Network Analysis of Swine Shipments in China: The First Step to Inform Disease Surveillance and Risk Mitigation Strategies

Abstract

China's pork industry has been dramatically changing in the last few years. Pork imports are increasing, and small-scale farms are being consolidated into large-scale multi-site facilities. These industry changes increase the need for traceability and science-based decisions around disease monitoring, surveillance, risk mitigation, and outbreak response. This study evaluated the network structure and dynamics of a typical large-scale multi-site swine facility in China, as well as the implications for disease spread using network-based metrics. Forward reachability paths were used to demonstrate the extent of epidemic spread under variable site and temporal disease introductions. Swine movements were found to be seasonal, with more movements at the beginning of the year, and fewer movements of larger pigs later in the year. The network was highly egocentric, with those farms within the evaluated production system demonstrating high connectivity. Those farms which would contribute the highest epidemic potential were identified. Among these, different farms contributed to higher expected epidemic spread at different times of the year. Using these approaches, increased availability of swine movement networks in China could help to identify priority locations for surveillance and risk mitigation for both endemic problems and transboundary diseases such as the recently introduced, and rapidly spreading, African swine fever virus.

Dynamics of African swine fever virus (ASFV) infection in domestic pigs infected with virulent, moderate virulent and attenuated genotype II ASFV European isolates

“…As of June 2021, the virus has spread to 13 Asian countries affecting Mongolia, Vietnam (all provinces and municipalities), Cambodia, Democratic People's Republic of Korea, Laos (all provinces and municipalities), Myanmar, Philippines, Republic of Korea, Timor‐Leste, Indonesia, India and Malaysia, as well as Papua New Guinea in Oceania (FAO, 2021). The reasons for this uncontrolled spread of the virus are related not only to the absence of vaccines or treatments against ASF, but also to the illegal transport of pigs or pork products and the complex structure of the pig industry in China that positions the legal trade as a high risk factor for the spread of the ASFV (Gao et al., 2021; O´Hara et al., 2020). The lack of knowledge of different aspects of the disease, mainly related to its complex epidemiology, and the transmission capacity of the virus have contributed to its rapid spread.…”

Network analysis of live pig movements in North Macedonia: Pathways for disease spread

“…Centrality measures of in-degree and out-degree were calculated for each node. In-degree is defined as the number of incoming shipments to a production site, out-degree is the number of outgoing shipments from a production site ( 35 , 40 , 41 ). Betweenness is the number of shortest paths between any pair of nodes in the network that pass through an individual node ( 40 – 42 ).…”

“…Betweenness is the number of shortest paths between any pair of nodes in the network that pass through an individual node ( 40 – 42 ). Diameter is the longest of all the shortest path lengths between nodes in the network ( 35 , 40 , 41 ). Edge density is the ratio of the number of edges observed in the network to the number of possible edges ( 35 , 40 , 41 ).…”

“…Diameter is the longest of all the shortest path lengths between nodes in the network ( 35 , 40 , 41 ). Edge density is the ratio of the number of edges observed in the network to the number of possible edges ( 35 , 40 , 41 ). Average path length is the mean length of all the shortest paths between nodes in the network ( 43 ).…”

“…The predominance of small-scale subsistence farmers in North Macedonia, highlights the potential impact of a TAD of swine on food production and security in the country ( 6 ). SNA applied to the pig industry may also allow for the identification of potential super-spreaders (nodes likely to spread disease fastest or to the most additional nodes given their network contacts) or super-receivers (nodes at highest risk of disease exposure due to receipt of incoming movements from the most other nodes) of disease within North Macedonia's pork industry chain, providing targeted locations for increased surveillance and risk mitigation ( 33 , 35 ).…”