An integrated national scale SARS-CoV-2 genomic surveillance network.

The Coronavirus Disease 2019 (COVID-19) Genomics UK Consortium (COG-UK) was launched in March, 2020, with £20 million support from UK Research and Innovation, the UK Department of Health and Social Care, and Wellcome Trust. The goal of this consortium is to sequence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for up to 230 000 patients, health-care workers, and other essential workers in the UK with COVID-19, which will help to enable the tracking of SARS-CoV-2 transmission, identify viral mutations, and integrate with health data to assess how the viral genome interacts with cofactors and consequences of COVID-19

Spatial Growth Rate of Emerging SARS-CoV-2 Lineages in England, September 2020–December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020-December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to routine assessments of the growth of emerging SARS-CoV-2 lineages in a defined population

Detecting SARS-CoV-2 variants with SNP genotyping.

Tracking genetic variations from positive SARS-CoV-2 samples yields crucial information about the number of variants circulating in an outbreak and the possible lines of transmission but sequencing every positive SARS-CoV-2 sample would be prohibitively costly for population-scale test and trace operations. Genotyping is a rapid, high-throughput and low-cost alternative for screening positive SARS-CoV-2 samples in many settings. We have designed a SNP identification pipeline to identify genetic variation using sequenced SARS-CoV-2 samples. Our pipeline identifies a minimal marker panel that can define distinct genotypes. To evaluate the system, we developed a genotyping panel to detect variants-identified from SARS-CoV-2 sequences surveyed between March and May 2020 and tested this on 50 stored qRT-PCR positive SARS-CoV-2 clinical samples that had been collected across the South West of the UK in April 2020. The 50 samples split into 15 distinct genotypes and there was a 61.9% probability that any two randomly chosen samples from our set of 50 would have a distinct genotype. In a high throughput laboratory, qRT-PCR positive samples pooled into 384-well plates could be screened with a marker panel at a cost of < £1.50 per sample. Our results demonstrate the usefulness of a SNP genotyping panel to provide a rapid, cost-effective, and reliable way to monitor SARS-CoV-2 variants circulating in an outbreak. Our analysis pipeline is publicly available and will allow for marker panels to be updated periodically as viral genotypes arise or disappear from circulation

Detecting SARS-CoV-2 variants with SNP genotyping.

Tracking genetic variations from positive SARS-CoV-2 samples yields crucial information about the number of variants circulating in an outbreak and the possible lines of transmission but sequencing every positive SARS-CoV-2 sample would be prohibitively costly for population-scale test and trace operations. Genotyping is a rapid, high-throughput and low-cost alternative for screening positive SARS-CoV-2 samples in many settings. We have designed a SNP identification pipeline to identify genetic variation using sequenced SARS-CoV-2 samples. Our pipeline identifies a minimal marker panel that can define distinct genotypes. To evaluate the system, we developed a genotyping panel to detect variants-identified from SARS-CoV-2 sequences surveyed between March and May 2020 and tested this on 50 stored qRT-PCR positive SARS-CoV-2 clinical samples that had been collected across the South West of the UK in April 2020. The 50 samples split into 15 distinct genotypes and there was a 61.9% probability that any two randomly chosen samples from our set of 50 would have a distinct genotype. In a high throughput laboratory, qRT-PCR positive samples pooled into 384-well plates could be screened with a marker panel at a cost of < £1.50 per sample. Our results demonstrate the usefulness of a SNP genotyping panel to provide a rapid, cost-effective, and reliable way to monitor SARS-CoV-2 variants circulating in an outbreak. Our analysis pipeline is publicly available and will allow for marker panels to be updated periodically as viral genotypes arise or disappear from circulation

Persistent SARS-CoV-2 infection in immunocompromised patients facilitates rapid viral evolution: Retrospective cohort study and literature review

BACKGROUND: Most patients with SARS-CoV-2 are non-infectious within 2 weeks, though viral RNA may remain detectable for weeks. However there are reports of persistent SARS-CoV-2 infection, with viable virus and ongoing infectivity months after initial detection. Beyond individuals, viral evolution during persistent infections may be accelerated, driving emergence of mutations associated with viral variants of concern. These patients often do not meet inclusion criteria for clinical trials, meaning clinical and virologic characteristics, and optimal management strategies are poorly evidence-based. METHODS: We analysed cases of SARS-CoV-2 infection from a regional testing laboratory in South-West England between March 2020 and December 2021, with at least two SARS-CoV-2 positive samples separated by ≥ 56 days were identified. Excluding those with confirmed or likely re-infection, we identified patients with persistent infection, characterised by an ongoing clinical syndrome consistent with COVID-19 alongside monophyletic viral lineage of SARS-CoV-2. We examined clinical and virologic characteristics, treatment, and outcome. We further performed a literature review investigating cases of persistent SARS-CoV-2 infection, reviewing patient characteristics and treatment. RESULTS: We identified six patients with persistent SARS-CoV-2 infection. All were hypogammaglobulinaemic and had underlying haematological malignancy, with four having received B-cell depleting therapy. Evidence of viral evolution, including accrual of mutations associated with variants of concern, was demonstrated in five cases. Four patients ultimately cleared SARS-CoV-2. In two patients, clearance followed treatment with casirivimab/imdevimab. Both survived beyond thirty days following viral clearance, having experienced infections of 305- and 269-days duration respectively, after failed attempts at clearance with alternative therapies. We found 60 cases of confirmed persistent infection in the literature, with a further 31 probable cases. Of those, 80% of patients treated with monoclonal antibodies cleared SARS-CoV-2, and none died. CONCLUSION: Haematological malignancy and patients receiving B-cell depleting therapies represent key groups at risk of persistent SARS-CoV-2 infection. Throughout persistent infection, SARS-CoV-2 can evolve rapidly, giving rise to significant mutations, including those implicated in variants of concern. Monoclonal antibodies appear to be a promising therapeutic option, potentially in combination with antivirals, crucial for individuals, and for public health

