Individual or Common Good? Voluntary Data Sharing to Inform Disease Surveillance Systems in Food Animals

Enhancing disease surveillance and preparedness: An early warning tool for disease occurrence in U.S. swine breeding herds

Understanding regional disease risk is critical for swine disease prevention and control. Since 2011, the Morrison Swine Health Monitoring Project (MSHMP) has strengthened partnerships among practitioners and producers to report health events (e.g., porcine reproductive and respiratory syndrome (PRRS) virus outbreaks) at the U.S. national level. Using MSHMP data and PRRS as an example, an early regional occurrence warning tool to provide near-real-time alerts was developed. MSHMP-participating production systems were invited to enroll. An algorithm was developed to calculate the number of PRRSV-positive sites near each enrolled site, determined from site-specific radius. The radius was determined in three steps. First, an initial radius of 25 miles was set for sites in pig-dense states and 50 miles for others. Secondly, four variables were generated to account for the sites within the initial radius: A) Total number of PRRSV-positive sites; B) Number of PRRSV-positive sites from other production systems; C) Total number of sites enrolled, and D) Total number of sites monitored by MSHMP. Subsequently, the reporting radius was automatically increased when confidentiality concerns arose. Results were compiled into system-specific reports and shared weekly with each participant. Reports have been shared since May 9, 2023, representing 178 breeding sites, comprising approximately 565 K sows. Examples of how participants use these reports include adjusting biosecurity programs, frequency of supply introduction, and transportation routes. The early occurrence warning tool developed in this study enhances producers' ability to communicate effectively and respond quickly to health threats, mitigating regional disease while preparing for foreign disease introductions.

Postmortem Sampling in Piglet Populations: Unveiling Specimens Accuracy for Porcine Reproductive and Respiratory Syndrome Detection

Specimens collected from dead pigs are a welfare-friendly and cost-effective active surveillance. This study aimed to evaluate the accuracy of different postmortem specimens from dead piglets for disease detection, using PRRSV as an example. Three farrow-to-wean farms undergoing PRRSV elimination were conveniently selected. Samples were collected at approximately 8- and 20-weeks post-outbreak. Postmortem specimens included nasal (NS), oral (OS), and rectal (RS) swabs, tongue-tip fluids (TTF), superficial inguinal lymph nodes (SIL), and intracardiac blood. These were tested individually for PRRSV by RT-PCR. Sensitivity, specificity, negative and positive predictive values, and agreement of postmortem specimens were calculated using intracardiac sera as the gold standard. OS and SIL had the best overall performance, with sensitivities of 94.6-100%, specificities of 83.9-85.1%, and negative predictive values of 97.3-100%. TTF had high sensitivity (92.2%) but low specificity (53.9%) and positive predictive value (48.3%). While challenges in meeting sampling targets due to variable pre-weaning mortality were noted, PRRS was detected in all postmortem specimens. OS and NS showed promising results for disease monitoring, though TTF, despite their sensitivity, had lower specificity, making them less suitable for individual infection assessment but useful for assessing environmental contamination.

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Disentangling transport movement patterns of trucks either transporting pigs or while empty within a swine production system before and during the COVID-19 epidemic

Transport of pigs between sites occurs frequently as part of genetic improvement and age segregation. However, a lack of transport biosecurity could have catastrophic implications if not managed properly as disease spread would be imminent. However, there is a lack of a comprehensive study of vehicle movement trends within swine systems in the Midwest. In this study, we aimed to describe and characterize vehicle movement patterns within one large Midwest swine system representative of modern pig production to understand movement trends and proxies for biosecurity compliance and identify potential risky behaviors that may result in a higher risk for infectious disease spread. Geolocation tracking devices recorded vehicle movements of a subset of trucks and trailers from a production system every 5 min and every time tracks entered a landmark between January 2019 and December 2020, before and during the COVID-19 pandemic. We described 6,213 transport records from 12 vehicles controlled by the company. In total, 114 predefined landmarks were included during the study period, representing 5 categories of farms and truck wash facilities. The results showed that trucks completed the majority (76.4%, 2,111/2,762) of the recorded movements. The seasonal distribution of incoming movements was similar across years (P > 0.05), while the 2019 winter and summer seasons showed higher incoming movements to sow farms than any other season, year, or production type (P < 0.05). More than half of the in-movements recorded occurred within the triad of sow farms, wean-to-market stage, and truck wash facilities. Overall, time spent at each landmark was 9.08% higher in 2020 than in 2019, without seasonal highlights, but with a notably higher time spent at truck wash facilities than any other type of landmark. Network analyses showed high connectivity among farms with identifiable clusters in the network. Furthermore, we observed a decrease in connectivity in 2020 compared with 2019, as indicated by the majority of network parameter values. Further network analysis will be needed to understand its impact on disease spread and control. However, the description and quantification of movement trends reported in this study provide findings that might be the basis for targeting infectious disease surveillance and control.

