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.