Capture–recapture approaches and the surveillance of livestock diseases: A review

Vergne, Timothée; Vilas, Victor Del Rio; Cameron, Angus; Dufour, Barbara; Grosbois, Vladimir

doi:10.1016/j.prevetmed.2015.04.003

Cited by 19 publications

(15 citation statements)

References 70 publications

(2 reference statements)

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“…Capture–recapture methods allow for the estimation of an unknown population size by using two or more samples from that population. Approaches with only one sample, in which the number of occurrences of cases are modelled, have also been developed [ 2 ]. The methodology has largely been developed in the field of ecology for abundance estimation [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance

et al. 2016

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IntroductionSurveillance networks are often not exhaustive nor completely complementary. In such situations, capture-recapture methods can be used for incidence estimation. The choice of estimator and their robustness with respect to the homogeneity and independence assumptions are however not well documented.MethodsWe investigated the performance of five different capture-recapture estimators in a simulation study. Eight different scenarios were used to detect and combine case-information. The scenarios increasingly violated assumptions of independence of samples and homogeneity of detection probabilities. Belgian datasets on invasive pneumococcal disease (IPD) and pertussis provided motivating examples.ResultsNo estimator was unbiased in all scenarios. Performance of the parametric estimators depended on how much of the dependency and heterogeneity were correctly modelled. Model building was limited by parameter estimability, availability of additional information (e.g. covariates) and the possibilities inherent to the method. In the most complex scenario, methods that allowed for detection probabilities conditional on previous detections estimated the total population size within a 20–30% error-range. Parametric estimators remained stable if individual data sources lost up to 50% of their data. The investigated non-parametric methods were more susceptible to data loss and their performance was linked to the dependence between samples; overestimating in scenarios with little dependence, underestimating in others. Issues with parameter estimability made it impossible to model all suggested relations between samples for the IPD and pertussis datasets. For IPD, the estimates for the Belgian incidence for cases aged 50 years and older ranged from 44 to58/100,000 in 2010. The estimates for pertussis (all ages, Belgium, 2014) ranged from 24.2 to30.8/100,000.ConclusionWe encourage the use of capture-recapture methods, but epidemiologists should preferably include datasets for which the underlying dependency structure is not too complex, a priori investigate this structure, compensate for it within the model and interpret the results with the remaining unmodelled heterogeneity in mind.

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Section: Introductionmentioning

confidence: 99%

Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance

et al. 2016

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“…Incorporation of test results at the badger social group level 20 could also be a useful refinement to account for the spatial aggregation of infection observed in this population 27 . Possible wider applications might include the interpretation of results from regular testing of farm animals for diseases affecting production 9 , and the results of testing humans for high-impact diseases 39 .…”

Section: Discussionmentioning

confidence: 99%

“…Such methods use information gleaned from sequences of detections and non-detections to estimate the number of missing individuals and hence allow adjustment for undercounting in population surveys 6, 7 . In epidemiology, the use of CRC was initially limited to estimating the number of infected individuals that were unrecorded (‘missed cases’), an application which has been popular for more than 30 years in the study of chronic and infectious diseases in humans 8 and, more recently, in livestock 9 . The use of CRC to estimate disease transmission parameters to gain insights into the dynamics of infectious diseases is a more recent development 10–13 .…”

Section: Introductionmentioning

confidence: 99%

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

Buzdugan

Vergne

Grosbois

et al. 2017

Sci Rep

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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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“…Capture-recapture models are often used to estimate the number of undetected units of interest by modeling multiple detections of each detected unit. In epidemiology, depending on the number of data sources, two capture-recapture approaches can be used: multilist and unilist approaches (Vergne et al, 2015). While multilist capture-recapture methods are relevant when infected units can be detected by several surveillance systems or components, unilist methods focus on the frequency of detection of infected units by a single source ("one list") of observations (Bronner et al, 2013;Chao et al, 2001;Del Rio Vilas and Böhning, 2008;Vergne et al, 2012).…”

Section: Capture-recapture Methodsmentioning

confidence: 99%

Estimating the incidence of equine viral arteritis and the sensitivity of its surveillance in the French breeding stock

Amat

Vergne

Tapprest

et al. 2016

Veterinary Microbiology

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Capture–recapture approaches and the surveillance of livestock diseases: A review

Cited by 19 publications

References 70 publications

Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance

Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance

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

Estimating the incidence of equine viral arteritis and the sensitivity of its surveillance in the French breeding stock

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