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.

When Everything Becomes Bigger: Big Data for Big Poultry Production

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data ("big data") is pervasive in both modern society and the farming industry. Information accessibility-coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms-offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms' population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies-and because several unknowns, limitations, and open-ended questions are inevitable-their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis-together with the use of open standards and integration with pathogen molecular epidemiology-have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health.

A recombinant porcine reproductive and respiratory syndrome virus type 2 field strain derived from two PRRSV-2-modified live virus vaccines

A porcine reproductive and respiratory syndrome virus (PRRSV) type 2 (PRRSV-2) isolate was obtained from lung samples collected from a 4.5-month-old pig at a wean-to-finish site in Indiana, USA, although no gross or microscopic lesions suggestive of PRRSV infection were observed in the lung tissue. Phylogenetic and molecular evolutionary analyses based on the obtained virus sequences indicated that PRRSV USA/IN105404/2021 was a natural recombinant isolate from Ingelvac PRRS® MLV and Prevacent® PRRS, which are PRRSV-2-modified live virus vaccines commercially available in the United States. This study is the first to report the detection of a PRRSV-2 recombinant strain consisting entirely of two modified live virus vaccine strains under field conditions. Based on clinical data and the absence of lung lesions, this PRRSV-2 recombinant strain was not virulent in swine, although its pathogenicity needs to be confirmed by clinical trials.

Time Farms Stay Naïve for Porcine Reproductive and Respiratory Syndrome

BACKGROUND

Hesitation on eliminating Porcine Reproductive and Respiratory Syndrome virus (PRRSV) from breeding herds exists since it is difficult to predict how long the herd will remain virus-free. We aimed to estimate the time that breeding herds remained virus-free (naïve) after PRRSV elimination was achieved.

METHODS

Production systems voluntarily shared their breeding herds' health status weekly between July 2009 and October 2021. PRRSV incidence rate and the total number of days a breeding herd remained virus-free were estimated.

RESULTS

A total of 221 (17%) herds reached the naïve status 273 times. The median time sites remained in this status was approximately two years. The overall PRRS incidence rate after sites achieved a naïve status was 23.43 PRRS outbreaks per 100 farm years.

CONCLUSION

Estimates obtained here provide insights on how frequently and for how long sites remain naïve, which contribute to informing management practices for PRRS control.

Evaluating oral swab samples for PRRSV surveillance in weaning-age pigs under field conditions

Introduction

The use of serum and family oral fluids for porcine reproductive and respiratory syndrome virus (PRRSV) surveillance in weaning-age pigs has been previously characterized. Characterizing more sample types similarly offers veterinarians and producers additional validated sample options for PRRSV surveillance in this subpopulation of pigs. Oral swab sampling is relatively easy and convenient; however, there is sparse information on how it compares to the reference sample type for PRRSV surveillance under field conditions. Therefore, this study's objective was to compare the PRRSV reverse-transcription real-time polymerase chain reaction (RT-rtPCR) test outcomes of oral swabs (OS) and sera samples obtained from weaning-age pig litters.

Method

At an eligible breeding herd, six hundred twenty-three weaning-age piglets from 51 litters were each sampled for serum and OS and tested for PRRSV RNA by RT-rtPCR.

Results and Discussion

PRRSV RT-rtPCR positivity rate was higher in serum samples (24 of 51 litters, 83 of 623 pigs, with a mean cycle threshold (Ct) value of RT-rtPCR-positive samples per litter ranging from 18.9 to 32.0) compared to OS samples (15 of 51 litters, 33 of 623 pigs, with a mean Ct of RT-rtPCR positive samples per litter ranging from 28.2 to 36.9); this highlights the importance of interpreting negative RT-rtPCR results from OS samples with caution. Every litter with a positive PRRSV RT-rtPCR OS had at least one viremic piglet, highlighting the authenticity of positive PRRSV RT-rtPCR tests using OS; in other words, there was no evidence of environmental PRRSV RNA being detected in OS. Cohen's kappa analysis (Ck = 0.638) indicated a substantial agreement between both sample types for identifying the true PRRSV status of weaning-age pigs.

