Analyzing Social Distancing and Seasonality of COVID-19 with Mean Field Evolutionary Dynamics

Hao Gao, Wuchen Li, Miao Pan, Zhu Han, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The outbreak of the coronavirus pandemic since the end of 2019 has been declared as a world health emergency by the World Health organization, which raised the importance of an accurate mathematical epidemiological dynamic model to predict the evolution of COVID-19. Replicator dynamics (RDs) are exclusively applied to many epidemic models, but they fail to satisfy the Nash stationarity and can only describe a unidirectional population flow between different states. In this paper, we proposed mean field evolutionary dynamics (MFEDs), inspired by the optimal transport theory and mean field games on graphs, to model epidemic dynamics. We compare the MFEDs with RDs theoretically. In particular, we also show the efficiency of MFEDs by modeling the evolution of COVID-19 in Wuhan, China. Furthermore, we analyze the effect of one-time social distancing as well as the seasonality of COVID-19 through the post-pandemic period.

Original languageEnglish (US)
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
StatePublished - Dec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei
Period12/7/2012/11/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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