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Background: Scenario tree modelling is a well-known method used to evaluate the confidence of freedom from infection or to assess the sensitivity of a surveillance system in detecting an infection at a certain design prevalence. It facilitates the use of data from various sources and the inclusion of risk factors into calculations, while still obtaining quantitative estimates of surveillance sensitivity and probability of freedom., Objectives: We conducted a scoping review to identify scenario tree models (STMs) applied to assess freedom from infection in veterinary medicine, characterize their use, parameterisation, reporting and potential limitations., Eligibility Criteria: We included published scientific articles and grey literature that were a) neither reviews nor expert opinions, b) aimed to assess freedom from infection, provided methods to assess it, or aimed to estimate the sensitivity of a surveillance program for early detection of an infection at a design prevalence, c) targeted infection in animals and d) used scenario tree modelling. The search covered documents published between January 2006 and August 2021., Design: Several search methods were used to retrieve scientific articles and grey literature relevant to the subject. The search strategy included searching in scientific databases and/or grey literature repositories, contacting experts across the world that previously worked with STMs and retrieving citations from relevant reviews., Results and Discussion: Four hundred twenty-four distinct documents were retrieved with our search string. After screening, data was extracted from 99 documents representing 67 projects. Forty different animal diseases were modelled with STMs, the most represented being infections with tuberculous Mycobacterium sp., Avian Influenza A virus and Brucella sp. STMs were mostly used for diseases of cattle, swine and wild mammals. Results showed that STMs were used in a large variety of studies, are very versatile and were used in disparate frameworks. However, we also found that studies are not reported in a standardized way and often lack important information. This makes results hard to interpret, compare and reproduce. Additionally, we identified common assumptions and misconceptions, the most important ones regarding sensitivity and specificity, which could have an impact on the results of the studies using STMs., Conclusion: We recommend the elaboration of internationally agreed guidelines about how to report results from STMs in a uniform manner. Such guidelines should include information on the study setting, procedures and analyses, but also on how the results could be interpreted concerning freedom from infection., Competing Interests: Declaration of Competing Interest None to declare. However, some of the authors and their respective teams have worked or still work with scenario tree models. The authors did not extract data from papers that they co-authored. The funder did not take part in the development or execution of the project., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Salmonella spp. infections in animals are a concern due to their zoonotic nature, welfare effects and economic impact on the livestock industry. To enable targeted surveillance, it is important to identify risk factors for the introduction of Salmonella spp. in a herd. Since 2009, Dutch dairy processors require herds delivering milk to their plants to participate in a Salmonella programme. In this programme, bulk milk is tested three times a year (i.e. test rounds) by ELISA on presence of antibodies against Salmonella spp. serogroups B and D. Based on these bulk milk results we identified newly infected herds, and aimed to identify associated risk factors. Effects of putative risk factors for becoming newly infected were studied using a multivariable population average logistic regression (PA-GEE) model with binomial distribution. Per test round in 2019-2021, 0.85-4.10 % of the Dutch dairy herds at risk became newly infected, with large regional differences. Several risk factors for becoming newly infected in the context of the low herd-level prevalence were identified. The most evident risk factors that were identified were having at least one infected or recently recovered dairy herd within 500 m (OR = 2.67), on-farm presence of pigs (OR = 1.63), introduction of more than 2 cattle from other herds in the previous 12 months (OR = 1.17), being in an area with a relative soil moisture of >0.54 % (OR = 1.31), being located in an area with a high water surface area (>2 %; OR = 1.14) and a larger herd size (OR = 1.65). These results indicate that, in addition to introduction of cattle, local transmission plays an important role in the between-herd transmission of Salmonella spp. Information on risk factors for becoming newly infected based on regularly collected data, can be used to improve surveillance and to implement targeted control measures against salmonellosis., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Bovine Viral Diarrhea Virus (BVDV) is a common viral disease in cattle, causing economic losses in naive herds where the virus is introduced. In the