Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

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.

Identifying target areas for risk-based surveillance and control of transboundary animal diseases: a seasonal analysis of slaughter and live-trade cattle movements in Uganda

Animal movements are a major driver for the spread of Transboundary Animal Diseases (TADs). These movements link populations that would otherwise be isolated and hence create opportunities for susceptible and infected individuals to meet. We used social network analysis to describe the seasonal network structure of cattle movements in Uganda and unravel critical network features that identify districts or sub-regions for targeted risk-based surveillance and intervention. We constructed weighted, directed networks based on 2019 between-district cattle movements using official livestock mobility data; the purpose of the movement ('slaughter' vs. 'live trade') was used to subset the network and capture the risks more reliably. Our results show that cattle trade can result in local and long-distance disease spread in Uganda. Seasonal variability appears to impact the structure of the network, with high heterogeneity of node and edge activity identified throughout the seasons. These observations mean that the structure of the live trade network can be exploited to target influential district hubs within the cattle corridor and peripheral areas in the south and west, which would result in rapid network fragmentation, reducing the contact structure-related trade risks. Similar exploitable features were observed for the slaughter network, where cattle traffic serves mainly slaughter hubs close to urban centres along the cattle corridor. Critically, analyses that target the complex livestock supply value chain offer a unique framework for understanding and quantifying risks for TADs such as Foot-and-Mouth disease in a land-locked country like Uganda. These findings can be used to inform the development of risk-based surveillance strategies and decision making on resource allocation. For instance, vaccine deployment, biosecurity enforcement and capacity building for stakeholders at the local community and across animal health services with the potential to limit the socio-economic impact of outbreaks, or indeed reduce their frequency.

Transmission of antimicrobial resistance (AMR) during animal transport

The transmission of antimicrobial resistance (AMR) between food-producing animals (poultry, cattle and pigs) during short journeys (< 8 h) and long journeys (> 8 h) directed to other farms or to the slaughterhouse lairage (directly or with intermediate stops at assembly centres or control posts, mainly transported by road) was assessed. Among the identified risk factors contributing to the probability of transmission of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), the ones considered more important are the resistance status (presence of ARB/ARGs) of the animals pre-transport, increased faecal shedding, hygiene of the areas and vehicles, exposure to other animals carrying and/or shedding ARB/ARGs (especially between animals of different AMR loads and/or ARB/ARG types), exposure to contaminated lairage areas and duration of transport. There are nevertheless no data whereby differences between journeys shorter or longer than 8 h can be assessed. Strategies that would reduce the probability of AMR transmission, for all animal categories include minimising the duration of transport, proper cleaning and disinfection, appropriate transport planning, organising the transport in relation to AMR criteria (transport logistics), improving animal health and welfare and/or biosecurity immediately prior to and during transport, ensuring the thermal comfort of the animals and animal segregation. Most of the aforementioned measures have similar validity if applied at lairage, assembly centres and control posts. Data gaps relating to the risk factors and the effectiveness of mitigation measures have been identified, with consequent research needs in both the short and longer term listed. Quantification of the impact of animal transportation compared to the contribution of other stages of the food-production chain, and the interplay of duration with all risk factors on the transmission of ARB/ARGs during transport and journey breaks, were identified as urgent research needs.

Multilayer network analysis of FMD transmission and containment among beef cattle farms

