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Supplementary information/ material for manuscript titled "Dutch dairy farmers’ perspectives on culling reasons and strategies"

In the original article, there was an error. We used the phrase “non-regulated” for cattle diseases that are in fact listed in the New Animal Health Law that went into force in 2021. A correction has beenmade toAbstract. The corrected section is shown below. Copyright © 2022, Hodnik, Acinger-Rogić, Alishani, Autio, Balseiro, Berezowski, Carmo, Chaligiannis, Conrady, Costa, Cvetkovikj, Davidov, Dispas, Djadjovski, Duarte, Faverjon, Fourichon, Frössling, Gerilovych, Gethmann, Gomes, Graham, Guelbenzu, Gunn, Henry, Hopp, Houe, Irimia, Ježek, Juste, Kalaitzakis, Kaler, Kaplan, Kostoulas, Kovalenko, Kneževič, Knific, Koleci, Madouasse, Malakauskas, Mandelik, Meletis, Mincu, Mõtus, Muñoz-Gómez, Niculae, Nikitović, Ocepek, Tangen-Opsal, Ózsvári, Papadopoulos, Papadopoulos, Pelkonen, Polak, Pozzato, Rapaliuté, Ribbens, Niza-Ribeiro, Roch, Rosenbaum Nielsen, Saez, Nielsen, van Schaik, Schwan, Sekovska, Starič, Strain, Šatran, Šerić-Haračić, Tamminen, Thulke, Toplak, Tuunainen, Verner, Vilček, Yildiz and Santman-Berends

A Corrigendum on Overview of Cattle Diseases Listed Under Category C, D or E in the Animal Health Law for Which Control Programmes Are in Place Within Europe (Front. Vet. Sci., (2021), 8, (688078), 10.3389/fvets.2021.688078)

Countries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates. The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes available.

Given that bovine herpesvirus 1 (BoHV-1) -the causative agent of Infectious Bovine Rhinotracheitis (IBR)- is still endemic in most European countries, BoHV-1 free herds are subject to a considerable risk of (re)introduction of the virus. The aim of this literature review was to describe published, quantified risk factors that are relevant for the introduction of BoHV-1. The risk factors described in this study can be used as input for modeling eradication scenarios and for communication on biosecurity measures to stakeholders. A literature search was conducted in November 2020 in two major online search databases, PubMed and Web of Science. The search criteria “risk factor” combined with different synonyms for BoHV-1 were explored, which resulted in 564 hits. Only studies performed in Europe, written in Dutch, English, French, German or Spanish with an English summary and that quantified risk factors for introduction of BoHV-1 into cattle herds were included. Studies had to quantify the risk factors with crude odds ratios (OR), an estimate of the chance of a particular event occurring in an exposed group to a non-exposed group. After checking for duplicates and excluding articles that did not meet the inclusion criteria, 12 publications remained for this review. Risk factors were classified into seven groups, i.e., herd characteristics, management, animal characteristics, purchase, direct animal contact, neighborhood and indirect transmission routes. Most relevant factors for introduction of BoHV-1 into cattle herds include herd size, purchase of cattle, cattle density, age of cattle, distance to neighboring cattle herds and professional visitors. Together with other direct and indirect animal contacts, these factors are important when elimination of BoHV-1 is considered. A closed farming system and protective clothing for professional visitors can eliminate the major routes of introduction of BoHV-1 in cattle herds. To the best of our knowledge, this is the first systematic review solely focussing on measures that can be taken to control introduction of BoHV-1 into cattle herds. Besides testing, focus on managing these (biosecurity) factors will decrease the risk of introducing the virus.