Netherlands, a BVDV control program has been in place since 1998, evolving from voluntary to mandatory participation for dairy herds since April 2018. Participation in the BVDV control program is not mandatory for non-dairy farms. The aim of this study was to determine risk factors for new introduction of BVDV into certified BVDV-free herds in the context of a national BVDV control program in dairy herds. In this retrospective case-control study, survey data were collected from 149 dairy farms that lost their BVDV-free status (case herds) and 148 matched dairy farms that maintained their BVDV-free status (control herds) between 2018 and 2021. The survey captured information about potential risk factors and herd characteristics in the 2 years leading up to the loss of BVDV-free status (case herds, virus detection in at least one animal or when seroconversion was detected) or remaining BVDV-free (control herds). Descriptive statistics and logistic regression with a backward selection and elimination procedure were used to identify potential risk factors associated with losing BVDV-free status. Risk factors were quantified as an Odds Ratio (OR) with the associated 95% confidence interval (CI). The risk factor with the highest OR for losing BVDV-free status was purchasing cattle from herds without BVDV-free status (OR 1.25, CI 1.10-1.41), followed by the farmer having another profession that resulted in contact with other cattle (OR 1.25, CI 1.06-1.47), housing young calves and adult cows in the same barn (OR 1.22, CI 1.08-1.38), having a permanent employee on the farm (OR 1.17, CI 1.04-1.31), having a group calving pen (OR 1.16, CI 1.03-1.32), escaped cattle from other farms that mingled with own cattle (OR 1.16, CI 1.01-1.33), and nearest distance to a non-dairy farm (OR 1.15, CI 1.03-1.28). Although the BVDV status of most dairy herds can be checked in an open register, approximately half of the farmers indicated that they purchased cattle from BVDV-free herds while they were actually purchasing from non-BVDV-free farms. Farmers should be stimulated to actively check the true BVDV status of the herd from which cattle are purchased to further reduce the risk of introduction. In addition, indirect contact with cattle from other farms through either the farmer or other on-farm staff should be avoided. It is strongly advised to work in these situations with proper biosecurity measures such as changing boots and coveralls. The results can be used to improve BVDV control programs to further decrease the prevalence., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

In September 2023, bluetongue virus serotype 3 (BTV-3) emerged in the Netherlands, infecting over five thousand livestock farms. In sheep, high morbidity and mortality rates were reported that were unlike previously described bluetongue outbreaks. This study aimed to quantify the impact of BTV-3 in the small ruminant population in the Netherlands in 2023. Sheep and goat movement census data and BTV-3 notification data were available from 2020 until the end of 2023. Data were aggregated to farm and week level and mortality indicators were calculated for lambs (<1 year) and adult animals (≥1 year). Population averaged GEE models with a Negative-binomial distribution and a log-link function correcting for repeated measures per farm in time were used to quantify the association between BTV-3 and mortality. In 2023, 2994 sheep farmers and 89 goat farmers notified clinical signs of BTV-3 to the NVWA. During this BTV-3 outbreak period, an additional 55,000 sheep died compared to the same period in 2020-2022. At flock level a high variety in mortality was observed, with a clear increase in mortality in both flocks that were not notified but that were located in infected areas and in flocks of which the farmer notified clinical signs. During the BTV-3 outbreak period, mortality in infected areas increased 4.2 (95 % CI: 4.0-4.3) times in sheep lambs (<1 year) and 4.6 (95 % CI: 4.4-4.8) times in sheep (≥1 year) compared to BTV-3 free areas. Flocks with a confirmed BTV-3 infection that were notified in September showed a 12.8 (95 % CI: 11.4-14.3) times higher mortality in lambs and a 15.1 (95 % CI: 13.7-16.6) times higher mortality in sheep compared to flocks in BTV-3 areas. In flocks of which the farmer notified clinical signs after September, mortality was 4.6 (95 % CI: 4.2-5.0) and 5.6 (95 % CI: 5.1-6.0) times higher in lambs and sheep compared BTV-3 areas respectively. In goats, around 4000 additional deaths were recorded during the BTV-3 outbreak period. In farms that were notified, mortality of goats (≥1 year) was 1.8 (95 % CI: 1.2-2.8) times higher compared to BTV-3 free areas. Since May 2024, multiple BTV-3 vaccines are available in the Netherlands. In June 2024, the first new infections of BTV-3 were confirmed in Dutch sheep flocks. Hopes are that with the possibility to vaccinate, the spread and impact of BTV-3 in the Netherlands will rapidly decline and that losses as observed in 2023 will no longer be seen., Competing Interests: Declaration of Competing Interest With this statement all authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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.)