ABSTRACT
To explore the early stage spatial-temporal characteristics and to assess the factors of atmospheric pollution that may affect the development of coronavirus disease 2019（COVID-19）outbreak in the Chinese Mainland in 2020，we collected the daily new cases of COVID-19 in the Municipalities and Provinces from the websites of National and Provincial Health Commission of China. The spatiotemporal characteristics of COVID-19 epidemic were studied using autocorrelation analysis and trend analysis. The Spearman's correlation coefficient for ranked data and generalized additive model were used for risk assessment of air pollutants affecting the COVID-19 epidemic of Hubei Province. Daily new cases of COVID-19 in the Chinese Mainland totaled 39 877 from January 20th to February 9th of 2020. The global Moran index values of these three weeks were 0.249，0.307 and 0.297（P<0.01），respectively. There was a significant clustering phenomenon. The high incidence regions included Hunan Province，Guangdong Province，Jiangxi Province，Zhejiang Province，Anhui Province and Jiangsu Province. The epidemic hot spots were basically distributed in the area from 108° 47'-123° 10' E to 25° 31'-35° 20' N. Daily new cases of COVID-19 in Hubei Province was positively correlated with daily average concentrations of PM10，NO2 and O3 pollutants（ρ =0.515，0.579 and 0.536，P<0.05）. The lag effects of air pollutions were existed. The relative risk（RR）values of PM2.5and PM10 reached to maximum with lag0，the RR value of NO2 reached to maximum with lag4，and the RR value of O3 reached to maximum with lag 0~1. We estimated that a 10 μg/m3 increase in day-before NO2 daily average concentration was associated with a 32.745% （95% Confidence Interval（CI）：11.586%-57.916%）excess risk（ER）of daily new cases of COVID-19. And NO2 had a significant impact on daily new cases of COVID-19. When NO2 was introduced to PM2.5and PM10 separately，for every 10 μg/m3 rise in NO2 daily average concentration，the ER of daily new cases of COVID-19 was 23.929%（95% CI：4.705%-46.682%）and 24.672%（95% CI：5.379%-47.496%），respectively. The study showed that the southeast was the main spread direction in the early stage of COVID-19 outbreak in the Chinese Mainland in 2020. Reducing the atmospheric concentration of nitrogen dioxide in epidemic hot spots has a positive effect on epidemic prevention and control. © 2022 Journal of Zhongshan University. All rights reserved.
ABSTRACT
It is of great significance to explore the spatial-temporal characteristics and analyze the driving factors of the diffusion of smart tourism city policy, which promotes the adoption of smart tourism city policy and the sustainable development of tourism. We aimed to explore the diffusion law and influencing factors of smart tourism city so as to provide reference for the construction of smart tourism city. By employing the 249 cases in China from 2012 to 2019, we revealed the spatial-temporal characteristics and driving factors influencing the diffusion of smart tourism city policy by employing the event history analysis method. The results reveal that the diffusion of smart tourism city policy presents the typical S-shaped curve in cumulative adoptions over time. Furthermore, the diffusion of smart tourism city policy presents the spatial distribution characteristic of the Hu Line, which spreads from the eastern coastal areas to the central inland areas. Moreover, there are multiple driving sources for the diffusion of smart tourism city policy, among which economic lift force, intellectual support force, technological pull force and demand impetus force are the important driving sources for the policy diffusion.
ABSTRACT
COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial(Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.