Bovine viral diarrhea (BVD) is controlled in many countries by detection and culling of persistently infected (PI) animals. The most important risk factor for BVDV introduction is purchase. An introduced cow can be PI and transmit the virus to other cattle in the herd. If she is not PI but is pregnant, there is still a risk because the subsequently born calf may be PI, when she encountered the virus in early pregnancy. To control this risk, all cows > 1 year from non-BVDV-free herds that are introduced in herds that participate in the Dutch BVDV control program are tested for virus and antibodies. Depending on the results, subsequent measures such as suspension of the BVDV-free status, removing the animals from the herd, or testing the off-spring of the cow for virus, are undertaken. The aim of this study was to evaluate the results of this risk mitigating measure. Data on cattle movements, calving's, herd-level BVDV status, and animal-level test data were available from all dairy herds that participated in the national BVDV control program (>14,000 dairy herds) for the year 2019. The data were combined and parameters of interest were calculated, i.e., (i) the number of purchased BVD virus positive cattle and (ii) the number of BVD virus positive calves born from purchased cows within 9 months after introduction. In 2019, 217,301 cattle were introduced in Dutch dairy herds that participated in the BVDV control program. Of these, 49,820 were tested for presence of BVD virus and 27 (0.05%) cows introduced in 21 different herds tested BVD virus positive. Out of 46,727 cattle that were tested for antibodies, 20.5% tested positive. The seropositive cows produced 4,341 viable calves, of which 3,062 were tested for virus and subsequently, 40 (1.3%) were found BVD virus positive. These 40 BVD virus positive calves were born in 23 herds. The risk mitigating measure led to detection of 67 BVD virus positive animals in 44 unique herds in 2019. This study makes plausible that the probability and impact of re-introduction of BVDV can be minimized by testing introduced cattle and their subsequently born calves.

13 páginas, 5 figuras, 3 tablas., The COST action “Standardising output-based surveillance to control non-regulated diseases of cattle in the European Union (SOUND control),” aims to harmonise the results of surveillance and control programmes (CPs) for non-EU regulated cattle diseases to facilitate safe trade and improve overall control of cattle infectious diseases. In this paper we aimed to provide an overview on the diversity of control for these diseases in Europe. A non-EU regulated cattle disease was defined as an infectious disease of cattle with no or limited control at EU level, which is not included in the European Union Animal health law Categories A or B under Commission Implementing Regulation (EU) 2020/2002. A CP was defined as surveillance and/or intervention strategies designed to lower the incidence, prevalence, mortality or prove freedom from a specific disease in a region or country. Passive surveillance, and active surveillance of breeding bulls under Council Directive 88/407/EEC were not considered as CPs. A questionnaire was designed to obtain country-specific information about CPs for each disease. Animal health experts from 33 European countries completed the questionnaire. Overall, there are 23 diseases for which a CP exists in one or more of the countries studied. The diseases for which CPs exist in the highest number of countries are enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis, bovine viral diarrhoea and anthrax (CPs reported by between 16 and 31 countries). Every participating country has on average, 6 CPs (min–max: 1–13) in place. Most programmes are implemented at a national level (86%) and are applied to both dairy and non-dairy cattle (75%). Approximately one-third of the CPs are voluntary, and the funding structure is divided between government and private resources. Countries that have eradicated diseases like enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis and bovine viral diarrhoea have implemented CPs for other diseases to further improve the health status of cattle in their country. The control of non-EU regulated cattle diseases is very heterogenous in Europe. Therefore, the standardising of the outputs of these programmes to enable comparison represents a challenge.

Meat inspection records of one large cattle slaughterhouse were analyzed to evaluate the added value of slaughterhouse data for cattle health surveillance in the Netherlands. Data were available from January 2015 to September 2018, consisting of 467,361 meat inspection records. Analyses included (1) an assessment of the representativeness of the cattle herds in the slaughterhouse data in relation to the cattle herd population in the Netherlands, and (2) multivariable analyses to quantify associations between meat inspection findings and farm of origin characteristics, and the trends in time of the findings in slaughtered cattle. Ninety percent of the meat inspection records originated from dairy cattle therefore this paper only presents the results of dairy herds (N = 422,194 cattle). The dairy herds in the slaughterhouse data seemed representative for the Dutch dairy population although their regional coverage differed from the distribution of dairy herds in the Netherlands. Non-dairy herds were underrepresented in the slaughterhouse data which stresses the importance of the inclusion of data from other slaughterhouses that may be more specialized in slaughtering beef cattle. Inspection records were categorized into 15 indicators related to ante-mortem and post-mortem findings. Following multivariable analyses, seven indicators were deemed of added value to existing cattle health surveillance components, as they provided either new information or information regarding specific health problems.

