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Introduction: Prescribing fenbendazole medicated feed for pheasants in the USA is considered extra-label drug use under CPG Sec 615.115, and a safe estimated withdrawal interval (WDI) must be applied following administration to this minor food-producing species. This study sought to determine the pharmacokinetic and residue depletion profile for fenbendazole and its major metabolites to estimate a WDI for pheasants following fenbendazole administration as an oral medicated feed., Method: Pheasants ( n = 32) were administered fenbendazole as an oral medicated feed (100 ppm) for 7 days. Fenbendazole, fenbendazole sulfoxide, and fenbendazole sulfone (FBZ-SO 2 ) in liver and muscle samples were analyzed using HPLC-UV. Tissue WDIs were estimated using FDA, European Medicines Agency (EMA), and half-life multiplication methods for US poultry tolerances, EMA maximum residue limits, and the analytical limit of detection (LOD; 0.004 ppm). Terminal tissue elimination half-lives (T 1/2 ) were estimated by non-compartmental analysis using a naïve pooled data approach., Results: The tissue T 1/2 was 14.4 h for liver, 13.2 h for thigh muscle, and 14.1 h for pectoral muscle. The maximum estimated withdrawal interval was 153 h (7 days) for FBZ-SO 2 in pectoral muscle using the FDA tolerance method (95% confidence interval for the 99 th percentile of the population), and the LOD as the residue limit., Discussion: The results from this study support the use of FBZ-SO 2 as the marker residue in the liver of pheasants and the provision of evidence based WDIs following the extra-label administration of fenbendazole medicated feed (100 ppm) for 7 days., Competing Interests: The 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., (Copyright © 2024 Carreño Gútiez, Mercer, Martínez-López, Griffith, Wetzlich and Tell.)

Globalization of trade, and the interconnectivity of animal production systems, continues to challenge efforts to control disease. A better understanding of trade networks supports development of more effective strategies for mitigation for transboundary diseases like African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). North Macedonia, bordered to the north and east by countries with ongoing ASF outbreaks, recently reported its first incursion of ASF. This study aimed to describe the distribution of pigs and pig farms in North Macedonia, and to characterize the live pig movement network. Network analyses on movement data from 2017 to 2019 were performed for each year separately, and consistently described weakly connected components with a few primary hubs that most nodes shipped to. In 2019, the network demonstrated a marked decrease in betweenness and increase in communities. Most shipments occurred within 50 km, with movements <6 km being the most common (22.5%). Nodes with the highest indegree and outdegree were consistent across years, despite a large turnover among smallholder farms. Movements to slaughterhouses predominated (85.6%), with movements between farms (5.4%) and movements to market (5.8%) playing a lesser role. This description of North Macedonia's live pig movement network should enable implementation of more efficient and cost-effective mitigation efforts strategies in country, and inform targeted educational outreach, and provide data for future disease modeling, in the region., Competing Interests: The 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., (Copyright © 2022 O'Hara, Beltrán-Alcrudo, Hovari, Tabakovski and Martínez-López.)

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer ( n = 27,390) and visit ( n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data., Competing Interests: The 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., (Copyright © 2022 Díaz Cao, Kent, Rupasinghe and Martínez-López.)

