Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling

Abstract

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others' policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country's first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.

Figure 1

Responses to the COVID-19 epidemic in the UK (red), Denmark (Blue) adn Sweden (Green). (a) Cumulative laboratory-confirmed COVID-19 deaths per million by date of death. (b) Results of YouGov surveys of population behavioural responses to COVID-19. The percentage of people: avoiding crowded public places (left); who report improved personal hygiene, e.g. frequent hand washing/using hand sanitiser (centre); avoiding going to work (right). (c) Google COVID-19 community mobility report data giving change from baseline in mobility associated with workplaces (left), and in residential mobility (i.e. time spent at home) (centre). Right plots the OxCGRT COVID-19 intervention stringency index measuring the number of COVID-19 policies present at any given time.

Figure 2

Median time-varying reproduction number over time ($R_t$) for Denmark (top block), Sweden (middle block) and the UK (lower block). Counterfactual $R_t$ profiles are in red and original fitted $R_t$ profiles are in black. For clarity, credible intervals are not shown, (see Figure S3 for uncertainty). Left column shows results for the absolute $R_t$ transposition approach, right column for the relative $R_t/R_0$ transposition approach.

Figure 3

Median daily infection incidence with $95\%$ credible interval for Denmark (top block), Sweden (middle block) and the UK (lower block). Left column shows results for the absolute $R_t$ transposition approach, middle column for the relative $R_t/R_0$ transposition approach, and right column shows the fit to observed data (i.e. without counterfactual swapping). Plots show y-axis on the same scale to highlight differences between scenarios.

Figure 4

As per Fig. 3 but for daily deaths (red curves with shaded 95% credible intervals). Observed deaths are shown as blue bars.