As a highly contagious livestock viral disease, foot-and-mouth disease poses a great threat to the beef-cattle industry. Direct animal movement is always considered as a major route for between-farm transmission of FMD virus. Sharing contaminated equipment and vehicles have also attracted increasing interests as an indirect but considerable route for FMD virus transmission. With the rapid development of communication technologies, information-sharing techniques have been used to control epidemics. In this paper, we built farm-level time-series three-layer networks to simulate the between-farm FMD virus transmission in southwest Kansas by cattle movements (direct-contact layer) and truck visits (indirect-contact layer) and evaluate the impact of information-sharing techniques (information-sharing layer) on mitigating the epidemic. Here, the information-sharing network is defined as the structure that enables the quarantine of farms that are connected with infected farms. When a farm is infected, its infection status is shared with the neighboring farms in the information-sharing network, which in turn become quarantined. The results show that truck visits can enlarge the epidemic size and prolong the epidemic duration of the FMD outbreak by cattle movements, and that the information-sharing technique is able to mitigate the epidemic. The mitigation effect of the information-sharing network varies with the information-sharing network topology and different participation levels. In general, an increased participation leads to a decreased epidemic size and an increased quarantine size. We compared the mitigation performance of three different information-sharing networks (random network, contact-based network, and distance-based network) and found the outbreak on the network with contact-based information-sharing layer has the smallest epidemic size under almost any participation level and smallest quarantine size with high participation. Furthermore, we explored the potential economic loss from the infection and the quarantine. By varying the ratio of the average loss of quarantine to the loss of infection, we found high participation results in reduced economic losses under the realistic assumption that culling costs are much greater than quarantine costs.

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.

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

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

Direct and indirect pathways for the spread of African swine fever and other porcine infectious diseases: An application of the mental models approach

In this study, we investigated the occurrence of direct and indirect infectious disease transmission pathways among pig farms in Switzerland, as well as their specific relevance for the spread of African swine fever, porcine reproductive and respiratory syndrome (PRRS), and enzootic pneumonia. Data were collected using an adapted mental models approach, involving initial interviews with experts in the field of pig health and logistics, semi-structured interviews with pig farmers, and a final expert workshop, during which all identified pathways were graded by their predicted frequency of occurrence, their likelihood of spread of the three diseases of interest, and their overall relevance considering both parameters. As many as 24 disease pathways were identified in four areas: pig trade, farmer encounters, external collaborators, and environmental or other pathways. Two thirds of the pathways were expected to occur with moderate-to-high frequency. While both direct and indirect pig trade transmission routes were highly relevant for the spread of the three pathogens, pathways from the remaining areas were especially important for PRRS due to higher spread potential via aerosols and fomites. In addition, we identified factors modifying the relevance of disease pathways, such as farm production type and affiliation with trader companies. During the interviews, we found varying levels of risk perception among farmers concerning some of the pathways, which affected adherence to biosecurity measures and were often linked to the degree of trust that farmers had towards their colleagues and external collaborators. Our findings highlight the importance of integrating indirect disease pathways into existing surveillance and control strategies and in disease modelling efforts. We also propose that biosecurity training aimed at professionals and risk communication campaigns targeting farmers should be considered to mitigate the risk of disease spread through the identified pathways.

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.

A descriptive exploration of animal movements within the United States cull sow marketing network

Objective: Collect and describe data regarding sow movements within the US cull sow marketing network, and what implications those movements may have on disease introduction and dissemination within the United States. Materials and methods: Premise identification tags (PITs) were collected with the help of the US Department of Agriculture’s Animal and Plant Health Inspection Service-Veterinary Services Brucellosis Laboratory. Collection occurred for a total of 6 months. From each PIT the management/sow identification (ID), premises ID, state, facility, and slaughter date were recorded. Participating production systems identified the cull dates of individual sows from their system. Results: A total of 17,493 PITs were collected. This study collected PITs from 32 states and 1211 unique premises IDs. Facilities received sows from a median (IQR) of 9.5 (12.5) states and 71 (79.25) unique premises each week. Sows traveled a median (IQR) distance of 472.7 (453.6) km with a maximum of 2812.8 km. A single premises delivered sows to 1, 2, or 3 or more slaughter facilities 59.7%, 33.4%, and 6.9%, respectively. Removal date from the farm of origin was available for 2886 (16.5%) individual sows. Of these, 66.1% were in the market channel for ≤ 3 days, 25% for 4 to 5 days, and 8.9% for > 5 days. Implications: These results suggest that the cull sow marketing channel provides an independent, but interconnected swine population that can maintain, expand, and transmit pathogens to the US swine herd. Control and elimination plans for novel, transboundary, and foreign animal diseases should include this population.

