Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis
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.
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Subjects: | L73 - Maladies des animaux, U10 - Informatique, mathématiques et statistiques, épidémiologie, blaireau, surveillance épidémiologique, Mycobacterium bovis, diagnostic, contrôle de maladies, variation saisonnière, maladie des animaux, http://aims.fao.org/aos/agrovoc/c_2615, http://aims.fao.org/aos/agrovoc/c_774, http://aims.fao.org/aos/agrovoc/c_16411, http://aims.fao.org/aos/agrovoc/c_12732, http://aims.fao.org/aos/agrovoc/c_2238, http://aims.fao.org/aos/agrovoc/c_2327, http://aims.fao.org/aos/agrovoc/c_24894, http://aims.fao.org/aos/agrovoc/c_426, |
Online Access: | http://agritrop.cirad.fr/585621/ http://agritrop.cirad.fr/585621/1/s41598-017-00806-4.pdf |
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dig-cirad-fr-5856212024-01-29T05:40:52Z http://agritrop.cirad.fr/585621/ http://agritrop.cirad.fr/585621/ Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis. Buzdugan Svetlana N., Vergne Timothée, Grosbois Vladimir, Delahay Richard J., Drewe Julian. 2017. Scientific Reports, 7:1111, 11 p.https://doi.org/10.1038/s41598-017-00806-4 <https://doi.org/10.1038/s41598-017-00806-4> Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis Buzdugan, Svetlana N. Vergne, Timothée Grosbois, Vladimir Delahay, Richard J. Drewe, Julian eng 2017 Scientific Reports L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/585621/1/s41598-017-00806-4.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1038/s41598-017-00806-4 10.1038/s41598-017-00806-4 info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-017-00806-4 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1038/s41598-017-00806-4 |
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L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 |
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L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 Buzdugan, Svetlana N. Vergne, Timothée Grosbois, Vladimir Delahay, Richard J. Drewe, Julian Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis |
description |
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 |
topic_facet |
L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie blaireau surveillance épidémiologique Mycobacterium bovis diagnostic contrôle de maladies variation saisonnière maladie des animaux http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_774 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_12732 http://aims.fao.org/aos/agrovoc/c_2238 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_426 |
author |
Buzdugan, Svetlana N. Vergne, Timothée Grosbois, Vladimir Delahay, Richard J. Drewe, Julian |
author_facet |
Buzdugan, Svetlana N. Vergne, Timothée Grosbois, Vladimir Delahay, Richard J. Drewe, Julian |
author_sort |
Buzdugan, Svetlana N. |
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 |
url |
http://agritrop.cirad.fr/585621/ http://agritrop.cirad.fr/585621/1/s41598-017-00806-4.pdf |
work_keys_str_mv |
AT buzdugansvetlanan inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT vergnetimothee inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT grosboisvladimir inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT delahayrichardj inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis AT drewejulian inferenceoftheinfectionstatusofindividualsusinglongitudinaltestingdatafromcrypticpopulationstowardsaprobabilisticapproachtodiagnosis |
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