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

Wang, S., Yang, X., Li, L. ORCID: 0000-0002-4026-0216 , Nadler, P, Arcucci, R, Huang, Y, Teng, Z & Guo, Y (2020). A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19. IEEE Computational Intelligence Magazine, 15(4), pp. 23-33. doi: 10.1109/mci.2020.3019874


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.

Publication Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: COVID-19, Data assimilation, Bayesian updating, Renewal process, Epidemiology, Non-pharmaceutical intervention
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HN Social history and conditions. Social problems. Social reform
Q Science > QR Microbiology > QR180 Immunology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments: School of Science & Technology > Engineering
SWORD Depositor:
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