Objective: Many antimicrobial resistance (AMR) studies in both human and veterinary medicine use traditional statistical methods that consider one bacteria and one antibiotic match at a time. A more robust analysis of AMR patterns in groups of animals is needed to improve on traditional methods examining antibiotic resistance profiles, the associations between the patterns of resistance or reduced susceptibility for all isolates in an investigation. The use of Bayesian network analysis can identify associations between distributions; this investigation seeks to add to the growing body of AMR pattern research by using Bayesian networks to identify relationships between susceptibility patterns in Escherichia coli ( E. coli ) isolates obtained from weaned dairy heifers in California., Methods: A retrospective data analysis was performed using data from rectal swab samples collected from 341 weaned dairy heifers on six farms in California and selectively cultured for E. coli . Antibiotic susceptibility tests for 281 isolates against 15 antibiotics were included. Bayesian networks were used to identify joint patterns of reduced susceptibility, defined as an increasing trend in the minimum inhibitory concentration (MIC) values. The analysis involved learning the network structure, identifying the best fitting graphical mode, and learning the parameters in the final model to quantify joint probabilities., Results: The graph identified that as susceptibility to one antibiotic decreases, so does susceptibility to other antibiotics in the same or similar class. The following antibiotics were connected in the final graphical model: ampicillin was connected to ceftiofur; spectinomycin was connected with trimethoprim-sulfamethoxazole, and this association was mediated by farm; florfenicol was connected with tetracycline., Conclusions: Bayesian network analysis can elucidate complex relationships between MIC patterns. MIC values may be associated within and between drug classes, and some associations may be correlated with farm of sample origin. Treating MICs as discretized variables and testing for joint associations in trends may overcome common research problems surrounding the lack of clinical breakpoints., Competing Interests: The 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., (Copyright © 2022 Morgan, Depenbrock and Martínez-López.)

North Macedonia, a country in the Balkan region of Europe, is currently bordered to the north and east by countries with active African swine fever (ASF) outbreaks. The predominantly traditional backyard pig farming sector in this country is under imminent threat of disease incursion. The characteristics and practices of such sectors have rarely been described, and thus the implications for these factors on disease introduction and spread are poorly understood. Using a semi-structured questionnaire, 457 pig producers were interviewed, providing information on 77.7% of the pig population in North Macedonia. In addition, a pilot study of 25 pig producers in Kosovo was performed. This study aimed to provide a detailed description of the North Macedonian pig sector, to make comparisons with nearby Kosovo, and to identify areas with high-risk practices for targeted mitigation. Descriptive data were summarized. Results of the questionnaire were used to identify farm-level risk factors for disease introduction. These factors were used in the calculation of a biosecurity risk score. Kernel density estimation methods were used to generate density maps highlighting areas where the risk of disease introduction was particularly concentrated. Multiple correspondence analysis with hierarchical clustering on principal components was used to explore patterns in farm practices. Results show that farms were predominantly small-scale with high rates of turnover. Pig movement was predominantly local. The highest biosecurity risk scores were localized in the eastern regions of North Macedonia, concerningly the same regions with the highest frequency of wild boar sightings. Veterinarians were highly regarded, regularly utilized, and trusted sources of information. Practices that should be targeted for improvement include isolation of new pigs, and consistent application of basic sanitary practices including washing hands, use of disinfection mats, and separation of clean and dirty areas. This study provides the most complete description of the North Macedonian pig sector currently available. It also identifies regions and practices that could be targeted to mitigate the risk of disease incursion and spread. These results represent the first steps to quantify biosecurity gaps and high-risk behaviors in North Macedonia, providing baseline information to design risk-based, more cost-effective, prevention, surveillance, and control strategies., Competing Interests: The 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 reviewer EK declared a past co-authorship with one of the authors BM-L to the handling editor., (Copyright © 2021 O'Hara, Beltrán-Alcrudo, Hovari, Tabakovski and Martínez-López.)

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance., Competing Interests: The 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., (Copyright © 2021 Kim, Lee, Rupasinghe, Rezaei, Martínez-López and Liu.)

Fenbendazole is a benzimidazole-class anthelmintic that is used for the control of immature and adult stages of internal parasites, such as nematodes and trematodes, in domestic food-animal species. It is not approved by the United States Food and Drug Administration for treating pheasants despite Syngamus trachea being one of the most prevalent nematodes that parasitize pheasants. Because it is a highly effective treatment, e.g., 90% effectiveness against S. trachea , and there are very few alternative therapeutic options, this anthelminthic is used in an extra-label manner in the pheasant industry, but few studies have been conducted assessing risks to humans. Therefore, we conducted a risk assessment to evaluate the potential repeat-dose and reproductive, teratogenic, and carcinogenic human risks that may be associated with the consumption of tissues from pheasants that were previously treated with fenbendazole. We conducted a quantitative risk assessment applying both deterministic and stochastic approaches using different fenbendazole sulfone residue limits (tolerance, maximum residue limits, and analytical limit of detection) established in different poultry species by the Food and Drug Administration, the European Medicines Agency, and other regulatory agencies in Japan, Turkey, and New Zealand. Our results show that fenbendazole poses minimal risk to humans when administered to pheasants in an extra-label manner, and a comparison of different fenbendazole sulfone residue limits can help assess how conservative the withdrawal interval should be after extra-label drug use., Competing Interests: The 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., (Copyright © 2021 Carreño Gútiez, Tell and Martínez-López.)

