Wild boar (Sus scrofa) populations have rapidly expanded across Europe, leading to increased agricultural damage and elevated risks of infectious disease transmission. Understanding how environmental conditions, population density and human activities shape movement across spatial scales is essential for effective management and disease control, particularly because the temporal windows when animals are infectious but asymptomatic align closely with the short-term movement dynamics relevant to transmission processes. We analysed GPS tracking data from 99 adult wild boar (16 males, 83 females) in Sweden (2004–2021) to quantify movement at two complementary scales. Biweekly (14-day) space use was estimated using autocorrelated kernel density estimation (AKDE; 95

Salmonella Dublin (S. Dublin), a zoonotic serotype adapted to cattle, causes animal welfare issues and economic losses. The disease has proven particularly challenging to control in Öland, Sweden. This study uses Bayesian simulation-based inference of bulk tank milk sample results to analyse the S. Dublin infection dynamics in Öland cattle. The infection process was formulated as a dynamic state-space model and particle Markov-chain Monte Carlo methods were applied to infer the underlying infection dynamics and estimate the basic reproduction number (R_0) as well as the effective reproduction number (R_t). These metrics provide insight into transmission dynamics, enabling assessment of the effectiveness of the current S. Dublin control in Swedish cattle and identification of interventions that may reduce the prevalence. The results show that most holdings on Öland have R_0 < 1, indicating that infection is expected to die out after introduction. However, in a subset of holdings R_0 > 1, and there the risk for spread of S. Dublin is higher. Furthermore, the analysis reveals that on average, R_t ≈ 1, suggesting a stable endemic presence unless effective interventions are implemented. In addition, the results show that it is insufficient to restrict the movements of infected cattle on Öland to bring R_t < 1, as local spread and within-herd transmission contribute equally to the force of infection (approximately 50

Movements of livestock between holdings plays an important role in the spread of many infectious diseases, and network analysis can provide a greater understanding of potential spread dynamics. This study explored cattle movements between Swedish holdings from 2005 to 2022 to enhance the knowledge basis for epidemiological analyses. In addition to classical network analysis, a novel method, Location Change Pattern (LCP), was used to analyse movements between holdings per individual. Furthermore, survival analysis was used to investigate animal- and herd level risk factors associated with moving cattle from a holding. Although the number of cattle and holdings decreased, the number of movements increased substantially over the study period. Simultaneously, the network became more disassortative with an increased average path length, whilst indegree and in- and outgoing contact chains decreased. Combined, the results suggests that an epidemic spread in the cattle population may be slower and reach a smaller final size compared to 2005. Additionally, the clustering coefficient and reciprocity increased over time which might change the dynamics of disease spread. The increase in movements can be partly explained by an increased number of cattle being moved back and forth between holdings, particularly seen for female cattle. Male calves at holdings with a high proportion of female cattle had the greatest hazard of being moved, with an increased hazard at around 20 days of age. In summary, significant changes over time were found in the cattle population and in the movement network, which must be accounted for when working with disease prevention.

Foodborne transmission appears to be a significant route for human hepatitis E virus (HEV) infection in Europe. We have developed a quantitative microbiological risk assessment (QMRA) for HEV infection due to consumption of three selected pork products (liver pâté, minced meat, and sliced liver), which models the steps from farm to human consumption in high detail, including within‐farm transmission dynamics and microbiological processes such as cross contamination and thermal inactivation. Our model is unique in that it considers prevalence and viral load of two microbiological variables, HEV RNA and infectious HEV, expressing the latter in terms of the former through so‐called “adjustment factors” where data are lacking. When the QMRA was parameterized for France and using infectious HEV, we found that sliced liver posed by far the highest risk of infection, with mean probability per portion , corresponding to human cases annually. For minced meat, the probability of infection was , with only human cases. While our model predicted appreciable levels of HEV RNA remaining in liver pâté at the point of consumption, the amount of infectious HEV and hence risk of infection was zero, emphasizing the importance of using the correct microbiological variable when assessing the risk to consumers. Owing to its highly mechanistic nature, our QMRA can be used in future work to assess the impact of control measures along the pork‐supply chain at high resolution.

