The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread. We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics. Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Bothr and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17-20, 2020, and in Hubei from January 23-24. No obvious third-generation spread was detected outside Hubei. The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads.

The outbreak of novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The complete epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to elsewhere until January 20th, 2020. The space-time basic reproduction number (R0) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R0 estimates to decrease to 80% and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain, and the findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide.

Knowing the multi-level influences of determinants on medical-service resumptions is of great benefits to the policymaking for medical-service recovery at different levels of study units during the post-COVID-19 pandemic era. This article evaluated the hospital- and city-level resumptions of medical services in mainland China based on the data of location-based service (LBS) requests of mobile devices during the two time periods (December 2019 and from February 21 to March 18, 2020). We selected medical-service capacity, human movement, epidemic severity, and socioeconomic factors as the potential determinants on medical-service resumptions and then explicitly assessed their multi-level explanatory powers and the interactive effects of paired determinants using the geographical detector method. The results indicate that various determinants had different individual explanatory powers and interactive relationships/effects at different levels of medical-service resumptions. The current study provides a novel multi-level insight for assessing work resumption and individual/interactive influences of determinants, and considerable implications for regionalized recovery strategies of medical services.