Peste des Petits Ruminants (PPR) is endemic in Bangladesh, but its spatial distribution and risk factors have not yet been reported. Using four years of national-level, passive surveillance data (2014 to 2017), in this study we aimed to identify risk factors, create PPR risk maps and describe PPR time-space clusters. We selected PPR case records-mainly based on presumptive diagnosis of small ruminants in subdistrict veterinary hospitals-and sheep and goat population data from all 64 districts of Bangladesh. Peste des Petits Ruminants cumulative incidence per 10,000 animals at risk per district was used to conduct cluster and hotspot analysis and create predictive maps for each year and all 4 years combined. The association between PPR cumulative incidence and hypothesized risk factors-including climatic variables, elevation, road length, river length, railroad length, land cover, and water bodies-was analyzed using a geographically weighted regression model. The total number of PPR cases reported during the study period was 5.2 million. We found that most PPR cases (27.6%) were reported in the monsoon season. The highest and lowest proportions of cases were reported from Rajshahi (36.1%) and Barisal divisions (2.1%), respectively. We identified five space-time clusters, 9 high-high clusters, and 9 hotspots. The predicted cumulative incidences of PPR were persistently higher in north-east, north-west, and south-east parts of Bangladesh. Road length ( P = 0.03) was positively associated with PPR incidence in Bangladesh. Results suggest that movement of animals (road length) plays an important role in the epidemiology of PPR in Bangladesh. Along with restriction of animal movement, hotspots and high-high clusters should be targeted first for immunization coverage in Bangladesh and similar PPR endemic countries to achieve eradication., Competing Interests: The 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., (Copyright © 2021 Rahman, Islam, Sufian, Talukder, Ward and Martínez-López.)

Cardiomyopathy syndrome (CMS) is a severe cardiac disease of Atlantic salmon caused by the piscine myocarditis virus (PMCV), which was first reported in Ireland in 2012. In this paper, we describe the use of data-driven network modeling as a framework to evaluate the transmission of PMCV in the Irish farmed Atlantic salmon population and the impact of different mitigation measures. Input data included live fish movement data from 2009 to 2017, population dynamics events and the spatial location of the farms. With these inputs, we fitted a network-based stochastic infection spread model. After assumed initial introduction of the agent in 2009, our results indicate that it took 5 years to reach a between-farm prevalence of 100% in late 2014, with older fish being most affected. Local spread accounted for only a small proportion of new infections, being more important for sustained infection in a given area. Spread via movement of subclinically infected fish was most important for explaining the observed countrywide spread of the agent. Of the targeted intervention strategies evaluated, the most effective were those that target those fish farms in Ireland that can be considered the most connected, based on the number of farm-to-farm linkages in a specific time period through outward fish movements. The application of these interventions in a proactive way (before the first reported outbreak of the disease in 2012), assuming an active testing of fish consignments to and from the top 8 ranked farms in terms of outward fish movement, would have yielded the most protection for the Irish salmon farming industry. Using this approach, the between-farm PMCV prevalence never exceeded 20% throughout the simulation time (as opposed to the simulated 100% when no interventions are applied). We argue that the Irish salmon farming industry would benefit from this approach in the future, as it would help in early detection and prevention of the spread of viral agents currently exotic to the country., (Copyright © 2020 Yatabe, Martínez-López, Díaz-Cao, Geoghegan, Ruane, Morrissey, McManus, Hill and More.)

