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

Swapnil Mishra*, James A. Scott, Daniel J. Laydon, Seth Flaxman, Axel Gandy, Thomas A. Mellan, H. Juliette T. Unwin, Michaela Vollmer, Helen Coupland, Oliver Ratmann, Melodie Monod, Harrison H. Zhu, Anne Cori, Katy A.M. Gaythorpe, Lilith K. Whittles, Charles Whittaker, Christl A. Donnelly, Neil M. Ferguson, Samir Bhatt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number16342
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2021
Externally publishedYes

Bibliographical note

Funding Information:
We would like to thank the COVID-19 Hospitalisation in England Surveillance System (CHESS) team at Public Health England for their work in collecting the onset-to-death delay data used here. We would like to thank a generous donation of compute time on the Azure cloud from Microsoft that facilitated this analysis. DJL, NMF acknowledge funding from Vaccine Efficacy Evaluation for Priority Emerging Diseases (VEEPED) grant, (ref. NIHR: PR-OD-1017-20002) from the National Institute for Health Research. SB would like to acknowledge the NIHR BRC Imperial College NHS Trust Infection and COVID themes the Academy of Medical Sciences Spring-board award and the Bill and Melinda Gates Foundation. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Foreign, Commonwealth and Development Office, the NIHR Health Protection Research Unit in Modelling and Health Economics, the NIHR Health Protection Research Unit in Emerging and Zoonotic Infections and by a philanthropic grant from Community Jameel.

Publisher Copyright:
© 2021, The Author(s).


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