Enhancing passive surveillance for African swine fever detection on U.S. swine farms

As the threat of African swine fever (ASF) introduction into new areas continues, animal health officials and epidemiologists need novel tools for early detection and surveillance. Passive surveillance from swine producers and veterinarians is critical to identify cases, especially the first introduction. Enhanced passive surveillance (EPS) protocols are needed that maximize temporal sensitivity for early ASF detection yet are easily implemented. Regularly collected production and disease data on swine farms may pose an opportunity for developing EPS protocols. To better understand the types of data regularly collected on swine farms and on-farm disease surveillance, a questionnaire was distributed in summer 2022 across multiple channels to MN swine producers. Thirty responses were received that indicated the majority of farms collect various types of disease information and conduct routine diagnostic testing for endemic swine diseases. Following this, a focus group discussion was held at the 2022 Leman Swine Conference where private and public stakeholders discussed the potential value of EPS, opportunities for collaboration, and challenges. The reported value of EPS varied by stakeholder group, but generally participants felt that for swine producers and packers, EPS would help identify abnormal disease occurrences. Many opportunities were identified for collaboration with ongoing industry initiatives and swine management software. Challenges included maintaining motivation for participation in ASF-free areas, labor, data sharing issues, and the cost of diagnostic testing. These highlight important issues to address, and future collaborations can help in the development of practical, fit-for-purpose, and valuable EPS protocols for ASF detection in the swine industry.

Porcine reproductive and respiratory syndrome prevalence and processing fluids use for diagnosis in United States breeding herds

Introduction

Processing fluids have been recently adopted by the U.S. swine industry as a breeding herd PRRS monitoring tool due to their increased representativeness of animals within the herd. Here, we use the Morrison Swine Health Monitoring Project (MSHMP) database, representative of ~50% of the U.S. swine breeding herd, to describe processing fluids submissions for PRRS diagnosis and their relation to PRRS prevalence and time to stability over time between 2009 and 2020.

Methods

An ecological time series Poisson regression modeling the number of status 1 farms and weekly percentage of processing fluids submissions for PRRS diagnosis was done. Time to stability was calculated for sites that detected a PRRS outbreak within the study period and modeled through a proportional hazards mixed effect survival model using production system as a random-effect factor and epiweek as a panel variable.

Results

Processing fluids diagnosis submissions increased starting in 2017. The difference between each year's highest and lowest weekly prevalence averaged 10.9% between 2009 and 2017, whereas it averaged 5.0% in 2018-2020 period. Each year's lowest weekly prevalence ranged from 11.3 to 19.5% in 2009-2017 and from 22.4 to 29.2% in 2018-2020. We also detected an increasing proportion of breeding sites that did not reach stability within 1 year of reporting an outbreak (chi-square for trend p < 0.0001). The total time to stability was not associated with the region of the country in which the site was located, the site's air filtration status, its PRRS status before the outbreak, or the different statuses a site achieved to be classified as stable, when accounting for the production system in the multivariate model. However, a higher proportion of system-wide processing fluids use was associated with increased time to stability.

Discussion

Altogether, the temporal concurrence of processing fluids used for PRRS virus monitoring suggests that the adoption of this sampling strategy may help explain the changes observed in PRRS status 1 prevalence since 2018, although further studies are still needed.

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.

Partitioning, a Novel Approach to Mitigate the Risk and Impact of African Swine Fever in Affected Areas

As African swine fever (ASF) continues to expand geographically, supplementary control strategies are needed to reduce disease risk and impact in affected areas. Full depopulation is central to current ASF control efforts, and its efficacy depends on surveillance and timely disease reporting, while resulting in large losses regardless of the producers' efforts to promptly detect, report, and contain the disease. This disconnect between prompt detection and reporting, and subsequent farm losses, can deter producers to invest in ASF detection and control. Alternative approaches are needed to incentivize individual producers to invest in early detection and reporting. We postulate that commercial swine farms may be effectively partitioned in separate units, or subpopulations, to which biosecurity, surveillance and control can be applied. The suggested Partitioning framework relies on three main components: 1. external and internal biosecurity to reduce the risk of ASF introduction and maintain separate subpopulations; 2. cost-effective on-farm ASF surveillance to enhance early detection; 3. response plans at the unit level, including culling of affected subpopulations, and demonstration of freedom from disease on the remaining ones. With such Partitioning approach, individual producers may reduce ASF risk on a farm and in the region, while also reducing ASF outbreak losses via targeted depopulation of affected units. It requires relevant legislation to incorporate the notion of within-farm subpopulations and provide a regulatory framework for targeted depopulation and substantiation of disease freedom. Its design should be tailored to fit individual farms. Partitioning can be an effective public-private partnership approach for ASF risk reduction. It should be driven by industry, as its benefits are accrued mainly by individual producers, but regulatory oversight is key to ensure proper implementation and avoid further disease spread. Partitioning's value is greatest for producers in ASF-affected regions, but ASF-free areas could also benefit from it for preparedness and early detection. It could also be adapted to other transboundary animal diseases and can be implemented as a stand-alone program or in conjunction with other efforts such as zoning and compartmentalization. Partitioning would contribute to the improved resilience and sustainability of the global pork industry and will benefit consumers and society through improved food security and animal welfare.

