Dynamics of livestock-associated methicillin resistant Staphylococcus aureus in pig movement networks: Insight from mathematical modeling and French data

Assessment of control measures against livestock-associated methicillin-resistant Staphylococcus aureus in a farrow-to-finish pig herd using infectious disease modelling

Pigs are considered to be the main reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA), which is a zoonotic opportunistic pathogen. As LA-MRSA is an occupational hazard, there is an incentive to control its spread in pig herds. Currently, knowledge about effective control measures which do not require culling the whole herd are limited, and the control strategies against LA-MRSA vary between countries. This study uses a stochastic compartment model to simulate possible control measures for LA-MRSA in a farrow-to-finish pig herd. The aims of the study were to (1) extend a previously published disease spread model with additional management and control measures; (2) use the extended model to study the effect of the individual LA-MRSA control measures on the within-herd LA-MRSA prevalence; (3) evaluate the effect of control measures when they are implemented in combinations. From the individual control measures tested in the study, thorough cleaning was found to be most effective in reducing the LA-MRSA prevalence in the herd. When the different control measures were combined, cleaning together with disease surveillance had the largest impact on reducing the LA-MRSA and a higher chance of causing disease elimination. The results of the study showed that achieving disease elimination once LA-MRSA had been introduced in the herd was challenging but was more likely when control measures were introduced early during the outbreak. This emphasises the importance of early detection of the pathogen and subsequent rapid implementation of LA-MRSA control measures.

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.

Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision-makers

African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur. This article is protected by copyright. All rights reserved.

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.

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.