During the global COVID-19 pandemic, densely populated megacities engaged in active international exchanges have faced the most severe impacts from both the disease and the associated infodemic. This study examines the factors influencing public participation behavior on government microblogs in these megacities during the pandemic. It guides megacities in disseminating epidemic information, promoting knowledge on epidemic prevention, managing public opinion, and addressing related matters.
Utilizing the elaboration likelihood model\'s central and peripheral routes, drawing on an empirical analysis of 6,677 epidemic-related microblogs from seven Chinese megacities, this study analyses the influence mechanisms influencing public participation behavior and reveals the regulatory role of confirmed case numbers. Meanwhile,a qualitative comparative analysis examines and discusses diferent confgurations of ixn fuential factors.
The study reveals that microblog content richness demonstrates a U-shaped impact on public participation behavior. Conversely, content interaction, content length, and the number of fans positively impact participation, while update frequency has a negative impact. Additionally, the number of new confrmed cases positively regulates the impact of microblog content and publisher characteristics on public participation behavior. Public participation behavior also varies based on publishing time and content semantic features. This study further revealed the different confgurations of influential factors by QCA method.
This study reveals the impact mechanism of the microblog content and publisher characteristics on public participation behavior. It also demonstrates the regulatory role of newly confrmed cases in the way content and publishers\' characteristics influence public participation behavior. This study is of great significance for the operation of government microblogs, the release of emergency information, and the promotion of public participation.

This study applies OLS, panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019 (Covid-19) outbreak on the global equity markets during the early stage of the pandemic. We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1% significance level. Furthermore, the pandemic has a more significant impact on the European countries than on the East Asian economies. The results have three main implications. Firstly, policy makers should react fast to mitigate the impact of a crisis. Secondly, investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly. Furthermore, the Covid-19 outbreak results in a shift of power from the west to the east.

The COVID-19 has been a worldwide pandemic and it needs for studies related to effect on people\'s demand for insurance during the pandemic which is an important way to transfer risk. However, there is a lack of research linking COVID-19 and people\'s demand for insurance. The objective of this paper is to investigate the impact of COVID-19 pandemic on issuance demand, using data covering 241 cities on confirmed COVID-19 cases and insurance company revenue in China. The empirical results show that more confirmed COVID-19 cases are associated with greater per capita insurance revenue and the results are robust when considering endogeneity concern. Economically, the per capita insurance revenue increases by 0.896 Yuan for each more confirmed case. In terms of insurance type, the greatest increased insurance revenue is for life insurance, followed by health insurance. We further consider the heterogeneity of regions and find that the impact of COVID-19 on insurance revenue only exists in regions with worse medical treatment conditions or higher medical burden.

Background: This article studies the relationship between the COVID-19 epidemic, public sentiment, and the volatility of infectious disease equities from the perspective of the United States. We use weekly data from January 3, 2020 to March 7, 2021. This provides a sufficient dataset for empirical analysis. Granger causality test results prove the two-way relationship between the fluctuation of infectious disease equities and confirmed cases. In addition, confirmed cases will cause the public to search for COVID-19 tests, and COVID-19 tests will also cause fluctuations in infectious disease equities, but there is no reverse correlation. The results of this research are useful to investors and policy makers. Investors can use the number of confirmed cases to predict the volatility of infectious disease equities. Similarly, policy makers can use the intervention of retrieved information to stabilize public sentiment and equity market fluctuations, and integrate a variety of information to make more scientific judgments on the trends of the epidemic.

A novel coronavirus (COVID-19) has caused viral pneumonia worldwide, posing a major threat to international health. Our study reports that city lockdown is an effective way to reduce the number of new cases and the nitrogen dioxide (NO2) concentration can be used as an environmental lockdown indicator to evaluate the effectiveness of lockdown measures. The airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas since the lockdown. The total number of newly confirmed cases reached an inflection point about two weeks since the lockdown and could be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly, and at the same time, the reduction rate of NO2 concentration will increase. Our research results show that NO2 satellite observations can help decision makers effectively monitor and manage non-pharmaceutical interventions in the epidemic.

Using a monthly panel data of 13 Chinese provinces (cities) over the period from December 2019 to August 2020, this research investigates the impact of COVID-19 on the freight transport. We find that COVID-19 has a positive impact on the road freight transport turnover. This effect is pronounced under the higher numbers of COVID-19 confirmed cases and the lower level of gasoline production, and vice versa. In brief,•This study finds that COVID-19 has a positive impact on the road freight transport turnover.•This effect is pronounced under the higher numbers of COVID-19 confirmed cases and the lower level of gasoline production, and vice versa.

BACKGROUND: Since December 2019, the coronavirus disease 2019 (COVID-19) has infected more than 12,322,000 people and killed over 556,000 people worldwide. However, Differential diagnosis remains difficult for suspected cases of COVID-19 and need to be improved to reduce misdiagnosis.
METHODS: Sixty-eight cases of suspected COVID-19 treated in Wenzhou Central Hospital from January 21 to February 20, 2020 were divided into confirmed and COVID-19-negative groups based on the results of real-time reverse transcriptase polymerase chain reaction (RT-PCR) nucleic acid testing of the novel coronavirus in throat swab specimens to compare the clinical symptoms and laboratory and imaging results between the groups.
RESULTS: Among suspected patients, 17 were confirmed to COVID-19-positive group and 51 were distinguished to COVID-19-negative group. Patients with reduced white blood cell (WBC) count were more common in the COVID-19-positive group than in the COVID-19-negative group (29.4% vs 3.9%, P = 0.003). Subsequently, correlation analysis indicated that there was a significant inverse correlation existed between WBC count and temperature in the COVID-19-positive patients (r = - 0.587, P = 0.003), instead of the COVID-19-negative group. But reduced lymphocyte count was no different between the two groups (47.1% vs 25.5%, P = 0.096). More common chest imaging characteristics of the confirmed COVID-19 cases by high-resolution computed tomography (HRCT) included ground-glass opacities (GGOs), multiple patchy shadows, and consolidation with bilateral involvement than COVID-19-negative group (82.4% vs 31.4%, P = 0.0002; 41.2% vs 17.6% vs P = 0.048; 76.5% vs 43.1%, P = 0.017; respectively). The rate of clustered infection was higher in COVID-19-positive group than COVID-19-negative group (64.7% vs 7.8%, P = 0.001). Through multiplex PCR nucleic acid testing, 2 cases of influenza A, 3 cases of influenza B, 2 cases of adenovirus, 2 cases of Chlamydia pneumonia, and 7 cases of Mycoplasma pneumoniae were diagnosed in the COVID-19-negative group.
CONCLUSIONS: WBC count inversely correlated with the severity of fever, GGOs, multiple patchy shadows, and consolidation in chest HRCT and clustered infection are common but not specific features in the confirmed COVID-19 group. Multiplex PCR nucleic acid testing helped differential diagnosis for suspected COVID-19 cases.