Estimation of swine movement network at farm level in the US from the Census of Agriculture data

Ecological drivers of evolution of swine influenza in the United States: a review

Influenza A viruses (IAVs) pose a major public health threat due to their wide host range and pandemic potential. Pigs have been proposed as "mixing vessels" for avian, swine, and human IAVs, significantly contributing to influenza ecology. In the United States, IAVs are enzootic in commercial swine farming operations, with numerous genetic and antigenic IAV variants having emerged in the past two decades. However, the dynamics of intensive swine farming systems and their interactions with ecological factors influencing IAV evolution have not been systematically analysed. This review examines the evolution of swine IAVs in commercial farms, highlighting the role of multilevel ecological factors. A total of 61 articles published after 2000 were reviewed, with most studies conducted after 2009 in Midwestern US, followed by Southeast and South-central US. The findings reveal that ecological factors at multiple spatial scales, such as regional transportation networks, interconnectedness of swine operations, farm environments, and presence of high-density, low-genetic diversity herds, can facilitate virus transmission and enhance virus evolution. Additionally, interactions at various interfaces, such as between commercial swine and feral swine, humans, or wild birds contribute to the increase in genetic diversity of swine IAVs. The review underscores the need for comprehensive studies and improved data collection to better understand the ecological dynamics influencing swine IAV evolution. This understanding is crucial for mitigating disease burden in swine production and reducing the risk of zoonotic influenza outbreaks.

Analyzing the intrastate and interstate swine movement network in the United States

Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.

Are all underimmunized measles clusters equally critical?

This research develops a novel system science approach to examine the potential risk of outbreaks caused by geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage for measles at the state level, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. This research examines why some underimmunized geographical clusters are more critical in causing outbreaks and how their criticality changes with a possible drop in overall vaccination coverage. Results show that different clusters can cause vastly different outbreaks in a region, depending on their size, location, immunization rate and network characteristics. Among the three underimmunized clusters, we find one to be critical and the other two to be benign in terms of an outbreak risk. However, when the vaccine coverage among children drops by just 5% (or 0.8% overall in the population), one of the benign clusters becomes highly critical. This work also examines the demographic and network properties of these clusters to identify factors that are responsible for affecting the criticality of the clusters. Although this work focuses on measles, the methodology is generic and can be applied to study other infectious diseases.

Assessing and improving on-farm biosecurity knowledge and practices among swine producers and veterinarians through online surveys and an educational website in Illinois, United States

There is a growing risk to the health and productivity of the Illinois and United States swine population from foreign and endemic infectious diseases. Effective on-farm biosecurity practices play a pivotal role in preventing these high-consequence pathogens from affecting swine farms. Veterinarians are essential in providing disease prevention advice to swine producers that can help them implement effective biosecurity practices on their farms. Our descriptive study objectives were to assess Illinois swine producers' and veterinarians' biosecurity perception, knowledge, and practices to identify knowledge gaps and address these by developing an online educational website. We developed two independent online questionnaires using Qualtrics^XM software. Swine producer members of the Illinois Pork Producers Association and veterinarians registered with the Illinois State Veterinary Medical Association were contacted via e-mail through their associations and asked to complete an online survey. In total, 13 swine producers across 9 Illinois counties operating 82 farms (8 managed single farms and 5 managed multiple farms) responded to the swine producer survey. Despite some biosecurity awareness among swine producers, the need for a biosecurity-related outreach program was evident. Among the 7 swine veterinarian responders, 5 predominantly treated swine (oversaw an average of 21.6 farms), and 2 were mixed animal practitioners. The swine veterinarian survey showed a disconnect between their biosecurity perception and practices. We developed a biosecurity educational website and used Google Analytics to collect website traffic and user data. The 4 months of data showed good coverage that included the highest proportion of users from the Midwest and North Carolina, the largest swine-production regions in the US, and China and Canada, the leading producers of swine worldwide. The most accessed webpage was the resources page, and the swine diseases page had the highest engagement time. Our study highlights the effectiveness of combining online surveys with an educational website to assess and improve the biosecurity knowledge of swine producers and veterinarians that can be applied to assess and improve the biosecurity knowledge and practices of other livestock farmers.

Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

The Application of an Augmented Gravity Model to Measure the Effects of a Regionalization of Potential Risk Distribution of the US Cull Sow Market

The continuous threat of foreign animal disease (FAD) is real and present for the U.S. swine industry. Because of this, the industry has developed plans to ensure business continuity during a FAD outbreak. A core aspect of these plans is regional standstill orders of swine movements to prevent disease spread following a FAD introduction. Unfortunately, there is a dearth of information about the impact of such practices on animal movements throughout the remaining swine marketing channel. This study utilizes a simplified gravity model, to understand the effects of standstill orders on individual states. The effect of each closure on the established trade patterns is determined by monitoring changes in a PPML regression coefficients of the model. Model validation compared the predicted impact of the closure of a terminal processing facility against a real-life closure dataset collected during the SARS-CoV-2 pandemic. The analysis determined that both the population size and location of the closure affected the observed trade patterns. These findings suggest that using a regional stop movement order may complicate disease introduction preparation as each policy comes with its own potential outcome, shifting the geospatial distribution of area risk posed by these cull populations.

Temporal stability of swine movement networks in the U.S

As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Social network analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. However, inferences from SNA and the effectiveness of targeted strategies may be influenced by temporal changes in network structure. Since farm movements represent a temporally dynamic network, it is also unclear how many months of data are required to gain an accurate picture of an individual farm's connectivity pattern and the overall network structure. The extent to which shipments between two specific farms are repeated (i.e., "loyalty" of farm contacts) can influence the rate at which the structure of a network changes over time, which may influence disease dynamics. In this study, we aimed to describe temporal stability and loyalty patterns of pig movement networks in the U.S. swine industry. We analyzed a total of 282,807 animal movements among 2724 farms belonging to two production systems between 2014 and 2017. Loyalty trends were largely driven by contacts between sow farms and nurseries and between nurseries and finisher farms; mean loyalty (percent of contacts that were repeated at least once within a 52-week interval) of farm contacts was 51-60 % for farm contacts involving weaned pigs, and 12-22% for contacts involving feeder pigs. A cyclic pattern was observed for both weaned and feeder pig movements, with episodes of increased loyalty observed at intervals of 8 and 17-20 weeks, respectively. Network stability was achieved when six months of data were aggregated, and only small shifts in node-level and global network metrics were observed when adding more data. This stability is relevant for designing targeted surveillance programs for disease management, given that movements summarized over too short a period may lead to stochastic swings in network metrics. A temporal resolution of six months would be reliable for the identification of potential super-spreaders in a network for targeted intervention and disease control. The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness.

Porcine reproductive and respiratory syndrome virus dissemination across pig production systems in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widespread in the North American pig population. Despite improvements in virus characterization, it is unclear whether PRRSV infections are a product of viral circulation within production systems (local) or across production systems (external). Here, we examined the local and external dissemination dynamics of PRRSV and the processes facilitating its spread in three production systems. Overall, PRRSV genetic diversity has declined since 2018, while phylodynamic results support frequent external transmission. We found that PRRSV dissemination predominantly occurred mostly through transmission between farms of different production companies for several months, especially from November until May, a timeframe already established as PRRSV season. Although local PRRSV dissemination occurred mainly through regular pig flow (from sow to nursery and then to finisher farms), an important flux of PRRSV dissemination also occurred in the opposite direction, from finisher to sow and nursery farms, highlighting the importance of downstream farms as sources of the virus. Our results also showed that farms with pig densities of 500 to 1,000 pig/km² and farms located at a range within 0.5 km and 0.7 km from major roads were more likely to be infected by PRRSV, whereas farms at an elevation of 41 to 61 meters and surrounded by denser vegetation were less likely to be infected, indicating their role as dissemination barriers. In conclusion, our results demonstrate that external dissemination was intense, and reinforce the importance of farm proximity on PRRSV spread. Thus, consideration of farm location, geographic characteristics and animal densities across production systems may help to forecast PRRSV collateral dissemination.

Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus

Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.