Agriculture is an important production sector in Albania that makes a significant contribution to the gross domestic product (GDP) and employment. The livestock sector contributes more than half of the agricultural GDP. The Albanian cattle population represents 50% of the total livestock units and accounts for 85% of the national milk production, the rest being supplied by small ruminants. Cattle productivity, health and welfare are hindered by infectious diseases, some of which are also transmissible to humans (zoonosis). The aim of this manuscript is to provide an overview of the control of selected regulated and non-EU regulated cattle diseases in Albania and to highlight specific challenges for the Albanian cattle industry. The most important infectious cattle diseases in Albania for which national control and eradication strategies are in place are bovine brucellosis, bovine tuberculosis, and anthrax, which are all zoonotic. Additionally, lumpy skin disease recently emerged in the Balkan region and is currently subject to controls. Most of the available funds and European Union support are allocated to the control of EU regulated zoonotic diseases. For control of non-EU regulated cattle diseases, no funds are available resulting in the lack of national control programmes (CPs). Based on research, clinical investigations and laboratory results, several non-EU regulated cattle infectious diseases appear endemic in Albanian dairy farms. While no national CPs exist for any of them, regional initiatives are available on a voluntary basis to control infectious bovine rhinotracheitis and bovine viral diarrhea. In the voluntary CPs, there is no monitored requirement to prove disease freedom of purchased animals and to re-evaluate the herd's free status after the introduction of animals into a herd. Data on animal movements that are routinely collected could potentially be used to control the risk of purchase, but quality needs to be further improved to increase its usefulness in disease CPs. This overview aims to collate existing information on the CPs implemented in Albania and to evaluate these to highlight gaps and threats in disease control, as well as opportunities and strengths through a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, with the goal of providing a framework for the future implementation of animal disease control measures in Albania.

International audience; The 2020 Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine (SVEPM) was scheduled to take place in Westport, Ireland, March 25−27. The committee reviewed 68 abstracts and prepared a scientific programme that included 21 oral presentations, 89 posters, 30 poster pitches, and five hands-on workshops. The local organising committee, chaired by Damien Barrett, prepared an excellent social schedule to welcome delegates to Westport. All this work was nearly finished when, in January 2020, the world focussed on news describing the emergence and spread of the SARS-CoV-2 virus. As epidemiologists we followed closer still, and in a true disease outbreak mitigation approach, by the time the World Health Organization (WHO) announced on March 11 2020 that the COVID-19 outbreak was a pandemic, our preparation phase had already begun with the committee having met in a virtual environment several times. We had discussed the ramifications and implications of physically convening for 202 registered participants at our annual event; we were ready to communicate a decision to not go ahead with the physical meeting in Westport. To keep our membership informed of our planning, we held our Annual General Meeting online, consistent with the planned meeting schedule.

International audience; Various European Member States have implemented control or eradication programmes for endemic infectious diseases in cattle. The design of these programmes varies between countries and therefore comparison of the outputs of different control programmes is complex. Although output-based methods to estimate the confidence of freedom resulting from these programmes are under development, as yet there is no practical modeling framework applicable to a variety of infectious diseases. Therefore, a data collection tool was developed to evaluate data availability and quality and to collect actual input data required for such a modeling framework. The aim of the current paper is to present the key learnings from the process of the development of this data collection tool. The data collection tool was developed by experts from two international projects: STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, www.stocfree.eu ) and SOUND control (Standardizing OUtput-based surveillance to control Non-regulated Diseases of cattle in the EU, www.sound-control.eu ). Initially a data collection tool was developed for assessment of freedom of bovine viral diarrhea virus in six Western European countries. This tool was then further generalized to enable inclusion of data for other cattle diseases i.e., infectious bovine rhinotracheitis and Johne's disease. Subsequently, the tool was pilot-tested by a Western and Eastern European country, discussed with animal health experts from 32 different European countries and further developed for use throughout Europe. The developed online data collection tool includes a wide range of variables that could reasonably influence confidence of freedom, including those relating to cattle demographics, risk factors for introduction and characteristics of disease control programmes. Our results highlight the fact that data requirements for different cattle diseases can be generalized and easily included in a data collection tool. However, there are large differences in data availability and comparability across European countries, presenting challenges to the development of a standardized data collection tool and modeling framework. These key learnings are important for development of any generic data collection tool for animal disease control purposes. Further, the results can facilitate development of output-based modeling frameworks that aim to calculate confidence of freedom from disease.

