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

Wang, Shuo, Yang, Xian, Li, Ling, Nadler, Philip, Arcucci, Rossella, Huang, Yuan, Teng, Zhongzhao, Guo, Yike (2020) A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19. IEEE Computational Intelligence Magazine, 15 (4). pp. 23-33. ISSN 1556-603X. E-ISSN 1556-6048. (doi:10.1109/MCI.2020.3019874) (KAR id:82582)

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http://dx.doi.org/10.1109/MCI.2020.3019874

Abstract

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 R_t 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.

Item Type: Article
DOI/Identification number: 10.1109/MCI.2020.3019874
Uncontrolled keywords: COVID-19, Data assimilation, Bayesian updating, Renewal process, Epidemiology, Non-pharmaceutical intervention
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 24 Aug 2020 14:02 UTC
Last Modified: 16 Feb 2021 14:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82582 (The current URI for this page, for reference purposes)
Li, Ling: https://orcid.org/0000-0002-4026-0216
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