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Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-0423-6702
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2019 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 28, no 4, p. 1126-1140Article in journal (Refereed) Published
Abstract [en]

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.

Place, publisher, year, edition, pages
2019. Vol. 28, no 4, p. 1126-1140
Keywords [en]
Bayesian variable selection, food-borne disease outbreaks, misclassification, missing value imputation, spike and slab prior
National Category
Public Health, Global Health, Social Medicine and Epidemiology Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-164846DOI: 10.1177/0962280217747311ISI: 000463234000010OAI: oai:DiVA.org:su-164846DiVA, id: diva2:1280525
Projects
Statistical Modelling, Monitoring and Predictive Analytics against Infectious Disease Outbreaks
Funder
Swedish Research Council, 2015_05182_VRAvailable from: 2019-01-19 Created: 2019-01-19 Last updated: 2019-05-02Bibliographically approved

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