China's pork industry has been dramatically changing in the last few years. Pork imports are increasing, and small-scale farms are being consolidated into large-scale multi-site facilities. These industry changes increase the need for traceability and science-based decisions around disease monitoring, surveillance, risk mitigation, and outbreak response. This study evaluated the network structure and dynamics of a typical large-scale multi-site swine facility in China, as well as the implications for disease spread using network-based metrics. Forward reachability paths were used to demonstrate the extent of epidemic spread under variable site and temporal disease introductions. Swine movements were found to be seasonal, with more movements at the beginning of the year, and fewer movements of larger pigs later in the year. The network was highly egocentric, with those farms within the evaluated production system demonstrating high connectivity. Those farms which would contribute the highest epidemic potential were identified. Among these, different farms contributed to higher expected epidemic spread at different times of the year. Using these approaches, increased availability of swine movement networks in China could help to identify priority locations for surveillance and risk mitigation for both endemic problems and transboundary diseases such as the recently introduced, and rapidly spreading, African swine fever virus., (Copyright © 2020 O'Hara, Zhang, Jung, Zhou, Qian and Martínez-López.)

Based on the 2016 National Cattlemen's Beef Association statistics, the cattle inventory in the US reached 93.5 million head, from which 30.5 million were commercial slaughter in 2016. California ranked fourth among all the US states that raise cattle and calves, with 5.15 million head and approximately 1.18 million slaughtered animals per year. Approximately 0.5% of cattle carcasses in the US are condemned each year, which has an important economic impact on cattle producers.In this study, we first described and compared the temporal trends of cattle carcass condemnations in all the US states from Jan-2005 to Dec-2014. Then, we focused on the condemnation reasons with a seasonal component in California and used dynamic harmonic regression (DHR) models both to model (from Jan-2005 to Dec-2011) and predict (from Jan-2012 to Dec-2014) the carcass condemnations rate in different time horizons (3 to 12 months).Data consisted of daily reports of 35 condemnation reasons per cattle type reported in 684 federally inspected slaughterhouses in the US from Jan-2005 to Dec-2014 and the monthly slaughtered animals per cattle type per states. Almost 1.5 million carcasses were condemned in the US during the 10 year study period (Jan 2005-Dec 2014), and around 40% were associated with three condemnation reasons: malignant lymphoma, septicemia and pneumonia. In California, emaciation, eosinophilic myositis and malignant lymphoma were the only condemnation reasons presenting seasonality and, therefore, the only ones selected to be modeled using DHRs. The DHR models for Jan-2005 to Dec-2011 were able to correctly model the dynamics of the emaciation, malignant lymphoma and eosinophilic myositis condemnation rates with coefficient of determination ( R t 2 ) of 0.98, 0.87 and 0.78, respectively. The DHR models for Jan-2012 to Dec-2014 were able to predict the rate of condemned carcasses 3 month ahead of time with mean relative prediction error of 33, 11, and 38%, respectively. The systematic analysis of carcass condemnations and slaughter data in a more real-time fashion could be used to identify changes in carcass condemnation trends and more timely support the implementation of prevention and mitigation strategies that reduce the number of carcass condemnations in the US.

