Precautionary Breaks: Planned, Limited Duration Circuit Breaks to Control the Prevalence of COVID-19

Abstract

Background: The COVID-19 pandemic in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (early March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities, that slowed during the summer as control measures were relaxed. Since August, infections, hospitalisations and deaths have been rising (precise estimation of the current growth rate is difficult due to extreme regional heterogeneity and temporal lags between the different epidemiological observations) and various NPIs have been applied locally throughout the UK in response.Methods: We investigate the impact of planned, limited duration periods of strict NPIs using simple models of exponential growth or decline and more complex age-structured models matched to a variety of data sources. We simulate the impact of two weeks of sustained control against a variety of intrinsic growth rates. We consider the impact of these breaks on the prevalence of infection, as well as the total number of predicted hospitalisations and deaths.Findings: Precautionary breaks are characterised by both a relative decline in infection and the temporal reset - how far back in time since this reduced level of cases was previously observed. Precautionary breaks provide the biggest temporal gains when the growth rate is low, but offer a much needed reduction to increasing infection when the growth rate is higher. Although not a long-term solution, an intense two-week break can reduce the number of deaths or hospitalisations by around 50% in the short-term.Interpretation The planned nature, and finite duration, of precautionary breaks, may lessen their social and economic impact, while by driving infection to lower levels they may allow resource limited controls (such as test, trace and isolated) to be more effective.Funding Statement: This work has been supported by the Engineering and Physical Sciences Research Council through the MathSys CDT [grant number EP/S022244/1], the Medical Research Council through the COVID-19 Rapid Response Rolling Call [grant number MR/V009761/1], and the Biotechnology and Biological sciences Research Council through the Midlands Integrative Biosciences Training Partnership (MIBTP) [grant number BB/M01116X/1].Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Data from the CHESS database were supplied after anonymisation under strict data protection protocols agreed between the University of Warwick and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the Government’s SPI-M(O) / SAGE committees.