How and When to End the Covid-19 Lockdown

An Optimization Approach

Bibliographic Data

ID22080392
AuthorsThomas Rawson (0000-0001-8182-4279, University of Oxford, corresponding author), Tom Brewer (0000-0001-7227-2150, University of Oxford), Dessislava Veltcheva (0000-0002-5846-5543, University of Oxford), Chris Huntingford (0000-0002-5941-7770, UK Centre for Ecology & Hydrology), Michael B Bonsall (0000-0003-0250-0423, University of Oxford, corresponding author)
Year2020
Volume8
Pages262-262
Publication date2020-06-10
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2020.00262
PMID32587844
OpenAlexW3035395932
LanguageEN
Citations received11
References cited29

Countries around the world are in a state of lockdown to help limit the spread of SARS-CoV-2. However, as the number of new daily confirmed cases begins to decrease, governments must decide how to release their populations from quarantine as efficiently as possible without overwhelming their health services. We applied an optimal control framework to an adapted Susceptible-Exposure-Infection-Recovery (SEIR) model framework to investigate the efficacy of two potential lockdown release strategies, focusing on the UK population as a test case. To limit recurrent spread, we find that ending quarantine for the entire population simultaneously is a high-risk strategy, and that a gradual re-integration approach would be more reliable. Furthermore, to increase the number of people that can be first released, lockdown should not be ended until the number of new daily confirmed cases reaches a sufficiently low threshold. We model a gradual release strategy by allowing different fractions of those in lockdown to re-enter the working non-quarantined population. Mathematical optimization methods, combined with our adapted SEIR model, determine how to maximize those working while preventing the health service from being overwhelmed. The optimal strategy is broadly found to be to release approximately half the population 2-4 weeks from the end of an initial infection peak, then wait another 3-4 months to allow for a second peak before releasing everyone else. We also modeled an "on-off" strategy, of releasing everyone, but re-establishing lockdown if infections become too high. We conclude that the worst-case scenario of a gradual release is more manageable than the worst-case scenario of an on-off strategy, and caution against lockdown-release strategies based on a threshold-dependent on-off mechanism. The two quantities most critical in determining the optimal solution are transmission rate and the recovery rate, where the latter is defined as the fraction of infected people in any given day that then become classed as recovered. We suggest that the accurate identification of these values is of particular importance to the ongoing monitoring of the pandemic

2019-20 coronavirus outbreak · Outbreak · Pathology · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Medicine · SARS-CoV-2 and COVID-19 Research · Virology

Tools

Open DOIOpen Access