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

Weathering the storm: Extreme weather events and their association with PED and PRRS occurrence

Porcine epidemic diarrhea (PED) and Porcine reproductive and respiratory syndrome (PRRS) are viral diseases that continue to challenge the US swine industry. Despite many known risk factors, unusual circumstances associated with their occurrence continues to be poorly explained. We investigated if extreme weather events (flood, heavy rain, high wind and tornadoes, measured at a county-level) are associated with the occurrence of both diseases up to ten weeks after the occurrence of the weather event using a case control study and logistic regression modeling to control for covariates. We obtained weekly farm-level disease occurrence information from the Morrison Swine Health Monitoring Project (MSHMP) and county-level weather events occurrence information from the National Oceanic and Atmospheric Administration (NOAA) storm events database. Our findings indicate that farms in counties exposed to floods had between two to three times higher odds of experiencing a PED outbreak between four to eight weeks after the event than control farms. However, we did not observe significant associations between other weather events and PED or PRRS occurrences. Even though the absolute risk these events pose to swine herds may be small, we suggest that companies should develop biosecurity protocols that consider the impact of extreme weather events in order to mitigate the risks posed to their herds.

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.

A discrete-time survival model for porcine epidemic diarrhoea virus

Since the arrival of porcine epidemic diarrhea virus (PEDV) in the United States in 2013, elimination and control programmes have had partial success. The dynamics of its spread are hard to quantify, though previous work has shown that local transmission and the transfer of pigs within production systems are most associated with the spread of PEDV. Our work relies on the history of PEDV infections in a region of the southeastern United States. This infection data is complemented by farm-level features and extensive industry data on the movement of both pigs and vehicles. We implement a discrete-time survival model and evaluate different approaches to modelling the local-transmission and network effects. We find strong evidence in that the local-transmission and pig-movement effects are associated with the spread of PEDV, even while controlling for seasonality, farm-level features and the possible spread of disease by vehicles. Our fully Bayesian model permits full uncertainty quantification of these effects. Our farm-level out-of-sample predictions have a receiver-operating characteristic area under the curve (AUC) of 0.779 and a precision-recall AUC of 0.097. The quantification of these effects in a comprehensive model allows stakeholders to make more informed decisions about disease prevention efforts.

Characterizing the connection between swine production sites by personnel movements using a mobile application-based geofencing platform

Biosecurity is critical to productivity and profitability in swine production systems and can be achieved by incorporating external (bioexclusion) and internal (biocontainment) practices. Although increasing threats of foreign animal diseases have justified the need of rigorous external biosecurity plans, their effectiveness highly depend on the compliance of on-farm employees, farm-related personnel, and visitors. In this study, we evaluated the uses of a mobile application-based geofencing platform in two swine production systems for accurately identifying personnel movements between swine production sites and detecting potential biosecurity breaches by violating required downtime between site visits. The geofencing platform accurately recognized 95.2% (379/398) of personnel entries comparing to physical entry logs. Further, among 1861 entries over a period of one month, 19 strongly connected components and 12 potential biosecurity breaches were identified. Personnel with duty in communications and information systems committed 75% of biosecurity breaches. The results reported herein demonstrated the possible uses of geofencing platforms for investigating connections among swine production sites by personnel movements and identifying biosecurity breaches.