Lockdown as a last resort option in case of COVID-19 epidemic rebound: a modelling study
Year: 2021
Type: article
Abstract: BackgroundGiven its high economic and societal cost, policymakers might be reluctant to implement a large-scale lockdown in case of coronavirus disease (COVID-19) epidemic rebound. They may consider it as a last resort option if alternative control measures fail to reduce transmission.AimWe developed a modelling framework to ascertain the use of lockdown to ensure intensive care unit (ICU) capacity does not exceed a peak target defined by policymakers.MethodsWe used a deterministic compartmental model describing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the trajectories of COVID-19 patients in healthcare settings, accounting for age-specific mixing patterns and an increasing probability of severe outcomes with age. The framework is illustrated in the context of metropolitan France.ResultsThe daily incidence of ICU admissions and the number of occupied ICU beds are the most robust indicators to decide when a lockdown should be triggered. When the doubling time of hospitalisations estimated before lockdown is between 8 and 20 days, lockdown should be enforced when ICU admissions reach 3.0-3.7 and 7.8-9.5 per million for peak targets of 62 and 154 ICU beds per million (4,000 and 10,000 beds for metropolitan France), respectively. When implemented earlier, the lockdown duration required to get back below a desired level is also shorter.ConclusionsWe provide simple indicators and triggers to decide if and when a last-resort lockdown should be implemented to avoid saturation of ICU. These metrics can support the planning and real-time management of successive COVID-19 pandemic waves.
Source: Eurosurveillance
Institutions: Centre National de la Recherche Scientifique, Institut Pasteur, Sorbonne Université, Génomique évolutive, modélisation et santé, École des Hautes Études en Santé Publique, Université de Rennes, Santé Publique France, Inserm, Université Paris Cité, Infection, Anti-microbiens, Modélisation, Evolution, University of Cambridge, Institut Pierre Louis d‘Épidémiologie et de Santé Publique
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
Awards: G1979782309, Versatile Emerging infectious disease Observatory, G282427092, G434337789, Rapid European SARS-CoV-2 Emergency research Response, Institut Convergences pour l’étude de l’Emergence des Pathologies au Travers des Individus et des populatiONs, G7240651115, Integrative Biology of Emerging Infectious Diseases, G7916265299, G8670608220
OpenAlex ID: W3165316422