The sudden outbreak of the novel coronavirus disease (COVID-19), an unexpected emergency event in human society, is spreading globally and has severe impacts on human health and social development. Based on the confirmed COVID-19 cases' details manually extracted from the official reports and the relevant Point of Interest (POI) data, this paper aims to explore the sociodemographic characteristics of confirmed COVID-19 cases and examine the spatio-temporal evolution patterns on different spatial scales in China by using text analysis, spatial analysis and other methods. Furthermore, we provide targeted measures for prevention and control of this epidemic. The results indicate that: (1) In contrast to the twenty-four solar terms, the epidemic started at Heavy Snow, ran rampant in the Beginning of Spring, declined in the Awakening of Insects, and stagnated at the Vernal Equinox. It experienced four stages: the early period of Wuhan dominant outbreak, the middle period of cross-country diffusion, the later period of Wuhan dominant decline and the ending period of rare local epidemic transmission. (2) The spatial distribution presents a pattern of "one core, two arcs and multiple islands", mainly affected by the distance to Wuhan and the flow of population; the spatial evolution is manifested in the mode of "core outbreak - cluster occurrence - dispersion occurrence - point occurrence". The core aggregation area in each stage was located in Hubei province. Finally, by integrating the influencing factors and effective measures of the epidemic evolution in China, this paper proposes effective countermeasures for the spread of the current epidemic and the coordinated development of human resources in response to similar challenges in the future, which are manifested in instant response, coordinated cooperation, regular prevention and control, and strict prevention of importation, etc. Also, the directions of in-depth study in the future are pointed out.

The COVID-19 epidemic is a global public health crisis. It is of practical significance to objectively understand the public's responses and regional differences in order to improve policy control and scientific governance during major public health threats. In this study, a topic extraction and classification model was constructed based on the Latent Dirichlet Allocation topic model and the Random Forest algorithm. Thirteen topics were identified about public opinion in the Chinese SINA microblog from January 9 to March 10, 2020. The regional distribution characteristics were explored in terms of the amount, space, time sequence, and content in major urban agglomerations including Hubei Province, Beijing-Tianjin-Hebei urban agglomeration, Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing region, and some border ports of China. The results showed that the spatio-temporal distribution of public opinion is related to the severity of the epidemic, degree of population aggregation, and level of economic development. The response of Chinese people is rational and positive, and the spatial distribution within these regions is obviously different. Among the regional hotspots, Beijing-Tianjin-Hebei region is centred on Beijing; the Yangtze River Delta is centred on Shanghai, followed by Nanjing, Hangzhou and other hotspots; the Pearl River Delta is centred on Guangzhou and Shenzhen; and Hubei Province is centred on Wuhan. The time series of topics in each region are synchronously related, but there are differences in timing sequence and periodic fluctuation in response time and intensity. The imbalance of resource allocation caused by the sharp rise of relief information in the short term is prominent, and the differences in response policies of various urban agglomerations combined with regional characteristics are not obvious. We should continue to focus on public opinion on epidemic situations in key areas and accurately respond to local regions according to its actual conditions.

The COVID-19 epidemic in 2020 has a severe impact on China's national economic and social development. Evaluating the short-term impact of the COVID-19 epidemic and the recovery of China's economy and society, as well as revealing its spatiotemporal characteristics, can provide a strong support for the economic situation research and urban restoration of the normalized epidemic prevention and control stage. Based on Baidu migration big data from January 13 to April 8 in 2020 and that of the same period in history, this paper constructs the Relative Recovery Index (RRI) and Recovery Gap Index (RGI). Furthermore, it reveals the daily characteristics, stage characteristics, and spatiotemporal patterns of the short-term impact of the COVID-19 epidemic at multiple scales. The results are as follows: (1) The outbreak did not affect the travel rush before the Spring Festival. The process after the Spring Festival experienced a recovery stagnation period, a rapid recovery period, and a slow recovery period. The overall degree of recovery nationwide rose from less than 20% during the stagnation period to about 60% at the end of the rapid recovery period. The slow recovery period began on March 3, with a recovery index of over 70%. It will take a long time to fully recover to the historical level. (2) The intercity activities on weekends and in holidays were significantly weakened, especially in the central and northeastern regions. (3) The impact of the epidemic on each region is significantly different, in terms of the RRI, the western region > eastern region > central region > northeastern region. (4) The degree of recovery varies significantly between cities. From the Spring Festival to April 8th, the spatial pattern was high in the south and low in the north. According to the severity of the epidemic, Guangzhou, Shenzhen and Chongqing are in the cluster of High confirmed case-High recovery; Hebei, Tianjin, Heilongjiang, Henan, Anhui and Hunan are in the cluster of Low confirmed case-Low recovery. (5) With the effective control of the epidemic, the recovery gap has shifted from the large-scale insufficiency of labor force in the urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta into the insufficiency in the central cities and some provincial capital cities. The results of this paper show that the use of spatiotemporal big data for real-time impact assessment of major public health emergencies have good application prospects.

Population migration, especially population input from epidemic area, is a key source of the risk related to the COVID-19 epidemic. Taking Guangdong Province as an example, this paper utilizes big data on population migration and the geospatial analysis technique to develop a model to conduct spatiotemporal analysis of COVID-19 risk. The model considers the risk differences among the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities. It further incorporates a time-lag process based on the time distribution of the onset of the imported cases. The model can predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and support the future planning and targeted prevention measures. The research findings indicate that: (1) The COVID-19 epidemic in Guangdong reached a inflection point on January 29, 2020, and then it showed a gradual decline. (2) Based on the time-lag analysis of the onset of the imported cases, there is a time interval between the case importation and the illness onset, and the cases with an interval of 1-14 days account for a high proportion. (3) There are obvious spatial differences in the risk of epidemics, based on their imported risk, susceptibility risk, and risk resisting ability. (4) The connection and the scale of population migration as well as the transportation and location factors of the cities in Guangdong's prefecture-level cities and the source regions of the epidemic, all have significant impacts on the risk classification of the cities in the province. The first-tier cities such as Shenzhen and Guangzhou are the high-risk areas. The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou, including Dongguan, Foshan, Huizhou, Zhuhai and Zhongshan, are the medium-risk cities. The eastern, northern, and western parts of Guangdong, which are outside the metropolitan areas of the Pearl River Delta, are classed into low-risk areas. Therefore, the government should take targeted prevention and control measures in different regions based on local conditions and risk classification so as to ensure people's daily life and wellbeing to the greatest possible extent.