Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

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.

Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest

“…Based on statistical analysis of the daily time series of meteorological variables, we found that air temperature and surface solar irradiance are inversely correlated (Figure 5) with the confirmed COVID-19 daily new cases (DNCs, r = −0.51, p < 0.01; and r = −0.60; p < 0.01) and deaths (DNDs, r = −0.67, p < 0.01; r = −0.65, p < 0.01), and respectively; the results are comparable with the scientific literature in the field [96], [97], [98].…”

“…Based on statistical analysis of the daily time series of meteorological variables, we found that air temperature and surface solar irradiance are inversely correlated (Figure 5) with the confirmed COVID-19 daily new cases (DNCs, r = −0.51, p < 0.01; and r = −0.60; p < 0.01) and deaths (DNDs, r = −0.67, p < 0.01; r = −0.65, p < 0.01), and respectively; the results are comparable with the scientific literature in the field [96][97][98]. Similar results have been reported by some previous studies, which demonstrated an association of weather factors (mostly air temperature, humidity, solar radiation) and COVID-19 transmission in specific regions of the world during different time windows [109], [110].…”

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

“…Like other studies ( Ho et al, 2021 , Bu et al, 2021 , Fang et al, 2021 ), this research demonstrates the high negative impact of outdoor exposure to high ground levels of air pollutants recorded in Bucharest metropolitan city on COVID-19 pandemic incidence and deaths and suggests the urgent need for reduction of air pollutants sources. Lockdown implementation actions are welcome during strong pandemic waves causing air quality improvement ( Rahman et al, 2021 , Han et al, 2023 , Orak and Ozdemir, 2021 , Jin et al, 2021 ).…”

Analysis of Rainstorm Process over Henan Province of China in July 2021

“…Gao et al (2020) summarized the research on torrential rain in North China in their research progress on the formation mechanism and forecasting methods of heavy rain in China. In response to the heavy rain in North China, Chinese scholars have also carried out research on water vapor conditions, terrain, the underlying surface, and the urban heat island effect (Lei et al, 2020;Ji & Li, 2021;Zhou et al, 2020;. They pointed out that the rainstorms in North China generally occurred under favorable large-scale background conditions, and the rainstorm vapors in North China were mainly contributed by the western Pacific and the South China Sea; the formation of rainstorms was also closely related to the high-and low-level jets.…”