African swine fever first broke out in mainland China in August 2018 and has caused a substantial loss to China’s pig industry. Numerous investigations have confirmed that trades and movements of infected pigs and pork products, feeding pigs with contaminative swills, employees, and vehicles carrying the virus are the main transmission routes of the African swine fever virus (ASFV) in mainland China. However, which transmission route is more risky and what is the specific transmission map are still not clear enough. In this study, we crawl the data related to pig farms and slaughterhouses from Baidu Map by writing the Python language and then construct the pig transport network. Following this, we establish an ASFV transmission model over the network based on probabilistic discrete-time Markov chains. Furthermore, we propose spatiotemporal backward detection and forward transmission algorithms in semi-directed weighted networks. Through the simulation and calculation, the risk of transmission routes is analyzed, and the results reveal that the infection risk for employees and vehicles with the virus is the highest, followed by contaminative swills, and the transportation of pigs and pork products is the lowest; the most likely transmission map is deduced, and it is found that ASFV spreads from northeast China to southwest China and then to west; in addition, the infection risk in each province at different times is assessed, which can provide effective suggestions for the prevention and control of ASFV.
An analysis of select swine feed ingredients and pork products imported into the US from African Swine Fever Virus (ASFV) Affected Countries