Bovine tuberculosis (bTB) has a complex infection ecology and is difficult to control in many countries, including Ireland. For many years, the Irish national bTB eradication programme relied on cattle-based control measures, including test-and-removal with related movement restrictions. In the early 2000s, badger culling was added as a part of the control measure in the national programme. As badgers are protected animals under Wildlife Acts, making culling undesirable, this practice is now progressively being replaced by badger vaccination. However, it is unclear whether badger vaccination, in combination with the cattle test-and-removal and movement restriction, is sufficient to eradicate bTB, or whether additional measures will be needed. Assessing the impact of badger vaccination on reducing bTB in cattle is complex due to the involvement of multiple hosts and transmission routes. Key contributors include transmission to and from wildlife (e.g., European badger, Meles meles), the persistence of Mycobacterium bovis in the environment, and - due to imperfect diagnostic tests - the movement of infected cattle and residual infection in the herd. Understanding of relative contribution of these infectious sources is a key knowledge gap. This study aims to assess the impact of badger vaccination, in addition to cattle test-and-removal and movement restriction, on bTB eradication at a regional level and to assess whether additional interventions are needed. Additionally, we investigate the contribution of several transmission mechanisms such as, local cattle, residual infection, badgers and introduced cattle on the transmission of bTB at the level of both the individual and the herd. To achieve this, we developed a metapopulation model that includes each of the above-mentioned transmission mechanisms for the Kilkenny badger vaccination trial area. The model incorporates within-herd transmission for cattle and within-territory transmission for badgers, and also transmission between herds, both via cattle trade movements and via overlapping badger territories. Our results show that cattle-to-cattle transmission contributes most to new cattle infections at the individual animal (cattle) level, while breakdowns at the herd level usually involve multiple routes. Badger vaccination, when combined with cattle test-and-removal programme, may not be sufficient to achieve eradication in this region. We highlight the need for additional interventions that target cattle, badger, and movement to form a comprehensive intervention strategy, including cattle vaccination, improve farm biosecurity, badger vaccination and risk-based trading.

Risk mapping in epidemiology is a strategic tool that identifies high-risk areas for disease outbreaks, guiding preventive, surveillance, and control measures. In this study, we investigated how the selection of risk factors in ASF risk mapping affected the output of risk maps using the outbreak in Fagersta, Sweden as a study area. We identified ASFV risk factors by considering the sequence of introduction, release, and spread. Introduction was linked to pathways through which the virus could enter new areas, while release was associated with human activities and infrastructure that may lead to environmental contamination. Spread was then examined in relation to wild boar populations and environmental conditions that influence virus transmission. We demonstrated how previously overlooked human activities, such as the management of residual waste in areas accessible to wild boar, contributed to the risk of ASF in Fagersta, an area classified as low risk, and how this affected the calculation of disease risk. This study emphasizes the need for robust risk assessment frameworks that take into account ecological and socio-demographic factors, as well as emerging research findings. In addition, the existence of region-specific threats or vulnerabilities point out the necessity of frequent revisions of risk maps by incorporating new threats or vulnerabilities and adapting regional features to environmental changes. These findings are meant to improve preparedness and response strategies for ASF and other infectious disease events, ultimately contributing to animal as well as public health protection.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) in animals are considered a human health threat, because this type of bacteria can serve as a reservoir of antibiotic resistant genes and act as a continuous threat of the emergence of new resistant bacteria, in addition to the direct effect of making infection untreatable. Although the prevalence of ESBL producing bacteria in broilers was drastically reduced in the Netherlands, chicken meat still has the highest prevalence among meat products. Therefore, further control of the ESBL-producing E. coli in the broiler production chain is important to reduce public health risks. The main objectives of this study were to evaluate the effectiveness of intervention scenarios to reduce the transmission of ESBL-producing E. coli in the broiler production chain and to quantitatively estimate the risk to public health. In this study, we developed two different types of transmission models that described the observed time-related decline in prevalence during a production round: one with time-dependent decline in susceptibility and one with partial immunity to phylogenetic groups. Both models incorporated the environmental contamination effect between production rounds and within flocks. The parameter values, including transmission rate and recovery rate, were estimated by Approximate Bayesian computation (ABC) method using data from a longitudinal study in a Dutch organic broiler farm. We applied the models to the three production stages in the broiler production chain, beginning from the Parent Stock (PS) farms, the hatcheries, and to the broiler farms. In our models, eggs were collected from different parent stock farms and transported to the hatchery and from there to a broiler farm.The size of a flock and the number of farms were adjusted to the Dutch situation. Both models were able to describe the observed dynamics within and between the production stages equally well, with estimated ESBL-producing E. coli prevalence of 8.98% and 11.47% in broilers at slaughter and 0.12% and 0.15% in humans due to chicken consumption. Both models indicated that improving farm management to eliminate the bacteria from the environment was the most effective intervention, making this outcome robust. Although chicken meat consumption is not a major risk factor for human carriage of the bacteria according to our models, reducing the bacteria in the PS and broiler farm environment to at least one percent can further decrease the prevalence in humans.

