Final published version
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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 - 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 -