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