Porcine Reproductive and Respiratory Syndrome (PRRS) Epidemiology in an Integrated Pig Company of Northern Italy: A Multilevel Threat Requiring Multilevel Interventions

Porcine reproductive and respiratory syndrome (PRRS) is probably the most relevant viral disease affecting pig farming. Despite the remarkable efforts paid in terms of vaccination administration and biosecurity, eradication and long-term control have often been frustrated. Unfortunately, few studies are currently available that objectively link, using a formal statistical approach, viral molecular epidemiology to the risk factors determining the observed scenario. The purpose of the present study is to contribute to filling this knowledge gap taking advantage of the advancements in the field of phylodynamics. Approximately one-thousand ORF7 sequences were obtained from strains collected between 2004 and 2021 from the largest Italian pig company, which implements strict compartmentalization among independent three-sites (i.e., sow herds, nurseries and finishing units) pig flows. The history and dynamics of the viral population and its evolution over time were reconstructed and linked to managerial choices. The viral fluxes within and among independent pig flows were evaluated, and the contribution of other integrated pig companies and rurally risen pigs in mediating such spreading was investigated. Moreover, viral circulation in Northern Italy was reconstructed using a continuous phylogeographic approach, and the impact of several environmental features on PRRSV strain persistence and spreading velocity was assessed. The results demonstrate that PRRSV epidemiology is shaped by a multitude of factors, including pig herd management (e.g., immunization strategy), implementation of strict-independent pig flows, and environmental features (e.g., climate, altitude, pig density, road density, etc.) among the others. Small farms and rurally raised animals also emerged as a potential threat for larger, integrated companies. These pieces of evidence suggest that none of the implemented measures can be considered effective alone, and a multidimensional approach, ranging from individual herd management to collaboration and information sharing among different companies, is mandatory for effective infection control.

Porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) genetic diversity and occurrence of wild type and vaccine-like strains in the United States swine industry

Porcine reproductive and respiratory syndrome virus genotype 2 (PRRSV-2) genetic diversity in the U.S. was assessed using a database comprising 10 years’ worth of sequence data obtained from swine production systems routine monitoring and outbreak investigations. A total of 26,831 ORF5 PRRSV-2 sequences from 34 production systems were included in this analysis. Within group mean genetic distance (i.e. mean proportion of nucleotide differences within ORF5) per year according to herd type was calculated for all PRRSV-2 sequences. The percent nucleotide difference between each sequence and the ORF5 sequences from four commercially available PRRSV-2 vaccines (Ingelvac PRRS MLV, Ingelvac PRRS ATP, Fostera PRRS, and Prevacent PRRS) within the same lineage over time was used to classify sequences in wild-type or vaccine-like. The mean ORF5 genetic distance fluctuated from 0.09 to 0.13, being generally smaller in years in which there was a relative higher frequency of dominant lineage. Vaccine-like sequences comprised about one fourth of sequences obtained through routine monitoring of PRRS. We found that lineage 5 sequences were mostly Ingelvac PRRS MLV-like. Lineage 8 sequences up to 2011 were 62.9% Ingelvac PRRS ATP-like while the remaining were wild-type viruses. From 2012 onwards, 51.9% of lineage 8 sequences were Ingelvac PRRS ATP-like, 45.0% were Fostera PRRS-like, and only 3.2% were wild-type. For lineage 1 sequences, 0.1% and 1.7% of the sequences were Prevacent PRRS-like in 2009–2018 and 2019, respectively. These results suggest that repeated introductions of vaccine-like viruses through use of modified live vaccines might decrease within-lineage viral diversity as vaccine-like strains become more prevalent. Overall, this compilation of private data from routine monitoring provides valuable information on PRRSV viral diversity.

