Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis

Abstract Effective control of many diseases requires the accurate detection of infected individuals. Confidently ascertaining whether an individual is infected can be challenging when diagnostic tests are imperfect and when some individuals go for long periods of time without being observed or sampl...

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Autores principales:	Svetlana N. Buzdugan, Timothée Vergne, Vladimir Grosbois, Richard J. Delahay, Julian A. Drewe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6b571d79b5a14157af8e4c366ccf15dc
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spelling	oai:doaj.org-article:6b571d79b5a14157af8e4c366ccf15dc2021-12-02T12:32:04ZInference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis10.1038/s41598-017-00806-42045-2322https://doaj.org/article/6b571d79b5a14157af8e4c366ccf15dc2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00806-4https://doaj.org/toc/2045-2322Abstract Effective control of many diseases requires the accurate detection of infected individuals. Confidently ascertaining whether an individual is infected can be challenging when diagnostic tests are imperfect and when some individuals go for long periods of time without being observed or sampled. Here, we use a multi-event capture-recapture approach to model imperfect observations of true epidemiological states. We describe a method for interpreting potentially disparate results from individuals sampled multiple times over an extended period, using empirical data from a wild badger population naturally infected with Mycobacterium bovis as an example. We examine the effect of sex, capture history and current and historical diagnostic test results on the probability of being truly infected, given any combination of diagnostic test results. In doing so, we move diagnosis away from the traditional binary classification of apparently infected versus uninfected to a probability-based interpretation which is updated each time an individual is re-sampled. Our findings identified temporal variation in infection status and suggest that capture probability is influenced by year, season and infection status. This novel approach to combining ecological and epidemiological data may aid disease management decision-making by providing a framework for the integration of multiple diagnostic test data with other information.Svetlana N. BuzduganTimothée VergneVladimir GrosboisRichard J. DelahayJulian A. DreweNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Svetlana N. Buzdugan Timothée Vergne Vladimir Grosbois Richard J. Delahay Julian A. Drewe Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
description	Abstract Effective control of many diseases requires the accurate detection of infected individuals. Confidently ascertaining whether an individual is infected can be challenging when diagnostic tests are imperfect and when some individuals go for long periods of time without being observed or sampled. Here, we use a multi-event capture-recapture approach to model imperfect observations of true epidemiological states. We describe a method for interpreting potentially disparate results from individuals sampled multiple times over an extended period, using empirical data from a wild badger population naturally infected with Mycobacterium bovis as an example. We examine the effect of sex, capture history and current and historical diagnostic test results on the probability of being truly infected, given any combination of diagnostic test results. In doing so, we move diagnosis away from the traditional binary classification of apparently infected versus uninfected to a probability-based interpretation which is updated each time an individual is re-sampled. Our findings identified temporal variation in infection status and suggest that capture probability is influenced by year, season and infection status. This novel approach to combining ecological and epidemiological data may aid disease management decision-making by providing a framework for the integration of multiple diagnostic test data with other information.
format	article
author	Svetlana N. Buzdugan Timothée Vergne Vladimir Grosbois Richard J. Delahay Julian A. Drewe
author_facet	Svetlana N. Buzdugan Timothée Vergne Vladimir Grosbois Richard J. Delahay Julian A. Drewe
author_sort	Svetlana N. Buzdugan
title	Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
title_short	Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
title_full	Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
title_fullStr	Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
title_full_unstemmed	Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
title_sort	inference of the infection status of individuals using longitudinal testing data from cryptic populations: towards a probabilistic approach to diagnosis
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6b571d79b5a14157af8e4c366ccf15dc
work_keys_str_mv	AT svetlananbuzdugan inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT timotheevergne inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT vladimirgrosbois inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT richardjdelahay inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT julianadrewe inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis
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Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis

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