Modelling control strategies against Classical Swine Fever: influence of traders and markets using static and temporal networks in Ecuador

Pig farming in Ecuador represents an important economic and cultural sector, challenged by classical swine fever (CSF). Recently, the National Veterinary Service (NVS), has dedicated its efforts to control the disease by implementing pig identification, mandatory vaccination against CSF and movement control. Our objective was to characterise pig premises according to risk criteria, to model the effect of movement restriction strategies and to consider the temporal evolution of the network. Social network analysis (SNA), SIRS (susceptible, infected, recovered, susceptible) network modelling and temporal analysis were used. The network contained 751,003 shipments and 6 million pigs from 2017 to 2019. Participating premises consisted of 144,118 backyard farms, 138 industrial farms, 21,337 traders and 51 markets. The 10 most influential markets, in the Andean highlands, received between 500 and 4,600 pigs each week. The 10 most influential traders made about 3 shipments with 17 pigs per week. Simulations without control strategy resulted in an average CSF prevalence of 14.4%; targeted movement restriction reduced the prevalence to 7.2%, while with random movement restriction it was 13%. Targeting the top 10 national traders and markets and one of the high-risk premises in every parish was one of the best strategies with the surveillance infrastructure available, highlighting its major influence and epidemiological importance in the network. When comparing the static network with its temporal counterpart, causal fidelity (c = 0.62) showed a 38% overestimation in the number of transmission paths, also traversing the network required 4.39 steps, lasting approximately 233 days. In conclusion, NVS surveillance strategies could be more efficient by targeting the most at-risk premises, and in particular, taking into account the temporal information would make the risk assessment much more precise. This information could contribute to implement risk-based surveillance reducing the time to eradicate CSF and other infectious animal diseases.