Sinonasal aspergillosis (SNA) causes chronic nasal discharge in dogs and has a worldwide distribution, although most reports of SNA in North America originate from the western USA. SNA is mainly caused by Aspergillus fumigatus , a ubiquitous saprophytic filamentous fungus. Infection is thought to follow inhalation of spores. SNA is a disease of the nasal cavity and/or sinuses with variable degrees of local invasion and destruction. While some host factors appear to predispose to SNA (such as belonging to a dolichocephalic breed), environmental risk factors have been scarcely studied. Because A. fumigatus is also the main cause of invasive aspergillosis in humans, unraveling the distribution and the environmental and climatic risk factors for this agent in dogs would be of great benefit for public health studies, advancing understanding of both distribution and risk factors in humans. In this study, we reviewed electronic medical records of 250 dogs diagnosed with SNA between 1990 and 2014 at the University of California Davis Veterinary Medical Teaching Hospital (VMTH). A 145-mile radius catchment area around the VMTH was selected. Data were aggregated by zip code and incorporated into a multivariate logistic regression model. The logistic regression model was compared to an autologistic regression model to evaluate the effect of spatial autocorrelation. Traffic density, active composting sites, and environmental and climatic factors related with wind and temperature were significantly associated with increase in disease occurrence in dogs. Results provide valuable information about the risk factors and spatial distribution of SNA in dogs in Northern California. Our ultimate goal is to utilize the results to investigate risk-based interventions, promote awareness, and serve as a model for further studies of aspergillosis in humans.

In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

Bushpigs (BPs) (Potamochoerus larvatus) and warthogs (WHs) (Phacochoerus africanus), which are widely distributed in Eastern Africa, are likely to cohabitate in the same environment with domestic pigs (DPs), facilitating the transmission of shared pathogens. However, potential interactions between BP, WH, and DP, and the resulting potential circulation of infectious diseases have rarely been investigated in Africa to date. In order to understand the dynamics of such interactions and the potential influence of human behavior and husbandry practices on them, individual interviews (n = 233) and participatory rural appraisals (n = 11) were carried out among Ugandan pig farmers at the edge of Murchison Falls National Park, northern Uganda. In addition, as an example of possible implications of wild and DP interactions, non-linear multivariate analysis (multiple correspondence analyses) was used to investigate the potential association between the aforementioned factors (interactions and human behavior and practices) and farmer reported African swine fever (ASF) outbreaks. No direct interactions between wild pigs (WPs) and DP were reported in our study area. However, indirect interactions were described by 83 (35.6%) of the participants and were identified to be more common at water sources during the dry season. Equally, eight (3.4%) farmers declared exposing their DP to raw hunting leftovers of WPs. The exploratory analysis performed suggested possible associations between the farmer reported ASF outbreaks and indirect interactions, free-range housing systems, dry season, and having a WH burrow less than 3 km from the household. Our study was useful to gather local knowledge and to identify knowledge gaps about potential interactions between wild and DP in this area. This information could be useful to facilitate the design of future observational studies to better understand the potential transmission of pathogens between wild and DPs.

Understanding the complexity of live pig trade organization is a key factor to predict and control major infectious diseases, such as classical swine fever (CSF) or African swine fever (ASF). Whereas the organization of pig trade has been described in several European countries with indoor commercial production systems, little information is available on this organization in other systems, such as outdoor or small-scale systems. The objective of this study was to describe and compare the spatial and functional organization of live pig trade in different European countries and different production systems. Data on premise characteristics and pig movements between premises were collected during 2011 from Bulgaria, France, Italy, and Spain, which swine industry is representative of most of the production systems in Europe (i.e., commercial vs. small-scale and outdoor vs. indoor). Trade communities were identified in each country using the Walktrap algorithm. Several descriptive and network metrics were generated at country and community levels. Pig trade organization showed heterogeneous spatial and functional organization. Trade communities mostly composed of indoor commercial premises were identified in western France, northern Italy, northern Spain, and north-western Bulgaria. They covered large distances, overlapped in space, demonstrated both scale-free and small-world properties, with a role of trade operators and multipliers as key premises. Trade communities involving outdoor commercial premises were identified in western Spain, south-western and central France. They were more spatially clustered, demonstrated scale-free properties, with multipliers as key premises. Small-scale communities involved the majority of premises in Bulgaria and in central and Southern Italy. They were spatially clustered and had scale-free properties, with key premises usually being commercial production premises. These results indicate that a disease might spread very differently according to the production system and that key premises could be targeted to more cost-effectively control diseases. This study provides useful epidemiological information and parameters that could be used to design risk-based surveillance strategies or to more accurately model the risk of introduction or spread of devastating swine diseases, such as ASF, CSF, or foot-and-mouth disease.