The first case of African swine fever (ASF) was confirmed in Sweden in September 2023. This article describes the local epidemiology, including the spatiotemporal dynamics of the outbreak and some of the factors that may have contributed to its apparently successful eradication. Upon detection of the outbreak, strict control measures were put in place in a preliminarily defined infected zone. A carcass search, including geo-localisation, removal, sampling, and destruction of found carcasses, was initiated and a preliminary core area was defined based on the results. Six months after confirmation of the first case, 93 wild boar carcasses had been found in the infected zone, of which 62 tested positive for ASF virus (ASFV). All ASFV-positive carcasses were found inside the core area. Based on two taphonomy methods, it was assumed that the infection was introduced between early May and late June 2023. The data also indicated that the epidemic curve peaked between mid-August and mid-September, with the last death occurring in late September 2023. Based on the average estimated time of death, geo-localisation of carcasses and two-dimensional kernel density estimation, clustering in space and time was identified. An online questionnaire with questions about hunting and the wild boar population was sent to all leaders of hunting groups in the infected zone. The results showed that the wild boar population had increased in the last 10 years but with large variations and geographical heterogeneity in space use. Disease introduction through natural wild boar movements was excluded and it was assumed that the long-distance translocation of the virus had occurred through human activities. A municipal waste collection centre without wild boar-proof fencing is located close to the epicentre of the outbreak, attracting many wild boar and contributing to the spread of the virus once it had been introduced to the population.

The Swedish wild boar ( Sus scrofa ) population has increased rapidly over the last decades, resulting in conflicts with human activities. Particularly, the increase has been challenging for agriculture as wild boar cause damage on crops and grasslands. To predict under what conditions to expect damage and where to prioritize management actions, basic knowledge about wild boar habitat and space use is needed. In this study, we used data from 99 wild boar equipped with GPS-collars, collected over a large temporal scale and throughout their distributional range in southern Sweden. We investigated wild boar home range size and habitat use across gradients of habitat availability and population density. Functional response in habitat use was assessed by estimating the use and availability of agricultural land on individual level and then, on population-level evaluating how use changed with changing availability. Finally, a potential response in habitat use was evaluated in relation to population density, i.e., the interaction between availability and population density. Home range size was negatively related to population density for both male and female wild boar. Wild boar used agricultural land more intensively with increasing population density and when other habitat types were less available. Our findings show that wild boar spatial behavior is highly context dependent and may vary considerably due to landscape characteristics and local conditions. Wild boars tend to overuse agricultural land at high densities which has strong implications for wildlife management. It is therefore important to consider local conditions when predicting space and habitat use by wild boar. Overall, this study provided a better understanding of the drivers of wild boar distribution and space use in agro-forested mosaic landscapes and how this knowledge can improve management practices.

