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Design and analysis of elimination surveys for neglected tropical diseases

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Design and analysis of elimination surveys for neglected tropical diseases. / Diggle, Peter; Fronterre, Claudio; Amoah, Benjamin et al.
In: Journal of Infectious Diseases, Vol. 21, No. Supplement_5, 15.06.2020, p. S554–S560.

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Diggle P, Fronterre C, Amoah B, Giorgi E, Stanton M. Design and analysis of elimination surveys for neglected tropical diseases. Journal of Infectious Diseases. 2020 Jun 15;21(Supplement_5):S554–S560. Epub 2020 Jan 13. doi: 10.1093/infdis/jiz554

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Diggle, Peter ; Fronterre, Claudio ; Amoah, Benjamin et al. / Design and analysis of elimination surveys for neglected tropical diseases. In: Journal of Infectious Diseases. 2020 ; Vol. 21, No. Supplement_5. pp. S554–S560.

Bibtex

@article{071b59006789431eb26b4f646394e2a8,
title = "Design and analysis of elimination surveys for neglected tropical diseases",
abstract = "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 requireincreasingly 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 toinvest 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{\textquoteright}s elimination status.",
keywords = "disease mapping, elimination surveys, geostatistics, neglected tropical diseases, predictions.",
author = "Peter Diggle and Claudio Fronterre and Benjamin Amoah and Emanuele Giorgi and Michelle Stanton",
year = "2020",
month = jun,
day = "15",
doi = "10.1093/infdis/jiz554",
language = "English",
volume = "21",
pages = "S554–S560",
journal = "Journal of Infectious Diseases",
issn = "0022-1899",
publisher = "Oxford University Press",
number = "Supplement_5",

}

RIS

TY - JOUR

T1 - Design and analysis of elimination surveys for neglected tropical diseases

AU - Diggle, Peter

AU - Fronterre, Claudio

AU - Amoah, Benjamin

AU - Giorgi, Emanuele

AU - Stanton, Michelle

PY - 2020/6/15

Y1 - 2020/6/15

N2 - 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 requireincreasingly 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 toinvest 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’s elimination status.

AB - 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 requireincreasingly 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 toinvest 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’s elimination status.

KW - disease mapping

KW - elimination surveys

KW - geostatistics

KW - neglected tropical diseases

KW - predictions.

U2 - 10.1093/infdis/jiz554

DO - 10.1093/infdis/jiz554

M3 - Journal article

VL - 21

SP - S554–S560

JO - Journal of Infectious Diseases

JF - Journal of Infectious Diseases

SN - 0022-1899

IS - Supplement_5

ER -