Quantifying the dynamics of pig movements improve targeted disease surveillance and control plans
Tracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that may influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on static network. We reconstructed the static and temporal pig networks of one
... tworks of one Brazilian state from 2017 until 2018, comprising 351,519 movements and 48 million transported pigs. Here the static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over our pig network, globally through the temporal movement of infected animals between farms and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. Results show that targeting the first 1,000 farms ranked by degree would be enough to control disease spread. Our finding also suggested that assuming a worst-case scenario in which every movement could transmit disease, pursuing farms by degree would limit the transmission to 29 farms, much lower if compared with random surveillance that resulted in epidemics of 2,593 farms. Overall, the proposed modeling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data is available. Finally, the top 1,000 farms ranked by degree could benefit from enhancing biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control intervention.