Bovine viral diarrhea (BVD) represents a serious threat to the cattle sector in Chile, indicating the need for a regionally defined control program. Ex-ante evaluations of program options using simulation modeling have proven to be a successful approach in providing decision-makers with relevant supporting insights in that respect. Given the complexity of bovine viral diarrhea virus (BVDV) infection dynamics, simulation of BVD spread in a metapopulation requires detailed consideration of both within and between herd transmission dynamics. The aims of the study are (i) to investigate the dynamics of BVDV transmission in cattle herds in southern Chile by linking a within-herd transmission model (WHM) that accounts for the BVDV's unique characteristics with a between-herd model (BHM) that meets the demands for further regional control strategy evaluation; (ii) to suggest and discuss criteria for evaluation of the model approach and plausibility for later research and for support decision-making. This resulted in bringing forth a modeling rationale for complex disease spread simulation in metapopulations. BHM simulations under this approach show outcomes that agree with BVDV's known situation in Chile; dairy herds prevalence at endemic equilibrium reaches and maintains 75%, which agrees with estimations of BVDV active infection in dairy herds in southern Chile (77%). For the entire herd population, the infection always reaches endemic levels with a large proportion of infected herds (median = 60%), where herd prevalence was higher in the dairy herd class than in the remaining categories. Transmission probability variation affects the new infections picked, prevalence at endemic levels, and the velocity in which the infection spreads between herds. The fact that the presented approach was able to model a complex infection dynamic such BVDV, with sufficient confidence, provides evidence that this approach can be used to explore mitigation strategies to control BVDV in southern Chilean herds.

Background In recent years, the wildlife/livestock interface has attracted increased attention due to disease transmission between wild and domestic animal populations. The ongoing spread of African swine fever (ASF) in European wild boar ( Sus scrofa ) emphasize the need for further understanding of the wildlife/livestock interface to prevent disease spill-over between the wild and domestic populations. Although wild boar may also act as a potential source for other infectious disease agents, ASF is currently the most severe threat from wild boar to domestic pigs. To gather information on the wild boar situation at commercial pig producing farms in Sweden, a digital questionnaire survey was distributed through the animal health services. Results Most pigs produced for commercial purposes in Sweden are raised without outdoor access. Of the 211 responding pig producers, 80% saw wild boar or signs of wild boar activity in the vicinity of their farm at least once during the year. Observations were significantly correlated with geographical region, but there was no correlation between farm characteristics (farm size, main type of production, outdoor access) and observed wild boar presence or proximity. However, a reported higher frequency of wild boar observations was positively correlated with the observations being made in closer proximity to the farm. Hunting and strategic baiting were the most common mitigation strategies used to keep wild boar at bay. Of the 14 farms raising pigs with outdoor access, 12 responded that these pigs could be raised solely indoors if needed. Pigs with outdoor access are required to be fenced in, but double fencing in these outdoor pig enclosures was not practiced by all. A perimeter fence surrounding any type of pig farm was very rare. More than half of the producers that grew crops with intended use for pigs reported crop damage by wild boar. Conclusion This study shows that although pigs raised for commercial purposes in Sweden are, to a large extent, kept indoors the potential for indirect contact with wild boar exists and must be considered. Variable local situations regarding wild boar abundance may require an adaptive approach regarding biosecurity efforts.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) in animals are considered a human health threat, because this type of bacteria can serve as a reservoir of antibiotic resistant genes and act as a continuous threat of the emergence of new resistant bacteria, in addition to the direct effect of making infection untreatable. Although the prevalence in broilers was drastically reduced, chicken meat still has the highest prevalence among meat products. Therefore, further control of the ESBL-producing E. coli in the broiler production chain is important to reduce public health risks. The main objectives of this study were to evaluate the effectiveness of intervention scenarios to reduce the transmission of ESBL-producing E. coli in the broiler production chain and to quantitatively estimate the risk to public health. In this study, we developed two different types of transmission models that described the observed time-related decline in prevalence during a production round: one with time-dependent decline in susceptibility and one with partial immunity to phylogenetic groups. Both models incorporated the environmental contamination effect between production rounds and within flocks. The parameter values, including transmission rate and recovery rate, were estimated by Approximate Bayesian computation (ABC) method using data from a longitudinal study in a Dutch broiler farm. We applied the models to the broiler production chain and further added the effect of mixing eggs and chicks from different origins and set the size of a flock and the number of farms according to the Dutch situation. Both models were able to describe the observed dynamics within and between the production stages equally well and estimated the outcome of interventions quantitatively. Both models indicated that improving farm management to eliminate the bacteria from the environment was the most effective intervention, making this outcome robust. According to our models, chicken meat consumption was not a major risk factor for human carriage of the bacteria.

