Description of swine producer biosecurity planning for foreign animal disease preparedness using the Secure Pork Supply framework.

Introduction: Preventing potential foreign animal diseases is a high priority, with re-emerging threats such as African Swine Fever emerging close to North American borders. The Secure Pork Supply (SPS) plan provides a voluntary framework for swine producer biosecurity planning and disease outbreak preparedness. However, biosecurity knowledge varies greatly among swine veterinarians, managers, and caretakers within the industry, which impacts the understanding, quality, implementation and biosecurity plan agreements with the SPS guidelines unless review procedures and quality control mechanisms are in place. Therefore, this study aimed to describe and identify the level of biosecurity planning agreements between producer-and/or swine veterinarian-made biosecurity plans for commercial swine sites and the SPS plan guidelines during a review process. Material and methods: Biosecurity maps (N = 368) and written plans (N = 247) were obtained from six Midwest swine companies/veterinary clinics. Maps were evaluated on accuracy and placement of mandatory map features based on SPS guidelines, and discrepancies between the development of producer-made biosecurity maps and written biosecurity plans. Multivariable mixed logistic regression analyses were conducted to identify differences in SPS planning accuracy based on herd size, production stage, and characteristics related to geographical site location (e.g., land cover type and expected feral swine population density in the region). Results: In this study, 55.8% (205/368) of all provided biosecurity maps had to be revised due to misplaced or missing map features. In addition, 80.9% (200/247) of the written plans had one or more conflicts with the corresponding biosecurity maps. The main biosecurity planning issues involved feed delivery activities, where the mapping of vehicle movements (89.9%, 222/247) were in direct conflict with the written SPS plans. Sites located in areas with a moderate expected feral swine population density had 3-fold increased odds of needing map revisions compared to sites with low expected feral swine population density. Sites located in predominately farmland had 7.3% lower odds of having biosecurity map and SPS plan conflicts for every 1.0% increase in farmland landcover in a 10-km radius around the swine site. Discussion: Human oversight or lack of knowledge regarding biosecurity planning and implementation is common, which may culminate in important preparedness shortcomings in disease prevention and control strategies for U.S. swine farms. Future efforts should focus on additional biosecurity training for swine producers and veterinarians alongside with quality control benchmarking of producer made plans.

The role of vehicle movement in swine disease dissemination: Novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties.

Several propagation routes drive animal disease dissemination, and among these routes, contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used near-real-time vehicle movement data and vehicle cleaning efficacy to reconstruct the between-farm dissemination of the African swine fever virus (ASFV). We collected one year of Global Positioning System data of 823 vehicles transporting feed, pigs, and people to 6363 swine production farms in two regions in the U.S. Without cleaning, vehicles connected up to 2157 farms in region one and 437 farms in region two. Individually, in region one vehicles transporting feed connected 2151 farms, pigs to farms 2089 farms, pigs to market 1507 farms, undefined vehicles 1760 farm, and personnel three farms. The simulation results indicated that the contact networks were reduced the most for crew transport vehicles with a 66% reduction, followed by vehicles carrying pigs to market and farms, with reductions of 43% and 26%, respectively, when 100% cleaning efficacy was achieved. The results of this study showed that even when vehicle cleaning and disinfection are 100% effective, vehicles are still connected to numerous farms. This emphasizes the importance of better understanding transmission risks posed by vehicles to the swine industry and regulatory agencies.

Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.

Estimating the effectiveness of control actions on African swine fever transmission in commercial swine populations in the United States.

Given the proximity of African swine fever (ASF) to the U.S., there is an urgent need to better understand the possible dissemination pathways of the virus within the U.S. swine industry and to evaluate mitigation strategies. Here, we extended PigSpread, a farm-level spatially-explicit stochastic compartmental transmission model incorporating six transmission routes including between-farm swine movements, vehicle movements, and local spread, to model the dissemination of ASF. We then examined the effectiveness of control actions similar to the ASF national response plan. The average number of secondary infections during the first 60 days of the outbreak was 49 finisher farms, 17 nursery farms, 5 sow farms, and less than one farm in other production types. The between-farm movements of swine were the predominant route of ASF transmission with an average contribution of 71.1%, while local spread and movement of vehicles were less critical with average contributions of 14.6% and 14.4%. We demonstrated that the combination of quarantine, depopulation, movement restrictions, contact tracing, and enhanced surveillance, was the most effective mitigation strategy, resulting in an average reduction of 79.0% of secondary cases by day 140 of the outbreak. Implementing these control actions led to a median of 495,619 depopulated animals, 357,789 diagnostic tests, and 54,522 movement permits. Our results suggest that the successful elimination of an ASF outbreak is likely to require the deployment of all control actions listed in the ASF national response plan for more than 140 days, as well as estimating the resources needed for depopulation, testing, and movement permits under these controls.

Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: Characterizing the dominant transmission routes.

The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease prevention, control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine pathways of between-farm transmission represented by a contact network of pig movements between sites, farm-to-farm proximity (local transmission), four distinct contact networks of transportation vehicles (trucks that transport pigs from farm-to-farm and farm-to-markets, as well as trucks transporting feed and staff), the volume of animal by-products in feed diets (e.g., fat and meat-and-bone-meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs between farms, and for both farm types, local transmission or pig movements were the next most relevant transmission routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs between farms. Feed ingredients did not significantly improve model calibration metrics, sensitivity, and specificity; therefore, it was considered to have a negligible contribution in the dissemination of PEDV. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes.

Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever.

The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National Veterinary Service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges. Furthermore, we also created a network of parishes as its nodes by aggregating their premises movements as edges. The annual network metrics showed an average diameter of 20.33, a number of neighbours of 2.61, a shortest path length of 4.39 and a clustering coefficient of 0.38 (small-world structure). The most frequent movements were to or from markets (55%). Backyard producers made up 89% of the network premises, and the top 2% of parishes (highest degree) contributed to 50% of the movements. The highest frequencies of movements between parishes were in the centre of the country, while the highest frequency of movements to abattoirs was in the south-west. Finally, the pattern of classical swine fever (CSF) disease outbreaks within the Ecuador network was likely the result of network transmission processes. In conclusion, our results represented the first exploratory analysis of domestic pig movements at premise and parish levels. The surveillance system could consider these results to improve its procedures and update the disease control and management policy, and allow the implementation of targeted or risk-based surveillance.

Modelling and assessing additional transmission routes for porcine reproductive and respiratory syndrome virus: Vehicle movements and feed ingredients.

Accounting for multiple modes of livestock disease dissemination in epidemiological models remains a challenge. We developed and calibrated a mathematical model for transmission of porcine reproductive and respiratory syndrome virus (PRRSV), tailored to fit nine modes of between-farm transmission pathways including: farm-to-farm proximity (local transmission), contact network of batches of pigs transferred between farms (pig movements), re-break probabilities for farms with previous PRRSV outbreaks, with the addition of four different contact networks of transportation vehicles (vehicles to transport pigs to farms, pigs to markets, feed and crew) and the amount of animal by-products within feed ingredients (e.g., animal fat or meat and bone meal). The model was calibrated on weekly PRRSV outbreaks data. We assessed the role of each transmission pathway considering the dynamics of specific types of production (i.e., sow, nursery). Although our results estimated that the networks formed by transportation vehicles were more densely connected than the network of pigs transported between farms, pig movements and farm proximity were the main PRRSV transmission routes regardless of farm types. Among the four vehicle networks, vehicles transporting pigs to farms explained a large proportion of infections, sow = 20.9%; nursery = 15%; and finisher = 20.6%. The animal by-products showed a limited association with PRRSV outbreaks through descriptive analysis, and our model results showed that the contribution of animal fat contributed only 2.5% and meat and bone meal only .03% of the infected sow farms. Our work demonstrated the contribution of multiple routes of PRRSV dissemination, which has not been deeply explored before. It also provides strong evidence to support the need for cautious, measured PRRSV control strategies for transportation vehicles and further research for feed by-products modelling. Finally, this study provides valuable information and opportunities for the swine industry to focus effort on the most relevant modes of PRRSV between-farm transmission.

The between-farm transmission dynamics of porcine epidemic diarrhoea virus: A short-term forecast modelling comparison and the effectiveness of control strategies.

A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly porcine epidemic diarrhoea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems, SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.

Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus.

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.