Dataset Information

The Effect of Emotion on Prosocial Tendency: The Moderating Effect of Epidemic Severity Under the Outbreak of COVID-19.

ABSTRACT: During the outbreak of COVID-19, information on the epidemic inundated people's lives and led to negative emotions (e.g., tension, anxiety, and fear) in many people. This study aims to explore the effect of various emotions on prosocial tendencies during the COVID-19 outbreak and the moderating effect of the severity of the epidemic. We explore these effects by conducting a text analysis of the content of posts by 387,730 Weibo users. The results show that the severity of the epidemic promotes prosocial tendencies; anger motivates prosocial tendencies significantly; and the severity of the epidemic moderates the effects of three emotions-anger, sadness, and surprise-on prosocial tendencies. These findings provide a reference for exploring the positive significance of major disasters.

SUBMITTER: Ye Y

PROVIDER: S-EPMC7779789 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Effect of Emotion on Prosocial Tendency: The Moderating Effect of Epidemic Severity Under the Outbreak of COVID-19.

Ye Yingying Y Long Tingting T Liu Cuizhen C Xu Dan D

Frontiers in psychology 20201221

During the outbreak of COVID-19, information on the epidemic inundated people's lives and led to negative emotions (e.g., tension, anxiety, and fear) in many people. This study aims to explore the effect of various emotions on prosocial tendencies during the COVID-19 outbreak and the moderating effect of the severity of the epidemic. We explore these effects by conducting a text analysis of the content of posts by 387,730 Weibo users. The results show that the severity of the epidemic promotes p ...[more]

PMID: 33408665

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Project description:BackgroundThe coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures.MethodsWe develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models.ResultsThe SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R > 1, and after January 30, all regions had a reproductive number R < 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty.ConclusionsWe suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.Supplementary materialsThe supplementary materials can be found online with this article at 10.1007/s40484-020-0199-0.

Dataset Information

The Effect of Emotion on Prosocial Tendency: The Moderating Effect of Epidemic Severity Under the Outbreak of COVID-19.

Publications

The Effect of Emotion on Prosocial Tendency: The Moderating Effect of Epidemic Severity Under the Outbreak of COVID-19.

Similar Datasets

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