aAmoah B1 aFronterrè C1 aGiorgi E1 aJohnson O00aRethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm  uhttps://academic.oup.com/trstmh/advance-article-pdf/doi/10.1093/trstmh/trab020/36264755/trab020.pdf3 a<jats:title>Abstract</jats:title>
               <jats:p>Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We set out an alternative, explicitly geospatial paradigm that can deliver much more precise estimates of the geospatial variation in prevalence over a country or region of interest. We describe the advantages of this approach under three headings: streamlining, whereby more precise results can be obtained with smaller sample sizes; integrating, whereby a joint analysis of data from two or more diseases can bring further gains in precision; and adapting, whereby the choice of future sampling location is informed by past data.</jats:p>  a0035-9203, 1878-3503