Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China

Numerous studies have demonstrated that exposure to live poultry or live poultry markets is the significant risk factor for human infection with avian influenza A(H7N9). However, the specific live poultry markets that are major infection sources for A(H7N9) human cases have not been explored in deta...

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Main Authors: Xin Pei, Zhen Jin, Wenyi Zhang, Yong Wang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8883166/
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spelling doaj-8133e23feb5941d398a789edc5f209312021-03-30T00:45:10ZengIEEEIEEE Access2169-35362019-01-01715575915577810.1109/ACCESS.2019.29496068883166Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in ChinaXin Pei0Zhen Jin1https://orcid.org/0000-0002-9763-707XWenyi Zhang2Yong Wang3Data Science and Technology, North University of China, Taiyuan, ChinaData Science and Technology, North University of China, Taiyuan, ChinaChinese PLA Center for Disease Control and Prevention, Beijing, ChinaChinese PLA Center for Disease Control and Prevention, Beijing, ChinaNumerous studies have demonstrated that exposure to live poultry or live poultry markets is the significant risk factor for human infection with avian influenza A(H7N9). However, the specific live poultry markets that are major infection sources for A(H7N9) human cases have not been explored in detail. In this study, we extract data associated with poultry farms, live poultry markets and farmers' markets from Baidu Map using the JavaScript language and then construct the live poultry transport network. From this, we establish our A(H7N9) transmission model over the network based upon probabilistic discrete-time Markov chain. On the basis of the obtained network and model, we propose spatiotemporal backward detection and forward transmission algorithms to detect the most likely infection sources and to compute the first arrival times of the infection sources. Our simulations use these algorithms to identify the specific locations of the infection sources, the first arrival times of the infection sources and the most likely transmission map of the A(H7N9) virus along the live poultry transport network. The results reveal that, in addition to the hazards posed by the live poultry markets, backyard poultry also contributed to A(H7N9) human infections; this risk source was significant especially in the newly affected provinces, in the fifth wave of infection. In particular, by analyzing the temperature characteristics at a given location when the infection source arrived, we find that the risk of human infection with the influenza A(H7N9) virus was high under 9°C~19°C; moderate under 0°C~9°C or 19°C~25°C; and low for temperatures <; 0°C or >25°C. Our results suggest that strengthening the supervision of the live poultry market system and immunizing poultry at both live poultry markets and the backyard poultry operations under the high risk temperature band of 9°C~19°C, will be able to significantly contribute to the control of avian influenza A(H7N9) in the future.https://ieeexplore.ieee.org/document/8883166/Live poultry transport networkavian influenza A(H7N9)transmission modeldetecting infection sources
collection DOAJ
language English
format Article
sources DOAJ
author Xin Pei
Zhen Jin
Wenyi Zhang
Yong Wang
spellingShingle Xin Pei
Zhen Jin
Wenyi Zhang
Yong Wang
Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
IEEE Access
Live poultry transport network
avian influenza A(H7N9)
transmission model
detecting infection sources
author_facet Xin Pei
Zhen Jin
Wenyi Zhang
Yong Wang
author_sort Xin Pei
title Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
title_short Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
title_full Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
title_fullStr Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
title_full_unstemmed Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network During the Fifth Wave in China
title_sort detection of infection sources for avian influenza a(h7n9) in live poultry transport network during the fifth wave in china
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Numerous studies have demonstrated that exposure to live poultry or live poultry markets is the significant risk factor for human infection with avian influenza A(H7N9). However, the specific live poultry markets that are major infection sources for A(H7N9) human cases have not been explored in detail. In this study, we extract data associated with poultry farms, live poultry markets and farmers' markets from Baidu Map using the JavaScript language and then construct the live poultry transport network. From this, we establish our A(H7N9) transmission model over the network based upon probabilistic discrete-time Markov chain. On the basis of the obtained network and model, we propose spatiotemporal backward detection and forward transmission algorithms to detect the most likely infection sources and to compute the first arrival times of the infection sources. Our simulations use these algorithms to identify the specific locations of the infection sources, the first arrival times of the infection sources and the most likely transmission map of the A(H7N9) virus along the live poultry transport network. The results reveal that, in addition to the hazards posed by the live poultry markets, backyard poultry also contributed to A(H7N9) human infections; this risk source was significant especially in the newly affected provinces, in the fifth wave of infection. In particular, by analyzing the temperature characteristics at a given location when the infection source arrived, we find that the risk of human infection with the influenza A(H7N9) virus was high under 9°C~19°C; moderate under 0°C~9°C or 19°C~25°C; and low for temperatures <; 0°C or >25°C. Our results suggest that strengthening the supervision of the live poultry market system and immunizing poultry at both live poultry markets and the backyard poultry operations under the high risk temperature band of 9°C~19°C, will be able to significantly contribute to the control of avian influenza A(H7N9) in the future.
topic Live poultry transport network
avian influenza A(H7N9)
transmission model
detecting infection sources
url https://ieeexplore.ieee.org/document/8883166/
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