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Using Genomic Concordance to Estimate COVID-19 Transmission Risk Across Different Community Settings in England 2020/21

24 Pages Posted: 15 Jun 2021

See all articles by Cong Chen

Cong Chen

Government of the United Kingdom - Public Health England

Simon Packer

Government of the United Kingdom - Public Health England

Charlie Turner

Government of the United Kingdom - Public Health England

Charlotte Anderson

Government of the United Kingdom - Public Health England

Gareth Hughes

Government of the United Kingdom - Public Health England

Obaghe Edeghere

Government of the United Kingdom - Public Health England

Isabel Oliver

Government of the United Kingdom - Public Health England

Ewan Birney

European Bioinformatics Institute - European Molecular Biology Laboratory

More...

Abstract

Background: Identifying areas that pose the greatest risk for community transmission of COVID-19 is essential to direct public health action and allow safe re-opening of society. Spread of B.1.1.7 (alpha) lineage provided a unique opportunity to quantify COVID-19 transmission risk associated with community settings in England 2020/21. 

Methods: All cases of COVID-19 occurring between 11/2020 and 01/2021 reported through the English national contact tracing system included. Recruitment occurred when B.1.1.7 regional prevalence was between 20-80%. Case groups were defined as: >2 cases reporting the same, location and attendance date 7-3 days before onset. Genetic concordance, presence/absence of S-gene target failure (SGTF) in grouped cases, was determined. Odds ratios for concordance and 95% confidence intervals were calculated. Sensitivity analysis compared concordance in single to 2-3 day case groups. 

Findings:  There were 41,325 case groups with SGTF data containing 115,410 exposure events. Odds ratios ranged from 1.87 (95% CI:1.76-1.98) for shops, 29.9 (95% CI:23.1-38.7), nursery/preschool and 35.6 (95% CI:19.7-64.2) for visiting friends/relatives.  Odds ratios of concordance increased with larger cluster sizes in educational settings. Concordance estimates were reduced when case grouping time period was increased from 1 to 2-3 days. 

Interpretation: Transmission risk varies across community settings, likely due to different behavioural or environmental factors. Risk does not capture number of users which also affects impact of settings on transmission. Limited data for certain settings due to non-pharmaceutical interventions in place. We recommend data are used to guide policy and prioritise action when assessing and managing COVID-19 community case clusters. 

Funding:  EB funded by EMBL. No additional funding.

Declaration of Interest: None to declare

Suggested Citation

Chen, Cong and Packer, Simon and Turner, Charlie and Anderson, Charlotte and Hughes, Gareth and Edeghere, Obaghe and Oliver, Isabel and Birney, Ewan, Using Genomic Concordance to Estimate COVID-19 Transmission Risk Across Different Community Settings in England 2020/21. Available at SSRN: https://ssrn.com/abstract=3867682 or http://dx.doi.org/10.2139/ssrn.3867682

Cong Chen

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Simon Packer (Contact Author)

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Charlie Turner

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Charlotte Anderson

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Gareth Hughes

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Obaghe Edeghere

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Isabel Oliver

Government of the United Kingdom - Public Health England

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Ewan Birney

European Bioinformatics Institute - European Molecular Biology Laboratory

Hinxton
United Kingdom

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