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First discovered in rural areas of middle-eastern China in 2009, severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis affecting hundreds of cases reported in China each year. Using the national surveillance data from 2010 to 2013, we conducted this retrospective epidemiological study and risk assessment of SFTS in China. We found that the incidence of SFTS and its epidemic areas are continuing to grow, but the case fatality rate (CFR) has steadily decreased. SFTS most commonly affected elderly farmers who acquired infection between May and July in middle-eastern China. However, other epidemiological characteristics such as incidence, sex ratio, CFR, and seasonality differ substantially across the affected provinces, which seem to be consistent with local agricultural activities and the seasonal abundance of ticks. Spatial scan statistics detected three hot spots of SFTS that accounted for 69.1% of SFTS cases in China. There was a strong association of SFTS incidence with temporal changes in the climate within the clusters. Multivariate modeling identified climate conditions, elevation, forest coverage, cattle density, and the presence of Haemaphysalis longicornis ticks as independent risk factors in the distribution of SFTS, based on which a predicted risk map of the disease was derived. [ABSTRACT FROM AUTHOR]

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.