The Impact of Real-Time Whole-Genome Sequencing in Controlling Healthcare-Associated SARS-CoV-2 Outbreaks.

Nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have severely affected bed capacity and patient flow. We utilized whole-genome sequencing (WGS) to identify outbreaks and focus infection control resources and intervention during the United Kingdom's second pandemic wave in late 2020. Phylogenetic analysis of WGS and epidemiological data pinpointed an initial transmission event to an admission ward, with immediate prior community infection linkage documented. High incidence of asymptomatic staff infection with genetically identical viral sequences was also observed, which may have contributed to the propagation of the outbreak. WGS allowed timely nosocomial transmission intervention measures, including admissions ward point-of-care testing and introduction of portable HEPA14 filters. Conversely, WGS excluded nosocomial transmission in 2 instances with temporospatial linkage, conserving time and resources. In summary, WGS significantly enhanced understanding of SARS-CoV-2 clusters in a hospital setting, both identifying high-risk areas and conversely validating existing control measures in other units, maintaining clinical service overall

Protocol for the COG-UK hospital onset COVID-19 infection (HOCI) multicentre interventional clinical study: evaluating the efficacy of rapid genome sequencing of SARS-CoV-2 in limiting the spread of COVID-19 in United Kingdom NHS hospitals

Introduction: Nosocomial transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a significant cause of mortality in National Health Service (NHS) hospitals during the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to evaluate the impact of rapid whole genome sequencing of SARS-CoV-2, supported by a novel probabilistic reporting methodology, to inform infection prevention and control (IPC) practice within NHS hospital settings. / Methods and analysis: COG-UK HOCI (COG-UK Consortium Hospital-Onset COVID-19 Infections study) is a multicentre, prospective, interventional, superiority study. Eligible patients must be admitted to hospital with first confirmed SARS-CoV-2 PCR positive test result >48h from time of admission, where COVID-19 diagnosis was not suspected upon admission. The projected sample size for 14 participating sites covering all study phases over winter-spring 2020/2021 in the United Kingdom is 2,380 patients. The intervention is the return of a sequence report, within 48 hours in one phase (rapid local lab) and within 5-10 days in a second phase (mimicking central lab use), comparing the viral genome from an eligible study participant with others within and outside the hospital site. The primary outcomes are the incidence of Public Health England (PHE)/IPC-defined SARS-CoV-2 hospital-acquired infection during the baseline and two interventional phases, and proportion of hospital-onset cases with genomic evidence of transmission linkage following implementation of the intervention where such linkage was not suspected by initial IPC investigation. Secondary outcomes include incidence of hospital outbreaks, with and without sequencing data; actual and desirable changes to IPC actions; periods of healthcare worker (HCW) absence. A process evaluation using qualitative interviews with HCWs will be conducted alongside the study and analysis, underpinned by iterative programme theory of the sequence report. Health economic analysis will be conducted to determine cost-benefit of the intervention, and whether this leads to economic advantages within the NHS setting. / Ethics and dissemination: The protocol has been approved by the National Research Ethics Service Committee (Cambridge South 20/EE/0118). This manuscript is based on version 5.0 of the protocol. The study findings will be disseminated through peer-reviewed publications

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020-December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population

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

We reviewed all genomic epidemiology studies on COVID-19 in long-term care facilities (LTCFs) that had been published to date. We found that staff and residents were usually infected with identical, or near identical, SARS-CoV-2 genomes. Outbreaks usually involved one predominant cluster, and the same lineages persisted in LTCFs despite infection control measures. Outbreaks were most commonly due to single or few introductions followed by a spread rather than a series of seeding events from the community into LTCFs. The sequencing of samples taken consecutively from the same individuals at the same facilities showed the persistence of the same genome sequence, indicating that the sequencing technique was robust over time. When combined with local epidemiology, genomics allowed probable transmission sources to be better characterised. The transmission between LTCFs was detected in multiple studies. The mortality rate among residents was high in all facilities, regardless of the lineage. Bioinformatics methods were inadequate in a third of the studies reviewed, and reproducing the analyses was difficult because sequencing data were not available in many facilities