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It has been documented that the epidemiological characteristics of human infections with H7N9 differ significantly between H5N1. However, potential factors that may explain the different spatial distributions remain unexplored. We use boosted regression tree (BRT) models to explore the association of agro-ecological, environmental and meteorological variables with the occurrence of human cases of H7N9 and H5N1 and map the probabilities of occurrence of human cases. Live poultry markets, density of human, coverage of built-up land, relative humidity and precipitation were significant predictors for both. In addition, density of poultry, coverage of shrub and temperature played important roles for human H7N9 infection, whereas human H5N1 infection was associated with coverage of forest and water body. Based on the risks and distribution of ecological characteristics which may facilitate the circulation of the two viruses, we found Yangtze River Delta and Pearl River Delta, along with a few spots on the southeast coastline, to be the high risk areas for H7N9 and H5N1. Additional, H5N1 risk spots were identified in eastern Sichuan and southern Yunnan Provinces. Surveillance of the two viruses needs to be enhanced in these high risk areas to reduce the risk of future epidemics of avian influenza in China.

The outbreak of human infections with an emerging avian influenza A (H7N9) virus occurred in China in early 2013. It remains unknown what and how the underlying risk factors were involved in the bird-to-human cross-species transmission. To illustrate the dynamics of viral spread, we created a thematic map displaying the distribution of affected counties and plotted epidemic curves for the three most affected provinces and the whole country. We then collected data of agro-ecological, environmental and meteorological factors at the county level, and used boosted regression tree (BRT) models to examine the relative contribution of each factor and map the probability of occurrence of human H7N9 infection. We found that live poultry markets, human population density, irrigated croplands, built-up land, relative humidity and temperature significantly contributed to the occurrence of human infection with H7N9 virus. The discriminatory ability of the model was up to 97.4%. A map showing the areas with high risk for human H7N9 infection was created based on the model. These findings could be used to inform targeted surveillance and control efforts in both human and animal populations to reduce the risk of future human infections.