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

Abstract

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 will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. 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 the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target 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. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control .

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

“…When evaluating the cluster coefficients, bovine and swine movements remain steady while small ruminants have large fluctuations. The swine network centralization was much higher than bovine or small ruminants, which could be related to how the industry is organized with the formation of groups of producers with commercial contracts with an integrator [ 21 , 37 ]. For the GSCC and GWCC, the bovine monthly-snapshot network followed the same temporal patterns with the nodes indicating an important dependency in the number of active farms, while small ruminants showed a seasonality with a higher percentage of GSCC and GWCC at the end of each year.…”

“…This is likely because of the pyramidal structure commercial pig production utilizes to improve performance and productivity. Because of such continuous flow, nurseries and wean to finishers tend to receive and send a great number of pigs, thus have a higher degree in comparison with other production types [ 21 , 37 , 39 ]. In contrast, the network connectivity in bovine farms was low despite having more than 20 000 edges and high node activity in a monthly view.…”

“…Additionally, the degree distribution results could help highlight super-spreader farms in all host populations through the identification of farms with several commercial partners that make a major contribution to disease spread [ 41 ]. For example, the swine network was highlighted as the network with the largest degree, which has been described previously in the same region [ 2 , 21 ]. Finally, the analysis of the small ruminant networks showed a very disconnected network with a marked seasonality at the end of each year but with fewer monthly edges and nodes than swine and cattle farms, despite having more registered farms than the swine production in the full network.…”

“…The use of such approaches is very useful to describe the topology and temporal variation in the networks, however, it can lead to overestimation of the connectivity of the animal trade. Therefore, approaches that account for the temporal order of movements such as temporal contact chains analysis and disease spread models have been successful in capturing epidemic trajectories [ 19 – 21 ].…”

Demand-Driven Spreading Patterns of African Swine Fever in China

“…Several studies have examined the ASF epidemics from various perspectives, including transboundary location predictions (13), molecular characterization (14), major capsid protein determination (15), pig movement simulation (16,17), analysis of carriers of ASF viruses (18). However, to date, little is known about the underlying transmission mechanism in China--the key to designing effective containment and mitigation strategies.…”