Decoding the complexity of large-scale pork supply chain networks in China

Purpose With the development of global food markets, the structural properties of supply chain networks have become key factors affecting the ability to evaluate and control infectious diseases and food contamination. The purpose of this paper is to describe and characterize the nationwide pork supply chain networks (PSCNs) in China and to demonstrate the potential of using social network analysis (SNA) methods for accessing outbreaks of diseases and contaminations. Design/methodology/approach A large-scale PSCN with 17,582 nodes and 49,554 edges is constructed, using the pork trade data collected by the National Important Products Traceability System (NIPTS) in China. A network analysis is applied to investigate the static and dynamic characteristics of the annual network and monthly networks. Then, the metric maximum spreading capacity (MSC) is proposed to quantify the spreading capacity of farms and estimate the potential maximum epidemic size. The structure of the network with the spatio-temporal pattern of the African swine fever (ASF) outbreak in China in 2018 was also analysed. Findings The results indicate that the out-degree distribution of farms approximately followed a power law. The pork supply market in China was active during April to July and December to January. The MSC is capable of estimating the potential maximum epidemic size of an outbreak, and the spreading of ASF was positively correlated with the effective distance from the origin city infected by ASF, rather than the geographical distance. Originality/value Empirical research on PSCNs in China is scarce due to the lack of comprehensive supply chain data. This study fills this gap by systematically examining the nationwide PSCN of China with large-scale reliable empirical data. The usage of MSC and effective distance can inform the implementation of risk-based control programmes for diseases and contaminations on PSCNs.