Emergence of a New Lineage 1C Variant of Porcine Reproductive and Respiratory Syndrome Virus 2 in the United States

We report an ongoing regional outbreak of an emerging porcine reproductive and respiratory syndrome virus (PRRSV2) variant within Lineage 1C affecting 154 breeding and grow-finishing sites in the Midwestern U.S. Transmission seemed to have occurred in two waves, with the first peak of weekly cases occurring between October and December 2020 and the second starting in April 2021. Most of cases occurred within a 120 km radius. Both orf5 and whole genome sequencing results suggest that this represents the emergence of a new variant within Lineage 1C distinct from what has been previously circulating. A case-control study was conducted with 50 cases (sites affected with the newly emerged variant) and 58 controls (sites affected with other PRRSV variants) between October and December 2020. Sites that had a market vehicle that was not exclusive to the production system had 0.04 times the odds of being a case than a control. A spatial cluster (81.42 km radius) with 1.68 times higher the number of cases than controls was found. The average finishing mortality within the first 4 weeks after detection was higher amongst cases (4.50%) than controls (0.01%). The transmission of a highly similar virus between different farms carrying on trough spring rises concerns for the next high transmission season of PRRS.

Description of changes of key performance indicators and PRRSV shedding over time in a naïve breeding herd following a PRRS MLV exposure

Porcine reproductive and respiratory syndrome (PRRS) is an important economic swine disease. The usage of PRRS-modified live vaccines (MLV) is the predominant breeding herd immunologic solution used in the United States to minimize the economic losses associated with wild-type PRRS infection. Most of the current information on the effects of contemporary PRRS MLV vaccination on breeding herd performance under field conditions comes from herds with previous PRRS virus (PRRSV) exposure. Hence, there is little information on key performance indicators (KPI) changes after the exposure to a PRRS MLV in PRRSV-naïve breeding herds. The main objective of this longitudinal observational study was to describe selected KPI changes in a naïve breeding herd after PRRS MLV exposure. The secondary objective was to describe the pattern of detection of PRRSV RNA by the quantitative reverse transcriptase-polymerase chain reaction in processing fluid samples. There were transient increases for mummies during weeks 4-23 (+0.86%); increased pre-weaning mortality on weeks 3-5 (+3.76%); a decrease in live born on weeks 4-5 (-0.46) leading to a decreased pig weaned/litter on weeks 5-10 (-0.69) and increased repeated services on weeks 3-23 (+5.53%). Transient changes observed after PRRS MLV exposures did not move total pigs weaned to outside the control intervals. Starting on week 83 and for 53 consecutive weeks, there was no PRRSV detection in processing fluids, even though two whole-herd MLV exposures occurred within that period.

Integrating animal movements with phylogeography to model the spread of PRRSV in the USA

Viral sequence data coupled with phylodynamic models have become instrumental in investigating the outbreaks of human and animal diseases, and the incorporation of the hypothesized drivers of pathogen spread can enhance the interpretation from phylodynamic inference. Integrating animal movement data with phylodynamics allows us to quantify the extent to which the spatial diffusion of a pathogen is influenced by animal movements and contrast the relative importance of different types of movements in shaping pathogen distribution. We combine animal movement, spatial, and environmental data in a Bayesian phylodynamic framework to explain the spatial diffusion and evolutionary trends of a rapidly spreading sub-lineage (denoted L1A) of porcine reproductive and respiratory syndrome virus (PRRSV) Type 2 from 2014 to 2017. PRRSV is the most important endemic pathogen affecting pigs in the USA, and this particular virulent sub-lineage emerged in 2014 and continues to be the dominant lineage in the US swine industry to date. Data included 984 open reading frame 5 (ORF5) PRRSV L1A sequences obtained from two production systems in a swine-dense production region (∼85,000 mi²) in the USA between 2014 and 2017. The study area was divided into sectors for which model covariates were summarized, and animal movement data between each sector were summarized by age class (wean: 3–4 weeks; feeder: 8–25 weeks; breeding: ≥21 weeks). We implemented a discrete-space phylogeographic generalized linear model using Bayesian evolutionary analysis by sampling trees (BEAST) to infer factors associated with variability in between-sector diffusion rates of PRRSV L1A. We found that between-sector spread was enhanced by the movement of feeder pigs, spatial adjacency of sectors, and farm density in the destination sector. The PRRSV L1A strain was introduced in the study area in early 2013, and genetic diversity and effective population size peaked in 2015 before fluctuating seasonally (peaking during the summer months). Our study underscores the importance of animal movements and shows, for the first time, that the movement of feeder pigs (8–25 weeks old) shaped the spatial patterns of PRRSV spread much more strongly than the movements of other age classes of pigs. The inclusion of movement data into phylodynamic models as done in this analysis may enhance our ability to identify crucial pathways of disease spread that can be targeted to mitigate the spatial spread of infectious human and animal pathogens.

