Population mobility is a key component in promoting the re-agglomeration and dissemination of social and economic factors. Based on Spring Festival data from 2019 to 2023 on Baidu Migration Big Data, this paper analyses the spatiotemporal patterns and structural characteristics of population mobility in cities across China through spatiotemporal statistics and social network analysis and investigates the evolution patterns of Chinese population migration behavior under the influence of COVID-19 epidemic during Spring Festival. The results of the study show that: (1) There are significant temporal and spatial differences in the impact of COVID-19 on population migration, with much stronger shocks on the cities of middle migration scale; (2) Population migration in Chinese cities is robust, and the impact of COVID-19 on population movement and community evolution is mainly manifested in short-term effects, with essentially no residual effects; (3) Between 2020 and 2023, a total of 119 cities experience a transfer of communities (32.25%), of which 69 cities transfer once, 20 cities transfer twice, and 30 cities transfer three times. In addition, it is found that the closeness of urban links based on population movements remains subject to geospatial effects, and the boundaries of “communities” coincide very closely with provincial borders. The results of this study have important theoretical and practical implications for a deeper understanding of the long-term impact of major public health events on changes in the geographical characteristics of population distribution and the structure of population mobility networks.

Background: Internet searches offer an indicator of public attention and possible demand for certain things. Studying the spatiotemporal characteristics of the public’s concern for vaccination can determine the spatiotemporal distribution of demand for vaccines in China, and capture the changes in the health awareness of the Chinese population, thus informing future vaccination strategies. Methods: Based on the collection of Baidu search indices for vaccination-related keywords in 363 cities in China, This paper seeks to explore the spatiotemporal changes and regional differences in public attention toward vaccination in China by using the seasonal index, seasonal concentration index, Herfindahl index, Moran index, and Dagum Gini coefficient. Results: The following findings are presented. First, there are significant seasonal fluctuations and unbalanced monthly distributions of vaccination-related public attention in China. Second, the public attention in Chinese cities shows the spatial characteristics of "leading in the east, followed by the central, western and northeastern regions". The spatial correlation of attention has been strengthened, and the high-high clusters are mainly distributed in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Greater Bay Area (GBA) urban agglomerations. Third, regional differences in overall public attention narrowed in China, with intra-regional differences narrowing in seven regions (Northwest China, Central China, and so on), and intra-regional differences increasing in East China. The dominant role in the Gini coefficient changes from transvariation intensity to inter-regional differences. Conclusion: Major public health emergencies stimulate the public’s attention to health topics. Although the short-term increase in vaccination-related public attention was not observed to translate into a long-term increase in public vaccine literacy, the seasonal and regional differences in vaccination-related public attention in China have significantly narrowed before and after COVID-19, suggesting that the imbalance between public health literacy levels has improved.

The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.

Purpose: Immunization programs depend on high vaccination coverage to effectively lower the incidence and prevalence of specific infectious diseases. This study examines the relationship between public and media attention to vaccines and vaccination coverage during COVID-19 in China, drawing on behavioral agenda-setting theory and considering the influence of online health information on real-world health behaviors. Design/methodology/approach: We collected daily data on COVID-19 vaccine doses administered in China from April 13, 2021, to September 19, 2022, and quantified public and media attention using the Baidu Search Index and Baidu Media Index over the same period. Time series analysis, logarithmic fitting, and Granger causality tests were conducted to analyze relationships and temporal dynamics among the datasets. Findings: Public attention to vaccination spurred vaccination-related media coverage and contributed to increased vaccine uptake. Notably, the decay tail of the distribution function for vaccination doses was longer than for public and media attention, revealing a long-term “echo effect” of online information exposure on vaccination behavior. Granger causality test revealed that the media attention did not Granger cause the public attention, suggesting a reconfiguration of the classic agenda-setting dynamic between the public and media. Originality/value: This study provides evidence-based insights into the temporal dynamics between health information acquisition and health behaviors, while also revealing a reverse agenda-setting relationship between media and public attention in the health domain.