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Spatio-temporal network analysis of pig trade to inform the design of risk-based disease surveillance
  • +3
  • Nicolás Céspedes Cárdenas,
  • Kimberly Vanderwaal,
  • Flávio Pereira Veloso,
  • Jason Onell Ardila Galvis,
  • Marcos Amaku,
  • José H H Grisi Filho -
Nicolás Céspedes Cárdenas
Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo
Kimberly Vanderwaal
Department of Veterinary Population Medicine, University of Minnesota
Flávio Pereira Veloso
Companhia Integrada de Desenvolvimento Agrícola de Santa Catarina (CIDASC
Jason Onell Ardila Galvis
Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo
Marcos Amaku
Department of Pathology, School of Medicine, University of São Paulo, Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo
José H H Grisi Filho -
Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo

Abstract

Network analysis is a powerful tool to describe, estimate, and predict the role of pig trade in the spread of pathogens and generate essential patterns that can be used to understand, prevent, and mitigate possible outbreaks. This study aimed to describe the network of between-farm pig movements and identify heterogeneities in the connectivity of premises in the state of Santa Catarina, Brazil, using social network analysis (SNA). We used static and temporal network approaches to describe pig trade in the state by quantifying network attributes using SNA parameters, such as causal fidelity, loyalty, the proportion of node-loyalty, resilience of outgoing contact chains, and communities. Two indexes were implemented, the first one is a normalized index based on SNA-farm level measures and other index-based SNA-farm level measures considering the swine herd population size from all premises, both indexes were summarized by municipality to target and rank surveillance activities. Within Santa Catarina, the southwest region played a key role in that 80% of trade was concentrated in this region, and thus acted as a hub in the network. In addition, nine communities were found. The results also showed that premises were highly connected in the static network, with the network exhibiting low levels of fragmentation and loyalty. Also, just 11% of the paths in the static network existed in the temporal network which accounted for the order in which edges occurred. Therefore, the use of time-respecting-paths was essential to not overestimate potential transmission pathways and outbreak sizes. Compared to static networks, the application of temporal network approaches was more suitable to capture the dynamics of pig trade and should be used to inform the design of risk-based disease surveillance.