ArticleIEEE transactions on computational social systems2021
Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study.
Article in IEEE transactions on computational social systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review.International journal of environmental research and public health · 2021Pooled it
- Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.Frontiers in public health · 2024Article
- STROVE: spatial data infrastructure enabled cloud-fog-edge computing framework for combating COVID-19 pandemic.Innovations in systems and software engineering · 2022Article
- Evaluating the mitigation strategies of COVID-19 by the application of the COEnvironmental research · 2022Article
- A class of delay SIQR-V models considering quarantine and vaccination: Validation based on the COVID-19 perspective.Results in physics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Social network information is a measure of the number of infections. Understanding the effect of social network information on disease spread can help improve epidemic forecasting and uncover preventive measures. Many driving factors for the transmission mechanism of infectious diseases remain unclear. Some experts believe that redundant information on social media may increase people's panic to evade the restrictions or refuse to report their symptoms, which increases the actual infection rate. We analyze the engagement in the COVID-19 topics on the Internet and find that the infection rate is not only related to the total amount of information. In our research, information entropy is introduced into the quantification of the impact of social network information. We find that the amount of information with different distributions has different effects on disease transmission. Furthermore, we build a new dynamic susceptible-exposed-infected-recovered (SEIR) model with information entropy to simulate the epidemic situation in China. Simulation results show that our modified model is effective in predicting the COVID-19 epidemic peaks and sizes.
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