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Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm

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Rethinking neglected tropical disease prevalence survey design and analysis : a geospatial paradigm. / Diggle, Peter; Amoah, Benjamin; Fronterre, Claudio; Giorgi, Emanuele; Johnson, Olatunji.

In: Transactions of The Royal Society of Tropical Medicine and Hygiene, Vol. 115, No. 3, 30.03.2021, p. 208–210.

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@article{1b1da0503f52492c8ce124ec6adfce03,
title = "Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm",
abstract = "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.",
keywords = "elimination surveys, geospatial methods, predictive inference, prevalence mapping",
author = "Peter Diggle and Benjamin Amoah and Claudio Fronterre and Emanuele Giorgi and Olatunji Johnson",
note = "This is a pre-copy-editing, author-produced PDF of an article accepted for Publication in Transactions of Royal Society of Tropical Medicine & Hygiene following peer review. The definitive publisher-authenticated version Peter J Diggle, Benjamin Amoah, Claudio Fronterre, Emanuele Giorgi, Olatunji Johnson, Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm, Transactions of The Royal Society of Tropical Medicine and Hygiene, Volume 115, Issue 3, March 2021, Pages 208–210 is available online at: https://doi.org/10.1093/trstmh/trab020",
year = "2021",
month = mar,
day = "30",
doi = "10.1093/trstmh/trab020",
language = "English",
volume = "115",
pages = "208–210",
journal = "Transactions of The Royal Society of Tropical Medicine and Hygiene",
issn = "0035-9203",
publisher = "Oxford University Press Inc",
number = "3",

}

RIS

TY - JOUR

T1 - Rethinking neglected tropical disease prevalence survey design and analysis

T2 - a geospatial paradigm

AU - Diggle, Peter

AU - Amoah, Benjamin

AU - Fronterre, Claudio

AU - Giorgi, Emanuele

AU - Johnson, Olatunji

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for Publication in Transactions of Royal Society of Tropical Medicine & Hygiene following peer review. The definitive publisher-authenticated version Peter J Diggle, Benjamin Amoah, Claudio Fronterre, Emanuele Giorgi, Olatunji Johnson, Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm, Transactions of The Royal Society of Tropical Medicine and Hygiene, Volume 115, Issue 3, March 2021, Pages 208–210 is available online at: https://doi.org/10.1093/trstmh/trab020

PY - 2021/3/30

Y1 - 2021/3/30

N2 - 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.

AB - 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.

KW - elimination surveys

KW - geospatial methods

KW - predictive inference

KW - prevalence mapping

U2 - 10.1093/trstmh/trab020

DO - 10.1093/trstmh/trab020

M3 - Journal article

VL - 115

SP - 208

EP - 210

JO - Transactions of The Royal Society of Tropical Medicine and Hygiene

JF - Transactions of The Royal Society of Tropical Medicine and Hygiene

SN - 0035-9203

IS - 3

ER -