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Following the emergence of H7N9 influenza in March 2013, local animal and public health authorities in China have been closing live bird markets as a measure to try to control the H7N9 influenza epidemic. The role of live bird market (LBM) closure on the spread of N7N9 influenza following the closure of LBMs during March to May 2013 (the first wave) and October 2013 to March 2014 (the second wave) is described in this paper. Different provinces implemented closure actions at different times, and intensive media reports on H7N9 in different provinces started at different times. Local broiler prices dropped dramatically in places with outbreaks and more live chickens were transported to other LBMs in neighboring areas without human cases from infected areas when live bird markets were being closed. There were six clusters of human infection from March to May 2013 and October 2013 to March 2014 and there may have been intensive poultry transportation among cluster areas. These findings provide evidence that the closure of LBMs in early waves of H7N9 influenza had resulted in expansion of H7N9 infection to uninfected areas. This suggests that provincial authorities in inland provinces should be alert to the risks of sudden changes in movement patterns for live birds after LBM closure or increased publicity about LBM closure. [ABSTRACT FROM AUTHOR]

Background: Zoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China. Methods and findings: In this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km2 cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9. Conclusions: We provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections. [ABSTRACT FROM AUTHOR]