Unravelling direct and indirect contact patterns between duck farms in France and their association with the 2016-2017 epidemic of Highly Pathogenic Avian Influenza (H5N8)

Live animal movements generate direct contacts (via the exchange of live animals) and indirect contacts (via the transit of transport vehicles) between farms, which can contribute to the spread of pathogens. However, most analyses focus solely on direct contacts and can therefore underestimate the contribution of live animal movements in the spread of infectious diseases. Here, we used French live duck movement data (2016-2018) from one of the largest transport companies to compare direct and indirect contact patterns between duck farms and evaluate how these patterns were associated with the French 2016-2017 epidemic of highly pathogenic avian influenza H5N8. A total number of 614 farms were included in the study, and two directed networks were generated: the animal introduction network (exchange of live ducks) and the transit network (transit of transport vehicles). Following descriptive analyses, these two networks were scrutinized in relation to farm infection status during the epidemic. Results showed that farms were substantially more connected in the transit network than in the animal introduction network and that the transit of transport vehicles generated more opportunities for transmission than the exchange of live animals. We also showed that animal introduction and transit networks' statistics decreased substantially during the epidemic (January-March 2017) compared to non-epidemic periods (January-March 2016 and January-March 2018). We estimated a probability of 33.3 % that a farm exposed to the infection through either of the two live duck movement networks (i.e. that was in direct or indirect contact with a farm that was reported as infected in the following seven days) becomes infected within seven days after the contact. However, we also demonstrated that the level of exposure of farms by these two contact patterns was low, leading only to a handful of transmission events through these routes. As a consequence, we showed that live animal movement patterns are efficient transmission routes for HPAI but have been efficiently reduced to limit the spread during the French 2020-2021 epidemic. These results underpin the relevance of studying indirect contacts resulting from the movement of animals to understand their transmission potential and the importance of accounting for both routes when designing disease control strategies.

Analysis of the movement of live broilers in Guangxi, China and implications for avian influenza control

Most Chinese provinces have a daily-updated database of live animal movements; however, the data are not efficiently utilized to support interventions to control H7N9 and other avian influenzas. Based on official records, this study assessed the spatio-temporal patterns of live broilers moved out of and within Guangxi in 2017. The yearly and monthly networks were analyzed for inter- and intra-provincial movements, respectively. Approximately 200,000 movements occurred in 2017, involving the transport of 200 million live broilers from Guangxi. Although Guangxi exported to 24 out of 32 provinces of China, 95% of inter-provincial movements occurred with three bordering provinces. Within Guangxi, counties were highly connected through the live broiler movements, creating conditions for rapid virus spreading throughout the province. Interestingly, a peak in movements during the Chinese Lunar New Year celebrations, late January in 2017, was not observed in this study, likely due to H7N9-related control measures constraining live bird trading. Both intra- and inter-provincial movements in March 2017 were significantly higher than in other months of that year, suggesting that dramatic price changes may influence the movement's network and reshape the risk pathways. However, despite these variations, the same small proportion of counties (less than 20%) exporting/importing more than 90% of inter- and intra-provincial movements remains the same throughout the year. Interventions, particularly surveillance and improving biosecurity, targeted to those counties are thus likely to be more effective for avian influenza risk mitigation than implemented indiscriminately. Additionally, simulations further demonstrated that targeting counties according to their degree or betweenness in the movement network would be the most efficient way to limit disease transmission via broiler movements. The study findings provide evidence to support the design of risk-based control interventions for H7N9 and all other avian influenza viruses in broiler value chains in Guangxi.

