dynamical seir model with information entropy using covid 19 as a case study CORD-Papers-2021-10-25 (Version 1)

Title: Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study
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 IEEE
Published: 2021
Journal: IEEE Transactions on Computational Social Systems
Author Name: Nie, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nie_q
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Zhang, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_d
Author Name: Jiang, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jiang_h
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1050323
has_full_text: FALSE
G_ID: dynamical_seir_model_with_information_entropy_using_covid_19_as_a_case_study
S2 ID: 234338215