Brunner, Franziska S, Payne, Alexander, Cairns, Edward, Airey, George, Gregory, Richard, Pickwell, Natalie D, Wilson, Myles, Carlile, Matthew, Holmes, Nadine, Hill, Verity et al (show 13 more authors)
(2023)
Wastewater genomic surveillance tracks the spread of the SARS-CoV-2 Omicron variant across England
[Preprint]
Abstract
AbstractBackgroundMany countries have moved into a new stage of managing the SARS-CoV-2 pandemic with minimal restrictions and reduced testing in the population, leading to reduced genomic surveillance of virus variants in individuals. Wastewater-based epidemiology (WBE) can provide an alternative means of tracking virus variants in the population but is lacking verifications of its comparability to individual testing data.MethodsWe analysed more than 19,000 samples from 524 wastewater sites across England at least twice a week between November 2021 and February 2022, capturing sewage from >70% of the English population. We used amplicon-based sequencing and the phylogeny based de-mixing tool Freyja to estimate SARS-CoV-2 variant frequencies and compared these to the variant dynamics observed in individual testing data from clinical and community settings.FindingsWe show that wastewater data can reconstruct the spread of the Omicron variant across England since November 2021 in close detail and aligns closely with epidemiological estimates from individual testing data. We also show the temporal and spatial spread of Omicron within London. Our wastewater data further reliably track the transition between Omicron subvariants BA1 and BA2 in February 2022 at regional and national levels.InterpretationOur demonstration that WBE can track the fast-paced dynamics of SARS-CoV-2 variant frequencies at a national scale and closely match individual testing data in time shows that WBE can reliably fill the monitoring gap left by reduced individual testing in a more affordable way.FundingDepartment of Health and Social Care, UK, Natural Environmental Research Council, UK, COG-UKResearch in contextEvidence before this studyGenomic monitoring of wastewater for SARS-CoV-2 variants has been introduced in several countries and shown to effectively detect the spread of known variants in multiple studies. However, verification of its alignment with individual testing data at a national scale has so far been reported only for Austria, where sampling covered around 5.4million people. Further and larger scale verifications of the reliability of wastewater-based epidemiology (WBE) are needed to increase confidence in its use for public health monitoring.Added value of this studyWe provide evidence that WBE was able to closely track the spread of the emerging SARS-CoV-2 variant Omicron, as well as its sub lineage dynamics, at a regional and national scale across England. Our sampling covered >70% of the English population, equivalent to 39.4 million people. We thereby demonstrate the scalability of our approach to national levels. We also show how WBE is able to track dynamics in different regions of the UK and at a finer scale within London. Its close alignment, in estimated epidemiological timings, with results from intensive individual testing in the same timeframe provides evidence that wastewater-based monitoring can be a reliable alternative when large scale data from individual testing is not available.Implications of all the available evidenceAltogether, evidence is accumulating that WBE is a reliable approach for monitoring SARS-CoV-2 variant dynamics and informing public health measures across spatial scales.
| Item Type: | Preprint |
|---|---|
| Uncontrolled Keywords: | 3207 Medical Microbiology, 32 Biomedical and Clinical Sciences, 42 Health Sciences, Infectious Diseases, Emerging Infectious Diseases, Coronaviruses, 2.5 Research design and methodologies (aetiology), 2.4 Surveillance and distribution, 3 Good Health and Well Being |
| Divisions: | Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Infection, Vet & Ecological Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Feb 2023 16:01 |
| Last Modified: | 08 Nov 2025 18:45 |
| DOI: | 10.1101/2023.02.15.23285942 |
| Open Access URL: | https://medrxiv.org/cgi/content/short/2023.02.15.2... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3168500 |
Altmetric
Altmetric