Platelet and leukocyte count reference intervals (RIs) for cattle differ by age and while adult RIs are known, RIs for calves are studied less. The aims of this observational study are to evaluate variation of platelet counts of Holstein Friesian calves over the first 14 days of life and to propose RIs for platelet and leukocyte counts of Holstein Friesian calves aged 0&ndash, 60 days. In a longitudinal study, 19 calves were blood sampled 17 times, in the first 14 days of their lives. Blood was collected in a citrate blood tube and platelet counts were determined. We assessed the course of platelet counts. In a field study, 457 healthy calves were blood sampled once. Blood was collected in an EDTA blood tube and platelet and leukocyte counts were determined. The RIs were calculated by the 2.5 and 97.5 percentiles. Platelet counts started to increase 24 h after birth (mean platelet count 381 &times, 109/L &plusmn, 138 &times, 109/L) and stabilized after five days (mean platelet count 642 &times, 265 &times, 109/L). In calves up to six days of age, platelet counts were lower than in calves older than five days. In conclusion, the RIs of platelet and leukocyte counts in calves were wider in range than the RIs for adult cattle, therefore, calf specific RIs for platelet and leukocyte counts should be used. From 6 until 60 days of age, we propose an RI for platelet counts of 287&ndash, 1372 &times, 109/L and for the first 60 days of life an RI for leukocyte counts of 4.0&ndash, 18.9 &times, 109/L.

Some European countries have successfully implemented country-specific control programs (CPs) for infectious cattle diseases that are not regulated or are regulated only to a limited extent at the European Union (EU) level. Examples of such diseases include bovine viral diarrhea (BVD), infectious bovine rhinotracheitis (IBR), and Johne's disease (JD). The CPs vary between countries in the design and quality of collected data as well as methods used to detect infection and estimate prevalence or probability of freedom from infection. Differences in disease status between countries and non-standardized approaches to assess freedom from infection pose a risk for countries with CPs for non-regulated diseases as infected animals may influence the progress of the disease control or eradication program. The implementation of output-based standards allows estimation and comparison of the probability of freedom for non-regulated cattle diseases in European countries. The aim of the current study was to assess the existence and quality of data that could be used for estimating freedom from infection in European countries. The online data collection tool was sent to 32 countries participating in the SOUND control COST Action and was completed by 24 countries. Data on cattle demographics and data from CPs of IBR and BVD exist in more than 50% of the response countries. However, data describing risk factors and CP of JD was reported as existing in

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program.

Two vector-borne infections have emerged and spread throughout the north-western part of Europe in the last decade: Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus (SBV). The objective of the current study was to compare three statistical methods when applied in a syndromic surveillance context for the early detection of emerging diseases in cattle in the Netherlands. Since BTV-8 and SBV both have a negative effect on milk production in dairy cattle, routinely collected bulk milk recordings were used to compare the three statistical methods in their potential to detect drops in milk production during a period of seven years in which BTV-8 and SBV emerged. A Cusum algorithm, Bayesian disease mapping model, and spatiotemporal cluster analysis using the space-time scan statistic were performed and their performance in terms of sensitivity and specificity was compared. Spatiotemporal cluster analysis performed best for early detection of SBV in cattle in the Netherlands with a relative sensitivity of 71% compared to clinical surveillance and 100% specificity in a year without major disease outbreaks. Sensitivity to detect BTV-8 was low for all methods. However, many alerts of reduced milk production were generated several weeks before the week in which first clinical suspicions were reported. It cannot be excluded that these alerts represent the actual first signs of BTV-8 infections in cattle in the Netherlands thus leading to an underestimation of the sensitivity of the syndromic surveillance methods relative to the clinical surveillance in place.

