Abstract
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 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.
Highlights
The novel avian influenza A(H7N9) virus emerged in 2013
RESULTS we provide the simulation results from the above algorithms based on the constructed live poultry transport network and the established A(H7N9) transmission model
DETECTED INFECTION SOURCES Take Suzhou City, where the A(H7N9) virus first occurred and the largest number of A(H7N9) human cases occurred in the 5th wave, as an example
Summary
The novel avian influenza A(H7N9) virus emerged in 2013. It is a bird flu strain of the influenza virus A (avian influenza virus or bird flu virus) [1]. X. Pei et al.: Detection of Infection Sources for Avian Influenza A(H7N9) in Live Poultry Transport Network in mainland China. The A(H7N9) virus strains circulating among poultry had been classified as low pathogenicity avian influenza (LPAI) in the previous four epidemic waves in China [9], but evolved to be highly pathogenic in poultry in the fifth epidemic wave [10]. According to the network constructed and model established, we propose spatiotemporal backward detection and forward transmission algorithms to detect the infection source of the epidemic. The simulation results from the algorithms are shown, which include the detected infection sources, first arrival time and maximum likelihood L(t, u) of infection sources, most likely spread map, and temperature characteristics in a given location at the arrival time of the A(H7N9) virus. In the final section of the paper, we present our major conclusions
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