Genomic epidemiology of COVID-19 in care homes in the east of England

Abstract

COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1,167 residents from 337 care homes were identified from a dataset of 6,600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population.

The role of viral genomics in understanding COVID-19 outbreaks in long-term care facilities

“…Three studies defined a consistent method in their study to identify clusters. 18 , 19 , 27 These studies used Phylogenetic Assignment of Named Global Outbreak Lineages software or a more granular clustering algorithm in the COVID-19 Genomics UK (COG-UK) Consortium pipeline, 19 transcluster algorithm, 18 or single nucleotide polymorphism (SNP) distance. 27 One study provides a rationale for how clusters were identified without providing a defined criteria for selecting genomic clusters.…”

“…Furthermore, assuming a mutation rate of approximately 2·5 SNPs per month 19 allows for the estimation of the amount of variation expected in a phylogeny at any particular timepoint in a series. Additionally, only two studies mention the use of negative controls 18 , 19 and no studies have released the sequencing reads found in the negative controls publicly. Many sample preparation protocols use amplification techniques that can also amplify contamination and give false results, particularly when the viral load in the source material is low.…”

“…Many sample preparation protocols use amplification techniques that can also amplify contamination and give false results, particularly when the viral load in the source material is low. 18 , 19 , 20 , 25 Without having all underlying raw data (including controls) alongside the sample preparation and sequencing metadata, 34 reanalysis and comparison between studies is difficult and prone to error.…”

“…Many studies publicly release their raw sequencing data or consensus or assembled genomes, or a combination, 18 , 19 , 23 , 24 through the International Nucleotide Sequence Database Collaboration (INSDC) 35 and the Global Initiative on Sharing Avian Influenza Data (GISAID). 36 This sharing allows for independent reanalysis, overcoming the effect of variation among methods between studies.…”

Characterising within-hospital SARS-CoV-2 transmission events: a retrospective analysis integrating epidemiological and viral genomic data from a UK tertiary care setting across two pandemic waves

“…In the limited instances where viral genomic data were analysed, this information was used to confirm or complement a purely epidemiological approach. [6][7][8][9] Elucidating the source of transmission events on the basis of viral genetic relatedness alone also entails considerable uncertainty due to the slow evolutionary rate of SARS-CoV-2. [10] In the time scale of an outbreak, a large proportion of individuals are infected by viruses too genetically similar to each other to distinguish genuine transmission events from unrelated infections.…”