PRRSV2 genetic diversity defined by RFLP patterns in the United States from 2007 to 2019

The genetic diversity of porcine reproductive and respiratory syndrome virus (PRRSV) increases over time. In 1998, restriction-fragment length polymorphism (RFLP) pattern analysis was introduced to differentiate PRRSV wild-type strains from VR2332, a reference strain from which a commercial vaccine (Ingelvac PRRS MLV) was derived. We have characterized here the PRRSV genetic diversity within selected RFLP families over time and U.S. geographic space, using available ISU-VDL data from 2007 to 2019. The 40,454 ORF5 sequences recovered corresponded to 228 distinct RFLPs. Four RFLPs [2-5-2 (21.2%), 1-7-4 (15.6%), 1-4-4 (11.8%), and 1-8-4 (9.9%)] represented 58.5% of all ORF5 sequences and were used for cluster analysis. Over time, there was increased detection of RFLPs 2-5-2, 1-7-4, 1-3-4, 1-3-2, and 1-12-4; decreased detection of 1-4-2, 1-18-4, 1-18-2, and 1-2-2; and different detection trends for 1-8-4, 1-4-4, 1-26-1, 1-22-2, and 1-2-4. An over-time cluster analysis revealed a single cluster for RFLP 2-5-2, supporting that sequences within RFLP 2-5-2 are still relatively conserved. For 1-7-4, 1-4-4, and 1-8-4, there were multiple clusters. State-wise cluster analysis demonstrated 4 main clusters for RFLP 1-7-4 and 1-8-4, and 6 for RFLP 1-4-4. For the other RFLPs, there was a significant genetic difference within them, particularly between states. RFLP typing is limited in its ability to discriminate among different strains of PRRSV. Understanding the magnitude of genetic divergence within RFLPs helps develop PRRSV regional control programs, placement, herd immunization strategies, and design of appropriate animal movements across borders to minimize the risk of PRRSV transmission.

Cross-Sectoral Zoonotic Disease Surveillance in Western Kenya: Identifying Drivers and Barriers Within a Resource Constrained Setting

Background: Collaboration between the human and animal health sectors, including the sharing of disease surveillance data, has the potential to improve public health outcomes through the rapid detection of zoonotic disease events prior to widespread transmission in humans. Kenya has been at the forefront of embracing a collaborative approach in Africa with the inception of the Zoonotic Disease Unit in 2011. Joint outbreak responses have been coordinated at the national level, yet little is currently documented on cross-sectoral collaboration at the sub-national level.

Methods: Key informant interviews were conducted with 28 disease surveillance officers from the human and animal health sectors in three counties in western Kenya. An inductive process of thematic analysis was used to identify themes relating to barriers and drivers for cross-sectoral collaboration.

Results: The study identified four interlinking themes related to drivers and barriers for cross-sectoral collaboration. To drive collaboration at the sub-national level there needs to be a clear identification of “common objectives,” as currently exemplified by the response to suspected rabies and anthrax cases and routine meat hygiene activities. The action of collaboration, be it integrated responses to outbreaks or communication and data sharing, require “operational structures” to facilitate them, including the formalisation of reporting lines, supporting legislation and the physical infrastructure, from lab equipment to mobile phones, to facilitate the activities. These structures in turn require “appropriate resources” to support them, which will be allocated based on the “political will” of those who control the resources.

Conclusions: Ongoing collaborations between human and animal disease surveillance officers at the sub-national level were identified, driven by common objectives such as routine meat hygiene and response to suspected rabies and anthrax cases. In these areas a suitable operational structure is present, including a supportive legislative framework and clearly designated roles for officers within both sectors. There was support from disease surveillance officers to increase their collaboration, communication and data sharing across sectors, yet this is currently hindered by the lack of these formal operational structures and poor allocation of resources to disease surveillance. It was acknowledged that improving this resource allocation will require political will at the sub-national, national and international levels.

