1. INTRODUCTION
Classical swine fever (CSF) is one of the most important viral diseases
affecting pig production, the disease has multiple manifestations that
depend on the host and viral factors, also is highly contagious (Blome
et al., 2017) (OIE, 2020). Most countries have regulatory control
measures in place, although the effectiveness of these measures depends
on the state of development of their veterinary services and laboratory
capacities (Edwards et al., 2000). In South America, CSF is endemic in
Bolivia, Guyana, Peru, Suriname and Venezuela. In Brazil, Colombia and
Ecuador, the disease is still present but limited by recognised OIE free
zones (OIE-WAHIS, 2021).
Pig movements are linked to the national and international spread of pig
(Sus scrofa ) pathogens (VanderWaal and Deen, 2018)
(Beltran-Alcrudo et al., 2019). Indeed, they represent one of the most
important pathways for disease spread between premises (Nöremark et al.,
2011) (Fritzemeier, 2000) (Moon et al., 2019), their link with CSF
outbreaks has been documented in Colombia (Pineda et al., 2020), Germany
(Fritzemeier, 2000), Spain (Allepuz et al., 2007), and the Netherlands
(Elber et al., 1999). A better comprehension of pig movement network may
improve the mitigation strategies used to reduce the risk of disease
spread (Moon et al., 2019).
Relations have been previously analysed between the movement of live
animals and the introduction of swine diseases to Ecuador: CSF from Peru
(neighbouring country) (Garrido Haro et al., 2018) and porcine epidemic
diarrhoea from Chile and the US (Barrera et al., 2017). However, the
internal livestock movement registry was historically focused mainly in
cattle, for the control of foot and mouth disease.
In 2016, the compulsory vaccination campaign against CSF started, along
with individual identification of animals by official ear tags, stricter
mobilisation regulations and a web-based control system (Acosta et al.,
2019).
A contact network or graph is a set of nodes and connecting edges
representing complex systems, such as animal movements (Dubé et al.,
2009) (Newman, 2010). Indeed, the description of livestock movement
patterns provides essential information to understanding the observed
distribution of infectious diseases (Crescio et al., 2020). Detection of
trade communities has proven useful in identifying clusters of premises
with a high frequency of interactions, which can be targeted at
intervention strategies or prioritized by surveillance systems (Gorsich
et al., 2016).
To implement prevention and control programs, network analysis can help
explain some of the epidemiological patterns underlying the spread of
diseases (Salathé and Jones, 2010) (Tillett, 1992), it can also
contribute to implement more efficient disease monitoring and control
strategies (Knific et al., 2020) (Cannon, 2009). For example: in
Argentina, the network structure of movements provided information to
build cost-effective surveillance (Baron et al., 2020); In Brazil, a
network-based risk index was created to prioritise the surveillance
actions at the municipality level (Cespedes et al., 2021); In Peru,
network analysis was used to quantify the risk of CSF associated with
movements from districts that had recently experienced outbreaks
(Gomez-Vazquez et al., 2019).
However,
in other low-income Andean countries, such as Venezuela, Colombia,
Ecuador and Bolivia, information and analysis on livestock movements
remains limited (Todaro and Smith, 2009).
The pig sector in Ecuador is an important source of protein (ASPE, 2016)
linked to a strong cultural heritage and traditional cuisine (Procel,
2019). Mainly in backyard premises, pigs are used by producers for
self-consuption and also for profit selling them on markets (Lowenstein
et al., 2016).
In this paper, we present the first exploratory network analysis of the
pig trade network at premise and parish levels, using the official trade
data (2017–2019) and Social network analysis (SNA). The aim of this
paper was to explore the characteristics of the pig network, detect
network communities, unravel the spatial structure of movements and
analyse the contribution of the network to CSF transmission.