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• The H7N9 virus epidemic was mainly concentrated in the Yangtze River Delta and Pearl River Delta. • The epidemic had obvious seasonality: mostly in winter and spring. • The epidemic had a significant correlation with temperature and precipitation. • The agent-based model combined meteorological factors, time, and environmental factors. From 2013 to 2017, the avian influenza A (H7N9) virus frequently infected people in China, which seriously affected the public health of society. This study aimed to analyze the spatial characteristics of human infection with the H7N9 virus in China and assess the risk areas of the epidemic. Using kernel density estimation, standard deviation ellipse analysis, spatial and temporal scanning cluster analysis, and Pearson correlation analysis, the spatial characteristics and possible risk factors of the epidemic were studied. Meteorological factors, time (month), and environmental factors were combined to establish an epidemic risk assessment proxy model to assess the risk range of an epidemic. The epidemic situation was significantly correlated with atmospheric pressure, temperature, and daily precipitation (P < 0.05), and there were six temporal and spatial clusters. The fitting accuracy of the epidemic risk assessment agent-based model for lower-risk, low-risk, medium-risk, and high-risk was 0.795, 0.672, 0.853, 0.825, respectively. This H7N9 epidemic was found to have more outbreaks in winter and spring. It gradually spread to the inland areas of China. This model reflects the risk areas of human infection with the H7N9 virus. [ABSTRACT FROM AUTHOR]

• The Spatio-temporal differences of the spread of the COVID-19 epidemic in 337 prefecture-level cities in China, as well as the social influencing factors (Baidu migration index, government response severity index) and natural influencing factors (meteorological factors, air pollutants) were analyzed. • The global and local spatial correlation characteristics (Moran's I) of the epidemic distribution were positively correlated, that is, the clustering trend was dominant. • Temporal and spatial clustering of the epidemic's distribution (i.e., areas with a high incidence of the epidemic and their corresponding high incidence time and risk), detected a total of six clusters. • Population migration, air pollution concentration, and low temperature played a positive role in the spread of COVID-19 in China. • Government response strictness index, that is, the formulation of relevant government policies has an inhibitory effect on the transmission of COVID-19, and the greater the intensity of government intervention, the fewer cases will occur. The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China, from January to October 2020. About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and prominent clustering regions. Spatio-temporal scanning analysis showed that most COVID-19 high-incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR = 491.57) which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. Environmental temperature has a certain inhibitory effect on the transmission of COVID-19; the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur. [ABSTRACT FROM AUTHOR]