Dynamic network connectivity influences the spread of a sub-lineage of porcine reproductive and respiratory syndrome virus

Swine production in the United States is characterized by dynamic farm contacts through animal movements; such movements shape the risk of disease occurrence on farms. Pig movements have been linked to the spread of a virulent porcine reproductive and respiratory syndrome virus (PRRSV), RFLP type 1-7-4, herein denoted as phylogenetic sub-lineage 1A [L1A]. This study aimed to quantify the contribution of pig movements to the risk of L1A occurrence on farms in the United States. Farms were defined as L1A-positive in a given 6-month period if at least one L1A sequence was recovered from the farm. Temporal network autocorrelation modelling was performed using data on animal movements and 1,761 PRRSV ORF5 sequences linked to 494 farms from a dense pig production area in the United States between 2014 and 2017. A farm's current and past exposure to L1A and other PRRSV variants was assessed through its primary and secondary contacts in the animal movement network. Primary and secondary contacts with an L1A-positive farm increased the likelihood of L1A occurrence on a farm by 19% (p = .04) and 23% (p = .03), respectively. While the risk posed by primary contacts with PRRS-positive farms is unsurprising, the observation that secondary contacts also increase the likelihood of infection is novel. Risk of L1A occurrence on a farm also increased by 3.0% (p = .01) for every additional outgoing shipment, possibly due to biosecurity breaches during loading and transporting pigs from the farm. Finally, use of vaccines or field virus inoculation on sow farms one year prior reduced the risk of L1A occurrence in downstream farms by 36% (p = .04), suggesting that control measures that reduce viral circulation and enhance immunological protection in sow farms have a carry-over effect on L1A occurrence in downstream farms. Therefore, coordinated disease management interventions between farms connected via animal movements may be more effective than individual farm-based interventions.

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.

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.

Spatial relative risk and factors associated with porcine reproductive and respiratory syndrome outbreaks in United States breeding herds

Details of incident cases of porcine reproductive and respiratory syndrome (PRRS) in United States breeding herds were obtained from the Morrison's Swine Health Monitoring Project. Herds were classified as cases if they reported an outbreak in a given season of the year and non-cases if they reported it in a season other than the case season or if they did not report a PRRS outbreak in any season. The geographic distribution of cases and non-cases was compared in each season of the year. The density of farms that had a PRRS outbreak during summer was higher in Southern Minnesota and Northwest-central Iowa compared to the density of the underlying population of non-case farms. This does not mean that PRRS outbreaks are more frequent during summer in absolute terms, but that there was a geographical clustering of herds breaking during summer in this area. Similar findings were observed in autumn. In addition, the density of farms reporting spring outbreaks was higher in the Southeast of the United States compared to that of the underlying population of non-case farms. A similar geographical clustering of PRRS outbreaks was observed during winter in the Southeast of the United States. Multivariable analyses, adjusting for the effect of known confounders, showed that the incidence rate of PRRS was significantly lower during winter and autumn during the porcine epidemic diarrhea (PED) epidemic years (2013-2014), compared to PRRS incidence rates observed during the winter and autumn of PED pre-epidemic years (2009-2012). After 2014, an increase in the incidence rate of PRRS was observed during winter and spring but not during autumn or summer. Pig dense areas were associated with a higher incidence rate throughout the year. However, this association tended to be stronger during the summer. Additionally, herds with ≥2500 sows had an increased incidence rate during all seasons except spring compared to those with <2500 sows. PRRS incidence was lower in year-round air-filtered herds compared to non-filtered herds throughout the year. We showed that not only the spatial risk of PRRS varies regionally according to the season of the year, but also that the effect of swine density, herd size and air filtering on PRRS incidence may also vary according to the season of the year. Further studies should investigate regional and seasonal drivers of disease. Breeding herds should maintain high biosecurity standards throughout the year.

Poultry Professionals' Perception of Participation in Voluntary Disease Mapping and Monitoring Programs in the United States: A Cluster Analysis

The development and implementation of disease mapping and monitoring programs can be useful tools for rapid communication and control of endemic and epidemic infectious diseases affecting the food animal industry. Commercial livestock producers have traditionally been reluctant to share information related to animal health, challenging the large-scale implementation of such monitoring and mapping programs. The main objective of this study was to assess the perception of poultry professionals toward disease mapping and monitoring programs and to identify groups of poultry professionals with similar perceptions and attitudes toward these projects. We conducted a survey to identify the perceived risks and benefits to be able to properly address them and encourage industry participation in the future. An anonymous online survey was developed and distributed to poultry professionals through industry and professional associations. The participant's demographic information and perceptions of risk and benefits from participation on voluntary poultry disease mapping and monitoring programs were collected. Multiple correspondence analysis and hierarchical clustering on principal components were performed to identify groups of professionals with similar characteristics. A total of 63 participants from 21 states filled out the survey. The cluster analysis yielded two distinct groups of respondents, each including approximately 50% of respondents. Cluster 1 subjects could be characterized as optimistic, perceiving major benefits of sharing farm-level poultry disease information. However, they also had major concerns, mostly related to potential accidental data release and providing competitive advantages to rival companies. Cluster 2 subjects were characterized as perceiving a lesser degree of benefits from sharing farm-level poultry disease information. This second cluster mostly included production and service technicians. The roles and perceptions of risk and benefits of the participants contributed significantly to cluster assignment, while the represented commodity and geographic location in the United States did not. Successful development of voluntary poultry disease mapping and monitoring programs in the future will require that different sectors of poultry professionals be approached in different manners in order to highlight the benefits of the programs and to achieve maximum participation.

