In the face of COVID-19, an emerging infectious disease, in addition to the classic non-pharmaceutical interventions such as isolation, quarantine, social, China also adopted strict mobility restrictions including inter-administrative districts travel restrictions, which severely affect residents' lives and almost completely stopped production activities at cost of a huge economic and social cost. In this paper, we develop the model of Dirk Brockmann and Dirk Helbing (2013) to theoretically explain the impact mechanism of prevention and control measures on the spread of the epidemic. Then, we divide the measures taken in China into two categories: mobility restrictions and other non-pharmacological interventions (O-NPI), and apply econometric approach to empirically test the effects of them. We found that although both of the two measures play a good role in controlling the development of the epidemic, the effect shows significant difference in different regions, and both the two measures had no significant effects in low-risk regions; Further, we prove that measures taken in a low-risk region is mainly against the imported cases, while a high-risk region has to defend against both imported cases and spread from within; The rapid and accurate transmission of information, a higher protection awareness of the public, and a stronger confidence of residents can promote the implementation of the measures.

Various epidemic prevention and control measures aimed at reducing person-to-person contact has paid a certain cost while controlling the epidemic. So accurate evaluation of these measures helps to maximize the effectiveness of prevention and control while minimizing social costs. In this paper, we develop the model in Dirk Brockmann and Dirk Helbing (2013) to theoretically explain the impact mechanism of traffic control and social distancing measures on the spread of the epidemic, and empirically tests the effect of the two measures in China at the present stage using econometric approach. We found that both traffic control and social distancing measures have played a very good role in controlling the development of the epidemic. Nationally, social distancing measures are better than traffic control measures; the two measures are complementary and their combined action will play a better epidemic prevention effect; Traffic control and social distancing do not work everywhere. Traffic control only works in cities with higher GDP per capita and population size, while fails in cities with lower GDP per capita and population size. In cities with lower population size, social distancing becomes inoperative; the rapid and accurate transmission of information, a higher protection awareness of the public, and a stronger confidence of residents in epidemic prevention can promote the realization of the measure effects. The findings above verify the effectiveness and correctness of the measures implemented in China at present, at the same time, we propose that it is necessary to fully consider the respective characteristics of the two measures, cooperating and complementing each other; what's more, measures should be formulated according to the city's own situation, achieving precise epidemic prevention; Finally, we should increase the transparency of information, improve protection awareness of the public, guide emotions of the public in a proper way, enhancing public confidence.

We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city’s situation, in order to achieve a better epidemic prevention and control.

The COVID-19 epidemic in China has been effectively controlled. It is of great significance to study the law of cross-regional spread of the epidemic, for the prevention and control of the COVID-19 in the future in China and other countries or regions. In this study, the cross-regional connection intensity between cities was characterized based on the probability and the effective distance of the shortest path tree, and the empirical analysis was carried out based on the high-frequency data such as the cases of COVID 19 outbreaks. It is concluded that the higher the intensity of inter-city connection, the larger scale the cross-regional spread of the epidemic.

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above −7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as “warning cities”) have more days of “dangerous weather”, which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.