A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Shuo Wang, Xian Yang, Ling Li, Philip Nadler, Rossella Arcucci, Yuan Huang, Zhongzhao Teng, Yi-Ke Guo*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies of combatting the pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information to assess the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors to quantify intervention impacts at a finer granularity. A data assimilation framework is developed to estimate these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is built to quantify the impacts of intervention strategies by monitoring the evolution of the estimated parameters. We reveal the intervention impacts in European countries and Wuhan and the resurgence risk in the United States.

Original languageEnglish
Pages (from-to)23-33
Number of pages11
JournalIEEE Computational Intelligence Magazine
Issue number4
Publication statusPublished - Nov 2020

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence


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