Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Abstract

Objectives The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China from January to October 2020. Methods About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. Results The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and obvious clustering regions. spatio-temporal scanning analysis showed that most COVID-19 high incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR = 491.57), which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. Conclusions Environmental temperature has a certain inhibitory effect on the transmission of COVID-19, and the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.

Dynamic impact of negative public sentiment on agricultural product prices during COVID-19

“…This was because the high-risk and medium-risk areas were the first to be affected by the pandemic ( Li et al, 2021 ) and the continuous fermentation of the online negative sentiment caused a public panic, which led to panic buying behaviors. However, as there were no confirmed COVID-19 cases in the low-risk areas in the early pandemic stages ( Wang et al, 2021 ), the public was less susceptible to the online negative sentiment and tended to follow their normal purchasing behaviors. As the pandemic became more serious, the low-risk area public began to panic, fearing there could be shortage of supplies in the future, which resulted in bulk purchasing behaviors and a continual rise in the agricultural product prices.…”

“…Therefore, the public in the medium-risk areas were more likely to be affected by the public online negative sentiment and to adopt irrational panic buying behaviors, which in turn resulted in a sustained and greater impact on prices. As the low-risk areas were less affected by the pandemic, the public was less affected by the online negative sentiments ( Wang et al, 2021 ), which meant that the agricultural product price responses to the public online negative sentiments were lower than in the medium-risk areas. Life cycle impulse responses …”

“…The agricultural product price data for the different risk areas was then obtained. First, the high, medium, and low epidemic risk areas were classified based on the COVID-19 risk area classification criteria ( Wang et al, 2020 , 2021 ). The provinces with high-risk cities were classified as high-risk areas, those with medium-risk cities were classified as medium-risk areas, and those with low-risk cities were classified as low-risk areas.…”

Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

“…But the other studies also showed similar results For instance, Han et al [ 20 ] have found the trend change characteristics of Moran’s I index slightly raised from 0.007 to 0.013 in Beijing, China. Another study by Wang et al [ 21 ] also revealed the trend of Moran’s I index values in 31 provincial-level areas of China slightly increased from 0.01 to 0.17. Based on this comparison, we estimated that the average of the Moran’s I index of the number of COVID-19 cases just slightly increased all over the world.…”

Online Public Attention Toward Premature Ejaculation in Mainland China: Infodemiology Study Using the Baidu Index

“…The inquiry frequency and concerning problems from the users have been well documented by internet platforms and their demographic data. Using data from these internet platforms, infodemiology research has been successfully practiced in reporting disease incidence [ 7 , 8 ], surveilling pandemic outbreaks [ 9 , 10 ], and analyzing other public health events and related public awareness [ 11 , 12 ]. Previously, public interest and the change over time of the search volume in sexual dysfunction were analyzed [ 13 , 14 ].…”