Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study

Abstract

Background: Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK.

Methods: We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66·4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions, as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions, and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R₀.

Findings: We projected a median unmitigated burden of 23 million (95% prediction interval 13-30) clinical cases and 350 000 deaths (170 000-480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R₀, but not sufficiently to prevent ICU demand from exceeding health service capacity. The combined intervention was more effective at reducing R₀, but only lockdown periods were sufficient to bring R₀ near or below 1; the most stringent lockdown scenario resulted in a projected 120 000 cases (46 000-700 000) and 50 000 deaths (9300-160 000). Intensive interventions with lockdown periods would need to be in place for a large proportion of the coming year to prevent health-care demand exceeding availability.

Interpretation: The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.

Funding: Medical Research Council.

Figure 1

State transitions in the model Individuals in the stochastic compartmental model are classified into susceptible, exposed, infectious (preclinical, clinical, or subclinical), and recovered states (ie, removed from the model). The model is stratified into 5-year age bands and epidemics are simulated in the 186 county-level administrative units of the UK.

Figure 2

Impact of interventions lasting 12 weeks (A) Daily incidence of new cases and prevalence of ICU beds required over the course of the simulated scenarios in the UK, from February to October, 2020. Divisions on the x-axis show the beginning of each calendar month. From 200 realisations of each projection, 11 representative simulations are shown: one for each decile of the total number of cases, with the bold curve showing the simulation resulting in the median projected number of cases. Tall blue shaded regions show scheduled school holiday closures, and pink shaded regions show the distribution of 12-week interventions. (B) Summary of simulated outputs in total number of clinical cases and deaths, clinical cases in the peak week, peak ICU beds required, peak non-ICU beds required, and the time from seeding until the peak of the epidemic. Vertical bars indicate 95% prediction intervals. (C) Estimated distribution of the basic reproduction number, R₀, under each intervention scenario, sampled across all counties and model runs for each scenario. ICU=intensive care unit.

Figure 3

Local versus national triggering and timing of interventions (A) Dynamics of the epidemic under local versus national triggers for introduction of the combined intervention (pink shaded regions). Tall blue shaded regions show regular school holiday closures whereas the pink shaded region shows the intervention period. From 200 realisations of each projection, 11 representative simulations are shown: one for each decile of the total number of new cases, with the bold curve showing the simulation resulting in the median projected daily incidence of cases. (B) Summary of simulated outputs in total number of clinical cases and deaths, clinical cases in the peak week, peak ICU beds required, peak non-ICU beds required, and the time from seeding until the peak of the epidemic. Vertical bars indicate 95% prediction intervals. (C) Illustration of peak timings of new cases varying across two counties in the UK, in comparison with predicted national trends, for a single simulation with no control interventions. Blue shaded regions show regular school holiday closures. Divisions on the x-axis in panels A and C show the beginning of each calendar month. ICU=intensive care unit.

Figure 4

Projected impact of intensive control measures with reactive lockdowns (A) Dynamics of the epidemic under different triggers for introduction and lifting of lockdowns (median timing of lockdowns shown as low grey shaded areas). Divisions on the x-axis show the beginning of each calendar month. From 200 realisations of each projection, 11 representative simulations are shown: one for each decile of the total number of ICU beds required, with the bold curve showing the simulation resulting in the median projected ICU bed requirement. Horizontal guides show the estimated number of ICU beds in the UK as of January, 2020 (solid line), and with a hypothetical doubling of capacity (dashed line). Tall blue shaded regions show school closures whereas the pink shaded region shows a background period of intensive interventions. Dynamics are shown up to April, 2021 (intensive interventions) or September, 2021 (lockdown scenarios), but all scenarios were modelled up to the end of December, 2021. (B) Summary of simulated outputs in total number of clinical cases and deaths, clinical cases in the peak week, peak ICU beds required, peak non-ICU beds required, and the time from seeding until the peak of the epidemic. Vertical bars indicate 95% prediction intervals. (C) Estimated distribution of the basic reproduction number, R₀, under three different interventions—intensive physical distancing with schools open and closed, and lockdown—sampled across all counties and model runs for each scenario. ICU=intensive care unit.