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.


Similar to the movement of live animals known to dominate between-farm pathogen dissemination (Galvis et al., 2022a, Green et al., 2006), transportation of vehicle movements is of great concern as an indirect dissemination route (Galvis et al., 2022a, Galvis et al., 2022b, Smith et al., 2013, Thakur et al., 2016). Recent studies investigated the role of vehicles as the pathway of porcine epidemic diarrhea virus (PEDV) outbreaks (Boniotti et al., 2018, Garrido-Mantilla et al., 2022, Lowe et al., 2014); African swine fever (ASF) (Adedeji et al., 2022, Cheng and Ward, 2022, Li et al., 2020, Nigsch et al., 2013; Yoo et al., 2021b); and avian influenza virus (Huneau-Salaün et al., 2020; Yoo et al., 2021a). In addition, (Boniotti et al., 2018, Dee et al., 2004, Gebhardt et al., 2022, Greiner, 2016, Mannion et al., 2008) demonstrated that infectious pathogens are found on vehicle surfaces, while others estimated the contribution of vehicles in PEDV and porcine reproductive and respiratory syndrome virus (PRRSV) (Dee et al., 2002, Galvis et al., 2022a, Thakur et al., 2017, VanderWaal et al., 2018). That said, the underlying mechanisms of vehicles as disease dissemination routes remain to be examined in large-scale studies (Galvis et al., 2022a, Neumann et al., 2021). Thus, without access to actual vehicle movement data along with pathogen stability in vehicle environments at field conditions; and the effects of vehicle cleaning and disinfection in reducing vehicle contamination, are still challenges highlighted in better understanding the indirect contribution of vehicles in disease dissemination (Bernini et al., 2019, Galvis et al., 2022a, Gao et al., 2023b, Neumann et al., 2021).

The complexity and the dynamics of animal and vehicle between farm movement networks present a formidable challenge for decision-makers and producers who need to implement disease control measures, often not knowing when a new load of animals will arrive and if the farm or origin has been recently infected or not, or if a feed truck is delivering feed after being at an infected farm (Galvis et al., 2022a, Galvis et al., 2022b, Lee et al., 2019; Yoo et al., 2021b). Some studies in North America and Europe utilized actual animal and vehicle movement data to reconstruct the between-farm transmission dynamics of infectious diseases (Andraud et al., 2022, Bernini et al., 2019, Galvis et al., 2022a, Galvis et al., 2022b) while considering pathogen stability at the environment and the effects of cleaning and disinfection. Even though previous studies enhanced our understanding of indirect swine disease dissemination through vehicle movements, authors identified uncertainties about the association between i) the efficacy of vehicle cleaning and disinfection and ii) factors affecting pathogen stability over their contribution in disseminating disease from farm-to-farm (Andraud et al., 2022, Bernini et al., 2019, Galvis et al., 2022a, Galvis et al., 2022b). Vehicle cleaning and disinfection may not effectively eliminate infectious pathogens, especially in difficult access areas, such as behind windows or gates (Boniotti et al., 2018, Li et al., 2020, Mannion et al., 2008). Therefore, it is essential to consider that several factors modulate the impact of vehicle cleaning and disinfection effectiveness, including using different disinfectants associated or not with heat, which is directly associated with the time needed for a complete truck wash (De Lorenzi et al., 2020, Porphyre et al., 2020). Similarly, the better pathogen that survives in the environment is more likely to be disseminated among farms by vehicles (Jacobs et al., 2010, Mazur-Panasiuk and Woźniakowski, 2020). Temperature, pH, humidity, and ultraviolet (UV) radiation are associated with pathogen stability (Carlson et al., 2020, Cutler et al., 2012, Espinosa et al., 2020, Hijnen et al., 2006). For example, the high temperature reduces ASF, PRRSV, PEDV, and foot-and-mouth disease stability outside the host over time (Bøtner and Belsham, 2012, Jacobs et al., 2010, Kim et al., 2018, Mazur-Panasiuk and Woźniakowski, 2020).

The scarcity of vehicle movement data and the lack of network methods capable of combining contact networks, variables associated with pathogens’ stability, and uncertainty of cleaning and disinfection limit our ability to understand the contribution of vehicles in disease transmission. Here, we collected GPS data of 567 vehicles transporting feed, pigs, and people to 6363 farms. We developed a novel vehicle contact network method that considers environmental variables and vehicle cleaning and disinfection effectiveness. Thus, our goal was to reconstruct a vehicle contact network of swine companies in the U.S. while using ASFV pathogen stability profile.

Section snippets


In this study, we used information from two U.S. regions. Region one with 1974 commercial swine farms managed by six swine production companies (coded hereafter A, B, C, D, E, and F), and region two with 4389 commercial swine farms managed by 13 swine companies (coded here as G, H, I, J, K, L, M, N, O, P, Q, R, and S). Farm data includes a unique premise identification, animal capacity stratified by age, latitude, and longitude representing the farm’s centroid and associated management company.

Number of farm visits

Table 1 shows that increasing the buffer distance around farm units leads to more vehicle visits while lengthening the minimum duration for a visit to count from 5 minutes to 20 or 60 minutes decreases the number of visits. These findings suggest a trade-off between buffer distance and visit frequency. In region one, the total number of vehicle visits varied between a minimum of 47,847 and a maximum of 301,774 visits (Supplementary Material Table S4), while the median by vehicle varied between


In this study, we developed a novel transportation vehicle contact network methodology that explicitly considers environmental pathogen stability and vehicle cleaning effectiveness uncertainties. We demonstrated that when cleaning and disinfection were either not performed in between farm visits or were simulated to be not effective (d = 0%), the vehicle’s contact networks had 5583,703 edges in region one and 128,483 in region two. This means that 88% of 2519 farm units in region one and 9% of

Limitations and further remarks

We recognize the limitations of the novel methodology for the proposed vehicle movement network and the available vehicle movement data. It is worth noting that the absence of data from vehicles serving most, but not all, premises in both regions underestimated the outcomes concerning indirect contact between companies and networks metrics evaluated at the regional level. Likewise, we were unaware of third-party vehicle washing locations; this limitation likely impacted significantly the crew


In this study, we extended a previously developed methodology for vehicle contact networks, which is commonly employed in disease transmission models (Galvis et al., 2022a, Galvis et al., 2022b, Sykes et al., 2023). In this updated approach, we have considered the uncertainty related to the processes of vehicle cleaning and disinfection, as well as the decay of ASFV stability in the environment. Our study revealed that although efficient cleaning and disinfection measures affected the number of


This project was funded by the Swine Health Information Center under the grant agreement number 22-059.