A survey of biosecurity and health management practices on Irish dairy farms engaged in contract-rearing

A survey was conducted to investigate potential differences in biosecurity and health management practices on Irish dairy farms that sent their heifers for contract-rearing (source dairy farms, SDF; n = 62) and those rearing their own heifers (control farms, CF; n = 50). Participating farmers were surveyed by postal questionnaire between September and November 2018. The overall response rate was 93%. Results show that structurally, SDF were larger, less fragmented, and more specialized than CF. Outsourcing of labor-intensive activities to external contractors was more common among SDF than CF, exposing them to potentially increased biosecurity risks associated with animal movements, use of shared equipment, and increased frequency of farm visitors. The majority of SDF sent heifers to a single-origin rearing facility (70%), with heifers most commonly arriving at the rearing unit between 2 and 4 mo (53%) and returning to the dairy farm between 18 and 21 mo of age (56%). Despite the increased biosecurity risk associated with contract-rearing, implementation of disease prevention measures was not superior on SDF compared with CF. For both farm types, there was scope for improvement to visitor biosecurity protocols, quarantine procedures, colostrum feeding practices, and hygiene of calving areas. This research provides an overview of the demographics and farm management practices implemented by dairy farmers engaged in contract-rearing of replacement heifers, and will serve to inform farmers, veterinary advisors, and policy makers.

Management and biosecurity practices by small to medium egg producers in Scotland

1. Information about procedures and biosecurity practices used by small and medium egg producers (SMEPs) is scarce. Anecdotal evidence suggests that biosecurity in such enterprises may be poor, as personnel and equipment move freely between sites and this may be compounded by personnel working on commercial units who keep their own poultry.2. To fill this knowledge gap, a questionnaire was designed and implemented targeting SMEPs in Scotland. Small enterprises were defined as egg producers that have ≥50 laying hens but <350 laying hens; while medium enterprises were defined as egg producers that have ≥350 laying hens but ≤32 000 laying hens. The questionnaire consisted of a total of 56 questions divided into multiple sections, covering the characteristics of the primary keeper, location of the enterprise and size of the flocks, husbandry, marketing of products and health/biosecurity.3. The questionnaire was posted to 375 holdings at the beginning of March 2017 and the survey remained open until the end of May 2017. In total 90 questionnaires were received by the cut-off date of which 76 questionnaires were from SMEPs. Forty were small enterprises and 36 were medium enterprises. For three questionnaires, it was not possible to identify the enterprise type.4. Differences were observed between SMEPs in terms of reported biosecurity and management practices, with medium enterprises reporting the adoption of more biosecurity measures than small enterprises. Furthermore, SMEPs behave differently from backyard poultry keepers and large commercial companies in terms of disease risk.5. In conclusion, it is important to ensure that SMEPs are considered in contingency plans and disease control programmes and that engagement with them is promoted so that the uptake of relevant information, such as awareness of disease control programmes, is optimised.

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.

Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Tracking animal movements over time may fundamentally determine the success of disease control interventions. In commercial pig production growth stages determine animal transportation schedule, thus it generates time-varying contact networks showed to influence the dynamics of disease spread. In this study, we reconstructed pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks view did not capture time-respecting movement pathways. For this reason, we propose a time-dependent network approach. A susceptible-infected model was used to spread an epidemic over the pig network globally through the temporal between-farm networks, and locally by a stochastic model to account for within-farm dynamics. We propagated disease to calculate the cumulative contacts as a proxy of epidemic sizes and evaluate the impact of network-based disease control strategies in the absence of other intervention alternatives. The results show that targeting 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our modelling results indicated that independently from where initial infections were seeded (i.e. independent, commercial farms), the epidemic sizes and the number of farms needed to be targeted to effectively control disease spread were quite similar; indeed, this finding can be explained by the presence of contact among all pig operation types The proposed strategy limited the transmission the total number of secondarily infected farms to 29, over two simulated years. The identified 1,000 farms would benefit from enhanced biosecurity plans and improved targeted surveillance. Overall, the modelling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data are available.