Compared with the spatio-temporal distribution of SARS in mainland China in 2003, COVID-19 showed different spatio-temporal aggregation patterns, which may be due to changes in social and demographic factors, different control strategies of local governments, and differences in transmission mechanisms of coronaviruses[4]. In this study, the PR of the selected provinces and cities showed a certain degree of spatial aggregation, that is, the farther away from Hubei Province, the lower the PR. This may be due to the fact that the deadline for the collection of confirmed cases was the end of February, 2020 when the proportion of imported cases was relatively high, corresponding to the relatively high turnover rates of nearby provinces and cities. The city of Shenzhen, however, is an exception. Although it is far away from Hubei Province, the PR was also relatively high by the end of February. The reason may be that the city of Shenzhen is a special economic zone with a high inter-provincial population turnover rate, which bring more imported cases to Shenzhen than its neighboring provinces and cities, and its lowest PLI also supports this hypothesis to some extent. While the PR in the city of Shenzhen is high, the PLI is low, whereas the opposite is true for the city of Tianjin. It was found that the average temperature in the city of Tianjin in January was between -5℃ and 2℃, and the average temperature in February was between -3℃ and 6℃. In Shenzhen, the average temperature in January was between 13℃ and 20℃, and the average temperature in February was between 14℃ and 21℃. It may be inferred that the PLI of COVID-19 will be affected when the temperature is high, and this conjecture is confirmed by related studies [17].
Reviewing the outbreak of SARS in 2003, it was found that the outbreak gradually subsided with the warming of the weather, and was basically under control by April and May[22], and ended completely in July. This indicated that the change of temperature may have affected the outbreak of SARS[23, 24], and SARS-CoV-2, which is similar to SARS-CoV[25], may thus also be affected by temperature. A study of total 24139 confirmed COVID-19 cases in China and 26 other countries also found that temperature can significantly change the spread of COVID-19[26]. The change of the early epidemic growth rate is consistent with the influence of local environmental conditions on the spread of the disease[27], Guo's study also confirms that there is a negative correlation between R0 and temperature[18]. This suggests that there may be an optimal temperature for the spread of the virus, and that the rates of incidence and spread of COVID-19 are expected to slow down with the advent of spring and summer[19]. These results are in agreement with those of previous studies modelling the aerosol transmission of the influenza virus in animals[21]. The results of this project concur that the temperature in January and February influenced the PLI.
Some studies have shown that COVID-19 mortality has a significant negative correlation to temperature, showing a significant negative correlation[20]. When the temperature increased, the mortality of ordinary and severe patients decreased significantly[22]. And previous studies have shown that both cold and heat may adversely affect the mortality of respiratory diseases[28], the results of this study show that the PLI of COVID-19 is negatively related to temperature, which may be due to the fact that the viability of SARS-CoV-2 is reduced with higher temperatures, which leads to a decrease in the incidence of local cases. The results of this study show that the higher the latitude, the higher the PLI, which also supports this view as temperatures are generally lower at higher latitudes.
A study in the United States shows that the growth rate of confirmed cases of COVID-19 is related to the size of the urban population, which means that the average rate of spread of SARS-CoV-2 in big cities is faster. If control measures are not taken, a larger proportion of the population will be infected in urban areas with larger populations[29]. However, according to the results of this analysis, the population density had the opposite effect on the PLI of COVID-19 in China. A possible reason may be that after the city of Wuhan announced the city closure, various other provinces and cities in China with large populations and high levels of labor movement immediately began implementing epidemic prevention and control measures[30, 31]. For example, Henan and Hunan Provinces are major labor export provinces, exporting more than 15 million people, with Henan Province reaching 28.76 million. And examples of these measures included: the prohibition of visiting during the Spring Festival; the closure of selected highway entrances and exits; the establishment of health and quarantine stations; the control of all provincial and country roads; and the isolation of Wuhan workers returning to their residences in other locations, and the tracking of their close contacts. These measures contributed to the prevention of further outbreaks in other provinces of China, which may have affected the original impact of population density on the spread of COVID-19. This study, shows that the higher the population density, the lower the PLI for the selected areas.
Studies have shown that the impact of the epidemic on the economy is very serious[32]. Before the outbreak of the epidemic, millions of people traveled around the world every year, contributing positively to the global economy. Simultaneously, the increase of employment opportunities in the tourism industry also contributed to the global GDP. Since the spread of the global pandemic; however, the effect on tourism has had a negative impact on the global economy[33]. Experts believe that the impact of the COVID-19 epidemic may be more far-reaching than that of the Great Depression of the 1930s[34]. It has been observed that most studies focus on the resultant impact of the epidemic on GDP, whereas fewer studies focus on the impact of GDP on the outbreak. This study analyzed the effect of GDP on the PLI in 20 provinces and cities in China, and the results showed that the effect of GDP on the outbreak was not statistically significant. The reason may be that the implementation of the above preventive control measures may offset the impact of GDP on the outbreak of the epidemic.
The strict implementation of prevention and control measures has effectively contained the spread of the epidemic and reduced the demand for medical resources in other provinces of China except Hubei[35]. This may be the reason for the number of medical institutions, the number of medical institutions per capita, health practitioners, and health practitioners per capita to have no statistically significant effect on the PLI in the results of this study. Research also suggests that when the epidemic develops early or when the number of imported cases is limited, strict containment, defense, and suppression strategies can effectively reduce the demand for medical resources and will not cause impact on the medical system by the results of model simulation[36]. The tracking and management of close contacts of patients with COVID-19 are important components of prevention and control measures[37]. So we can see that the emergency measures implemented by the Chinese government since late January have played a decisive role in the prevention and control of the epidemic, and the evidence shows that the active cooperation of the public can break the original chain of virus transmission[38]. For example, if close contacts of COVID-19 patients agreed to be isolated, the public can do unnecessary going out and wear masks when going out, it can effectively block the transmission route and control the number of infected people, thereby reducing the burden of medical resources.
Limitations
There are also limitations in this study. First, the sample size selected is small, as only the relevant data from 21 provinces and cities in China were selected. This may affect the results of the statistical analysis. Second, the impact of various prevention and control policies and the willingness of the general public to co-operate needs to be further studied.