Post-lockdown abatement of COVID-19 by fast periodic switching

Bin, M. et al. (2021) Post-lockdown abatement of COVID-19 by fast periodic switching. PLoS Computational Biology, 17(1), e1008604. (doi: 10.1371/journal.pcbi.1008604) (PMID:33476332)

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COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at A software tool has also been developed so that interested parties can explore the proof-of-concept system.

Item Type:Articles
Additional Information:R.S., T.P., P.C. & R.M.-S. acknowledge support from EPSRC project EP/V018450/1. R.M.-S. and S.S. acknowledge funding support from EPSRC grant EP/R018634/1, "Closed-loop Data Science". M.B. and T.P. acknowledge funding support from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 739551 (KIOS CoE). H.L. and R.S. acknowledge the support of Science Foundation Ireland. P.F. acknowledges the support of IOTA Foundation (SFI grant 16/IA/4610). L.S. acknowledges support from the Australian Research Council (ARC) from Discovery Grant DP 170102303.
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Stein, Dr Sebastian
Creator Roles:
Murray-Smith, R.Investigation, Methodology
Stein, S.Investigation, Methodology
Authors: Bin, M., Cheung, P. Y.K., Crisostomi, E., Ferraro, P., Lhachemi, H., Murray-Smith, R., Myant, C., Parisini, T., Shorten, R., Stein, S., and Stone, L.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:PLoS Computational Biology
Publisher:Public Library of Science
ISSN (Online):1553-7358
Published Online:21 January 2021
Copyright Holders:Copyright © 2021 Bin et al.
First Published:First published in PLoS Computational Biology 17(1): e1008604
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science