Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25734, first published .
Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study

Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study

Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study

Authors of this article:

Fulian Yin1 Author Orcid Image ;   Xueying Shao1 Author Orcid Image ;   Meiqi Ji1 Author Orcid Image ;   Jianhong Wu2 Author Orcid Image

Journals

  1. Kang S, Hou X, Hu Y, Liu H. Dynamic analysis and optimal control considering cross transmission and variation of information. Scientific Reports 2022;12(1) View
  2. Ogbuokiri B, Ahmadi A, Bragazzi N, Movahedi Nia Z, Mellado B, Wu J, Orbinski J, Asgary A, Kong J. Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts. Frontiers in Public Health 2022;10 View
  3. Sooknanan J, Seemungal T. FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models. International Journal of Dynamics and Control 2023;11(2):892 View
  4. Geiß D, Kroy K, Holubec V. Signal propagation and linear response in the delay Vicsek model. Physical Review E 2022;106(5) View
  5. Yin F, She Y, Pan Y, Tang X, Hou H, Wu J. Hot-topics cross-propagation and opinion-transfer dynamics in the Chinese Sina-microblog social media: A modeling study. Journal of Theoretical Biology 2023;566:111480 View
  6. Yi F, Li X, Yu S, Zhang Q. Time matters in pandemic risk communication: A moderated effect of information timeliness on stakeholder perception in Singapore. Risk Analysis 2024;44(5):1254 View
  7. Yin F, Jiang X, Wang J, Guo Y, Wu Y, Wu J. Revealing the sentiment propagation under the conscious emotional contagion mechanism in the social media ecosystem: For public opinion management. Physica D: Nonlinear Phenomena 2024;469:134327 View
  8. Deng 邓 Y, Hu 胡 Z, Lin 林 F, Tang 唐 C, Wang 王 H, Huang 黄 Y. Identify information sources with different start times in complex networks based on sparse observers. Chinese Physics B 2024;33(11):118901 View