Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China

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 carryin...

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Autores principales:	Jiang-Hong Hu, Xin Pei, Gui-Quan Sun, Zhen Jin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	African swine fever virus transmission route pig transport network dynamic model assessing the infection risk Physics QC1-999
Acceso en línea:	https://doaj.org/article/60a400ff3d294b58ab443ae73b4cc755
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spelling	oai:doaj.org-article:60a400ff3d294b58ab443ae73b4cc7552021-11-30T18:51:05ZRisk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China2296-424X10.3389/fphy.2021.785885https://doaj.org/article/60a400ff3d294b58ab443ae73b4cc7552021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.785885/fullhttps://doaj.org/toc/2296-424XAfrican 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.Jiang-Hong HuJiang-Hong HuXin PeiGui-Quan SunGui-Quan SunZhen JinZhen JinFrontiers Media S.A.articleAfrican swine fever virustransmission routepig transport networkdynamic modelassessing the infection riskPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	African swine fever virus transmission route pig transport network dynamic model assessing the infection risk Physics QC1-999
spellingShingle	African swine fever virus transmission route pig transport network dynamic model assessing the infection risk Physics QC1-999 Jiang-Hong Hu Jiang-Hong Hu Xin Pei Gui-Quan Sun Gui-Quan Sun Zhen Jin Zhen Jin Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
description	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.
format	article
author	Jiang-Hong Hu Jiang-Hong Hu Xin Pei Gui-Quan Sun Gui-Quan Sun Zhen Jin Zhen Jin
author_facet	Jiang-Hong Hu Jiang-Hong Hu Xin Pei Gui-Quan Sun Gui-Quan Sun Zhen Jin Zhen Jin
author_sort	Jiang-Hong Hu
title	Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
title_short	Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
title_full	Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
title_fullStr	Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
title_full_unstemmed	Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China
title_sort	risk analysis of the transmission route for the african swine fever virus in mainland china
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/60a400ff3d294b58ab443ae73b4cc755
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Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China

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