Review state-of-the-art of output-based methodological approaches for substantiating freedom from infection.

Autor:	Meletis E; Department of Public and One Health, School of Health Sciences, University of Thessaly, Karditsa, Greece., Conrady B; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Complexity Science Hub Vienna, Vienna, Austria., Hopp P; Norwegian Veterinary Institute, Ås, Norway., Lurier T; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France.; Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint-Genès-Champanelle, France., Frössling J; Department of Disease Control and Epidemiology, National Veterinary Institute (SVA) Uppsala, Uppsala, Sweden.; Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Skara, Sweden., Rosendal T; Department of Disease Control and Epidemiology, National Veterinary Institute (SVA) Uppsala, Uppsala, Sweden., Faverjon C; Ausvet Europe, Lyon, France., Carmo LP; Norwegian Veterinary Institute, Ås, Norway.; Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland., Hodnik JJ; Clinic for Reproduction and Large Animals-Section for Ruminants, Veterinary Faculty, University of Ljubljana, Ljubljana, Slovenia., Ózsvári L; Department of Veterinary Forensics and Economics, University of Veterinary Medicine Budapest, Budapest, Hungary., Kostoulas P; Department of Public and One Health, School of Health Sciences, University of Thessaly, Karditsa, Greece., van Schaik G; Royal GD, Deventer, Netherlands.; Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands., Comin A; Department of Disease Control and Epidemiology, National Veterinary Institute (SVA) Uppsala, Uppsala, Sweden., Nielen M; Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands., Knific T; Institute of Food Safety, Feed and Environment, Veterinary Faculty, University of Ljubljana, Ljubljana, Slovenia., Schulz J; Institute of Epidemiology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Greifswald, Germany., Šerić-Haračić S; Faculty of Veterinary Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina., Fourichon C; Oniris, INRAE, BIOEPAR, Nantes, France., Santman-Berends I; Royal GD, Deventer, Netherlands.; Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands., Madouasse A; Oniris, INRAE, BIOEPAR, Nantes, France.
Jazyk:	angličtina
Zdroj:	Frontiers in veterinary science [Front Vet Sci] 2024 Mar 14; Vol. 11, pp. 1337661. Date of Electronic Publication: 2024 Mar 14 (Print Publication: 2024).
DOI:	10.3389/fvets.2024.1337661
Abstrakt:	A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a 'guide towards substantiating freedom from infection' that describes both all assumptions-limitations and available methods that can be applied in different settings. Competing Interests: CFa was employed by Ausvet Europe. IS-B was employed by Royal GD. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Meletis, Conrady, Hopp, Lurier, Frössling, Rosendal, Faverjon, Carmo, Hodnik, Ózsvári, Kostoulas, van Schaik, Comin, Nielen, Knific, Schulz, Šerić-Haračić, Fourichon, Santman-Berends and Madouasse.)
Databáze:	MEDLINE
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