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

PLoS Comput Biol. 2021 Jan 21;17(1):e1008604. doi: 10.1371/journal.pcbi.1008604. eCollection 2021 Jan.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / epidemiology
  • COVID-19 / prevention & control*
  • COVID-19 / transmission
  • Communicable Disease Control / methods*
  • Communicable Disease Control / statistics & numerical data*
  • Computational Biology
  • Humans
  • Models, Statistical*
  • SARS-CoV-2
  • Software

Grants and funding

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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.