There is a plethora of meat-borne hazards – including parasites - for which there may be a need for surveillance. However, veterinary services worldwide need to decide how to use their scarce resources and prioritise among the perceived hazards. Moreover, to remain competitive, food business operators – irrespective of whether they are farmers or abattoir operators - are preoccupied with maintaining a profit and minimizing costs. Still, customers and trade partners expect that meat products placed on the market are safe to consume and should not bear any risks of causing disease.\ud \ud Risk-based surveillance systems may offer a solution to this challenge by applying risk analysis principles; first to set priorities, and secondly to allocate resources effectively and efficiently. The latter is done through a focus on the cost-effectiveness ratio in sampling and prioritisation. Risk-based surveillance was originally introduced into veterinary public health in 2006. Since then, experience has been gathered, and the methodology has been further developed. Guidelines and tools have been developed, which can be used to set up appropriate surveillance programmes. In this paper, the basic principles are described, and by use of a surveillance design tool called SURVTOOLS (https://survtools.org/), examples are given covering three meat-borne parasites for which risk-based surveillance is 1) either in place in the European Union (EU) (Trichinella spp.), 2) to be officially implemented in December 2019 (Taenia saginata) or 3) only carried out by one abattoir company in the EU as there is no official EU requirement (Toxoplasma gondii). Moreover, advantages, requirements and limitations of risk-based surveillance for meat-borne parasites are discussed.

To combat antimicrobial resistance (AMR), policymakers need an overview of evolution and trends of AMR in relevant animal reservoirs, and livestock is monitored by susceptibility testing of sentinel organisms such as commensal E. coli. Such monitoring data are often vast and complex and generates a need for outcome indicators that summarize AMR for multiple antimicrobial classes. Model‐based clustering is a data‐driven approach that can help to objectively summarize AMR in animal reservoirs. In this study, a model‐based cluster analysis was carried out on a dataset of minimum inhibitory concentrations (MIC), recoded to binary variables, for 10 antimicrobials of commensal E. coli isolates (N = 12,986) derived from four animal species (broilers, pigs, veal calves and dairy cows) in Dutch AMR monitoring, 2007–2018. This analysis revealed four clusters in commensal E. coli in livestock containing 201 unique resistance combinations. The prevalence of these combinations and clusters differs between animal species. Our results indicate that to monitor different animal populations, more than one indicator for multidrug resistance seems necessary. We show how these clusters summarize multidrug resistance and have potential as monitoring outcome indicators to benchmark and prioritize AMR problems in livestock.

The aims of our study were to calculate the most appropriate cut-off value for milk samples in a serum-validated Mycobacterium avium subsp. paratuberculosis (MAP) ELISA and to analyze MAP ELISA responses in milk samples from vaccinated and nonvaccinated dairy goats in the Netherlands. Analyzed herds were representative for location and herd size of dairy goat herds in the Netherlands. A significantly higher proportion of the analyzed 49 herds were organic as compared with the total Dutch dairy goat population. First, the MAP ELISA was optimized using 992 paired serum and milk samples. At a cut-off of 25 S/P%, the relative sensitivity (Se) was 58.4% (n = 992, 95% CI: 48.8%&minus, 67.6%) and relative specificity (Sp) was 98.5% (n = 992, 95% CI: 97.5%&minus, 99.2%), as compared to serum ELISA results. The percentage of positively tested herds was 78.2% (n = 49, 95% CI: 63.4%&minus, 88.1%). The percentage of positive milk samples per herd (n = 22) was on average 4.6% (median, min, and max of 4.7%, 0.0%, and 10.7%, respectively). Average age of ELISA-positive (3.2 years) and -negative goats (3.2 years) was not different. Significantly more vaccinated goats tested positive (6.7%) as compared with nonvaccinated goats (1.1%). This study shows that a high number of vaccinated and nonvaccinated commercial dairy goat herds in the Netherlands have MAP-ELISA-positive goats.