Pigs are considered to be the main reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA), which is a zoonotic opportunistic pathogen. As LA-MRSA is an occupational hazard, there is an incentive to control its spread in pig herds. Currently, knowledge about effective control measures which do not require culling the whole herd are limited, and the control strategies against LA-MRSA vary between countries. This study uses a stochastic compartment model to simulate possible control measures for LA-MRSA in a farrow-to-finish pig herd. The aims of the study were to (1) extend a previously published disease spread model with additional management and control measures; (2) use the extended model to study the effect of the individual LA-MRSA control measures on the within-herd LA-MRSA prevalence; (3) evaluate the effect of control measures when they are implemented in combinations. From the individual control measures tested in the study, thorough cleaning was found to be most effective in reducing the LA-MRSA prevalence in the herd. When the different control measures were combined, cleaning together with disease surveillance had the largest impact on reducing the LA-MRSA and a higher chance of causing disease elimination. The results of the study showed that achieving disease elimination once LA-MRSA had been introduced in the herd was challenging but was more likely when control measures were introduced early during the outbreak. This emphasises the importance of early detection of the pathogen and subsequent rapid implementation of LA-MRSA control measures.

The Swedish National Veterinary Institute (SVA) is working on implementing reusable and adaptable workflows for epidemiological analysis and dynamic report generation to improve disease surveillance. Important components of this work include: data access, development environment, computational resources and cloud-based management. The development environment relies on Git for code collaboration and version control and the R language for statistical computing and data visualization. The computational resources include both local and cloud-based systems, with automatic workflows managed in the cloud. The workflows are designed to be flexible and adaptable to changing data sources and stakeholder demands, with the ultimate goal to create a robust infrastructure for the delivery of actionable epidemiological information.

The circulation of livestock pathogens in the pig industry is strongly related to animal movements. Epidemiological models developed to understand the circulation of pathogens within the industry should include the probability of transmission via between-farm contacts. The pig industry presents a structured network in time and space, whose composition changes over time. Therefore, to improve the predictive capabilities of epidemiological models, it is important to identify the drivers of farmers' choices in terms of trade partnerships. Combining complex network analysis approaches and exponential random graph models, this study aims to analyze patterns of the swine industry network and identify key factors responsible for between-farm contacts at the French scale. The analysis confirms the topological stability of the network over time while highlighting the important roles of companies, types of farm, farm sizes, outdoor housing systems and batch-rearing systems. Both approaches revealed to be complementary and very effective to understand the drivers of the network. Results of this study are promising for future developments of epidemiological models for livestock diseases. This study is part of the One Health European Joint Programme: BIOPIGEE.

An inventory of data sources to be used in the Digivet case studies, along with project documentation that gives them context. FAIRer datasets, in which context and data are stored in linked formats will be produced in next steps of the project, and also published in the Digivet community.