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.
Introduction
Infectious diseases in livestock cause great economic losses, block international trade, compromise animal welfare, reduce productivity, and induce large costs through disease control and eradication (BAJARDI et al., 2012; MOSLONKA-LEFEBVRE et al., 2016). In Brazil, the swine industry consists of approximately two million sows and produced 3.76 million tons of meat in 2018, which has made Brazil the fourth-largest producer of pork in the world. Within Brazil, the state of Santa Catarina (SC) is the largest swine producer (EMBRAPA, 2019). The control of infectious diseases is critical to maintaining this leadership, and the movement of animals through trade plays an important role in spreading infectious diseases (SCHULZ et al., 2017).
In recent years, the Brazilian National Veterinary Services (BNVS) has intensified disease surveillance, control, and eradication efforts. Until 2019, SC is the only state in the country free of foot-and-mouth disease (FMD) without vaccination. Therefore, it is critical to understand patterns and risks associated with animal movements to maintain this disease-free status. This is especially relevant for the national efforts to transition from the country-wide status of FMD disease-free with vaccination to without vaccination, which is expected to be achieved by 2023 (MAPA, 2017). In this context, the analysis of contact networks is a powerful tool to describe, predict and estimate the role of trade in the spread of diseases (LEBL et al., 2016; VANDERWAAL et al., 2016a), producing important data to understand, prevent, and mitigate possible outbreaks in the region.
Much of the research on livestock movement networks has used static representations of networks, which assumes that connections among farms are constant and unchanging through time. However, static representations over-represent the network’s connectivity (LENTZ; SELHORST; SOKOLOV, 2013). Temporal representations of networks consider the active and inactive connections according to a time interval, which is a more accurate characterization of network patterns (BÜTTNER; SALAU; KRIETER, 2016; CHATERS et al., 2019; MACHADO et al., 2020).
The objective of this study was to characterize the dynamics of pig trade, describe both static and temporal movement networks, and provide useful information to target surveillance actions in municipalities and individual premises in the state of Santa Catarina, Brazil, according to their importance in the network. Such rankings, which are based on different parameters derived through social network analysis (SNA), helps to improve the prevention, control, and eradication of communicable diseases spread through animal movements.
Material and methods