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

TL;DR

This study models control strategies for Classical Swine Fever in Ecuador using static and temporal network analysis, highlighting the importance of targeted surveillance of high-risk premises, traders, and markets to effectively reduce disease spread.

Contribution

It introduces a comprehensive network-based approach to evaluate and optimize CSF control strategies, emphasizing the role of traders and markets in disease transmission.

Findings

Targeted strategies significantly reduce CSF prevalence.

Markets and traders are key nodes influencing disease spread.

Temporal analysis overestimates transmission paths by 38%.

Abstract

Classical swine fever (CSF) in Ecuador is prevalent since 1940, pig farming represents an important economic and cultural sector. Recently, the National Veterinary Service (NVS) has implemented individual identification of pigs, movement control and mandatory vaccination against CSF, looking for a future eradication. Our aim was to characterise the pig premises according to risk criteria, analyse the effect of random and targeted strategies to control CSF and consider the temporal development of the network. We used social network analysis (SNA), SIRS (susceptible, infected, recovered, susceptible) network modelling and temporal network analysis. The data set contained 751,003 shipments and 6 million pigs from 2017 to 2019. 165,593 premises were involved: 144,118 farms, 138 industrials, 21,337 traders, and 51 markets. On annual average, 124,976 premises (75%) received or sent one movement with 1.5 pigs, in contrast, 166 (0.01%) with 1,372 movements and 11,607 pigs. Simulations resulted in CSF mean prevalence of 29.93%; Targeted selection strategy reduced the prevalence to 3.3%, while 24% with random selection. Selection of high-risk premises in every province was the best strategy using available surveillance infrastructure. Notably, selecting 10 traders/markets reduced the CSF prevalence to 4%, evidencing their prime influence over the network. Temporal analysis showed an overestimation of 38% (causal fidelity) in the number of transmission paths; The steps to cross the network were 4.3 (average path length), but take approximately 233 days. In conclusion, surveillance strategies applied by the NVS could be more efficient to find cases, reduce the spread of diseases and enable the implementation of risk-based surveillance. To focus the efforts on target selection of high-risk premises, special attention should be given to markets/traders which proved similar disease spread potential.