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year's weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015-2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14-25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14-25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

Contrasting animal movement and spatial connectivity networks in shaping transmission pathways of a genetically diverse virus

Analyses of livestock movement networks has become key to understanding an industry's vulnerability to infectious disease spread and for identifying farms that play disproportionate roles in pathogen dissemination. In addition to animal movements, many pathogens can spread between farms via mechanisms mediated by spatial proximity. Heterogeneities in contact patterns based on spatial proximity are less commonly considered in network studies, and studies that jointly consider spatial connectivity and animal movement are rare. The objective of this study was to determine the extent to which movement versus spatial proximity networks determine the distribution of an economically important endemic virus, porcine reproductive and respiratory syndrome virus (PRRSV), within a swine-dense region of the U.S. PRRSV can be classified into numerous phylogenetic lineages. Such data can be used to better resolve between-farm infection chains and elucidate types of contact most associated with transmission. Here, we construct movement and spatial proximity networks; farms within the networks were classified as cases if a given PRRSV lineage had been recovered at least once in a year for each of three years analyzed. We evaluated six lineages and sub-lineages across three years, and evaluated the epidemiological relevance of each network by applying network k-tests to statistically evaluate whether the pattern of case occurrence within the network was consistent with transmission via network linkages. Our results indicated that animal movements, not local area spread, play a dominant role in shaping transmission pathways, though there were differences amongst lineages. The median number of case farms inter-linked via animal movements was approximately 4.1x higher than random expectations (range: 1.7-13.7; p < 0.05, network k-test), whereas this measure was only 2.7x higher than random expectations for farms linked via spatial proximity (range: 1.3-5.4; p < 0.05, network k-test). For spatial proximity networks, contact based on proximities of <5 km appeared to have greater epidemiological relevance than longer distances, likely related to diminishing probabilities of local area spread at greater distances. However, the greater overall levels of connectivity of the spatial network compared to the movement network highlights the vulnerability of pig populations to widespread transmission via this route. By combining genetic data with network analysis, this research advances our understanding of dynamics of between-farm spread of PRRSV, helps establish the relative importance of transmission via animal movements versus local area spread, and highlights the potential for targeted control strategies based upon heterogeneities in network connectivity.

Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak

Investments in biosecurity practices are made by producers to reduce the likelihood of introducing pathogens such as porcine reproductive and respiratory syndrome virus (PRRSv). The assessment of biosecurity practices in breeding herds is usually done through surveys. The objective of this study was to evaluate the use of machine-learning (ML) algorithms to identify key biosecurity practices and factors associated with breeding herds self-reporting (yes or no) a PRRS outbreak in the past 5 years. In addition, we explored the use of the positive predictive value (PPV) of these models as an indicator of risk for PRRSv introduction by comparing PPV and the frequency of PRRS outbreaks reported by the herds in the last 5 years. Data from a case control study that assessed biosecurity practices and factors using a survey in 84 breeding herds in U.S. from 14 production systems were used. Two methods were developed, method A identified 20 variables and accurately classified farms that had reported a PRRS outbreak in the previous 5 years 76% of the time. Method B identified six variables which 5 of these had already been selected by model A, although model B outperformed the former model with an accuracy of 80%. Selected variables were related to the frequency of risk events in the farm, swine density around the farm, farm characteristics, and operational connections to other farms. The PPVs for methods A and B were highly correlated to the frequency of PRRSv outbreaks reported by the farms in the last 5 years (Pearson r = 0.71 and 0.77, respectively). Our proposed methodology has the potential to facilitate producer's and veterinarian's decisions while enhancing biosecurity, benchmarking key biosecurity practices and factors, identifying sites at relatively higher risk of PRRSv introduction to better manage the risk of pathogen introduction.