A stochastic assessment to quantify the risk of introduction of African swine fever virus to Taiwan via illegal pork products carried by international travellers

2021 ◽  
Author(s):  
Kendy Tzu‐Yun Teng ◽  
Chao‐Chin Chang ◽  
Yi‐Lun Tsai ◽  
Chun‐Yao Chiu ◽  
Cheng‐Yao Yang ◽  
...  
Keyword(s):  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus ◽  
Pork Products

scholarly journals Detection of African swine fever virus in pork products brought to Taiwan by travellers

2019 ◽  
Vol 8 (1) ◽  
pp. 1000-1002 ◽  
Author(s):  
Wen-Hung Wang ◽  
Chih-Yen Lin ◽  
Max R. Chang Ishcol ◽  
Aspiro Nayim Urbina ◽  
Wanchai Assavalapsakul ◽  
...  
Keyword(s):  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus ◽  
Pork Products

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiang-Hong Hu ◽  
Xin Pei ◽  
Gui-Quan Sun ◽  
Zhen Jin
Keyword(s):  
African Swine Fever Virus ◽  
Mainland China ◽  
African Swine Fever ◽  
Infection Risk ◽  
Fever Virus ◽  
Transmission Model ◽  
Transmission Route ◽  
Pork Products ◽  
Python Language ◽  
And Control

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.

Genetic markers of the African swine fever virus

2020 ◽  
Vol 23 (04) ◽  
pp. 21-26
Author(s):  
A.K. Sibgatullova ◽  
◽  
M.E. Vlasov ◽  
I.A. Titov ◽  
◽  
...  
Keyword(s):  
Genetic Markers ◽  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus

Multigene families in African swine fever virus: family 110.

1990 ◽  
Vol 64 (5) ◽  
pp. 2064-2072 ◽  
Author(s):  
J M Almendral ◽  
F Almazán ◽  
R Blasco ◽  
E Viñuela
Keyword(s):  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus ◽  
Multigene Families ◽  
Virus Family

GS-441524 inhibits African swine fever virus infection in vitro

2021 ◽  
pp. 105081
Author(s):  
Zhao Huang ◽  
Lang Gong ◽  
Zezhong Zheng ◽  
Qi Gao ◽  
Xiongnan Chen ◽  
...  
Keyword(s):  
Virus Infection ◽  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus
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