Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -