BACKGROUND:Dried blood spot (DBS) testing enables hepatitis C virus (HCV) testing in hard‑to‑reach patient populations but typically lacks full genotype and drug resistance information. This study evaluates a commercially available hybrid‑capture whole‑genome sequencing (WGS) workflow for HCV from DBS to determine suitability for clinical diagnostic and surveillance purposes. METHODS:We analysed 147 DBS and 24 plasma samples known to be HCV‑RNA positive using the QIAGEN QIAseq xHYB HepC panel. Whole genome sequences were generated using an in-house custom designed iterative bioinformatic pipeline. We assessed coverage characteristics, genotyping accuracy, resistance‑associated mutation (RAM) detection, and analytical sensitivity to determine panel performance. RESULTS:WGS success rates were high for both DBS (95%) and plasma (96%). Genome enrichment was uniform across major HCV genotypes, with no systematic dropouts. Genotyping showed 100% concordance with reference laboratory results including for novel genotype 1 subtypes. RAMs were detected in 27% of samples assessed, with full concordance with reference laboratory results in those with known resistance profiles. Analytical sensitivity was calculated at < 7301 IU/ml for WGS with slightly poorer performance when assessing the NS5a gene in isolation. CONCLUSION:This protocol reliably generates high‑quality HCV genomes from DBS, enabling accurate genotype, subtype, and resistance profiling. This approach overcomes limitations of venous sampling in hard-to-reach populations and could support decentralised diagnostics and enhancing genomic surveillance in populations underserved by traditional healthcare pathways.

Background In the United Kingdom, a 3-step algorithm for HIV screening is standard practice. Where there is discordancy between different serological assays, the interpretation of HIV status is challenging and guidance on reporting and follow up is conflicting. Inconclusive HIV results often prompt requests for repeat testing which is known to cause considerable anxiety as well as increased healthcare costs. Objectives We aimed to demonstrate the safety and efficacy of reporting single assay reactive HIV screens with a negative serum HIV-1 RNA PCR result as HIV negative, without the requirement for repeat sampling. Study design We conducted a retrospective review of local practice across two neighbouring NHS trusts. HIV results were reviewed to determine the proportion of single assay reactivity and to assess if any individuals exhibiting single assay reactivity were found to be HIV-1 positive by PCR, or with interval serological testing. Results A total of 1225 (0.26 %) of HIV screening requests exhibited single assay reactivity. None of these were found to be HIV-1 positive by PCR. 356 individuals with single assay reactivity had repeat testing performed, but none were shown to have developed HIV infection. Conclusions In our cohort, specimens exhibiting single HIV assay reactivity did not confirm when tested for HIV-1 RNA by PCR, or with interval serological testing of the source individual. This demonstrates the safety and efficacy of a longstanding approach at both NHS trusts.

This study examines the prevalence of respiratory viral infections (RVIs) in transplant recipients during the COVID-19 pandemic. Patients were admitted to a regional haematology unit (RHU) in England which provides a tertiary referral service for haematological malignancy, stem cell transplantation, CAR-T therapy, thrombosis, haemostasis and haemoglobinopathies. Weekly screening for RVIs was conducted on all inpatients in the RHU wards, and data were collected retrospectively for all admissions from August 2018 to February 2021. There was a significant drop in the circulation of non-SARS-CoV-2 RVIs in transplant recipients during the COVID-19 pandemic. The most common viral pathogen in the transplant cohort was rhinovirus, followed by parainfluenza 3, adenovirus, and RSV. The study also highlights the importance of infection prevention and control measures to reduce the risk of nosocomial transmission of RVIs and SARS-CoV-2 in transplant recipients. Further studies are needed to observe whether this effect is pronounced in multiple transplant centres.

Objectives: HTLV-1 is predominantly a sexually-transmitted infection but testing is not mentioned in HIV-PrEP guidelines. We ascertained HTLV-1/HTLV-2 seroprevalence amongst HIV-PrEP users in England. Methods: An unlinked anonymous seroprevalence study. Results: Amongst 2015 HIV-PrEP users, 95% were men, 76% of white ethnicity and 83% had been born in Europe. There were no HTLV-1/HTLV-2 seropositive cases (95% confidence interval 0% - 0.18%). Conclusions: There were no HTLV positive cases, likely reflecting the demographic of mostly white and European-born individuals. Similar studies are needed worldwide to inform public health recommenda-tions for HIV-PrEP using populations, particularly in HTLV-endemic settings. Crown Copyright (c) 2023 Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

BACKGROUND:The COG-UK hospital-onset COVID-19 infection (HOCI) trial evaluated the impact of SARS-CoV-2 whole-genome sequencing (WGS) on acute infection, prevention, and control (IPC) investigation of nosocomial transmission within hospitals.AIM:To estimate the cost implications of using the information from the sequencing reporting tool (SRT), used to determine likelihood of nosocomial infection in IPC practice.METHODS:A micro-costing approach for SARS-CoV-2 WGS was conducted. Data on IPC management resource use and costs were collected from interviews with IPC teams from 14 participating sites and used to assign cost estimates for IPC activities as collected in the trial. Activities included IPC-specific actions following a suspicion of healthcare-associated infection (HAI) or outbreak, as well as changes to practice following the return of data via SRT.FINDINGS:The mean per-sample costs of SARS-CoV-2 sequencing were estimated at £77.10 for rapid and £66.94 for longer turnaround phases. Over the three-month interventional phases, the total management costs of IPC-defined HAIs and outbreak events across the sites were estimated at £225,070 and £416,447, respectively. The main cost drivers were bed-days lost due to ward closures because of outbreaks, followed by outbreak meetings and bed-days lost due to cohorting contacts. Actioning SRTs, the cost of HAIs increased by £5,178 due to unidentified cases and the cost of outbreaks decreased by £11,246 as SRTs excluded hospital outbreaks.CONCLUSION:Although SARS-CoV-2 WGS adds to the total IPC management cost, additional information provided could balance out the additional cost, depending on identified design improvements and effective deployment.

Background: An increase in acute severe hepatitis of unknown aetiology in previously healthy children in the UK in March, 2022, triggered global case-finding. We aimed to describe UK epidemiological investigations of cases and their possible causes.Methods: We actively surveilled unexplained paediatric acute hepatitis (transaminase >500 international units per litre) in children younger than 16 years presenting since Jan 1, 2022, through notifications from paediatricians, microbiologists, and paediatric liver units; we collected demographic, clinical, and exposure information. Then, we did a case-control study to investigate the association between adenoviraemia and other viruses and case-status using multivariable Firth penalised logistic regression. Cases aged 1-10 years and tested for adenovirus were included and compared with controls (ie, children admitted to hospital with an acute non-hepatitis illness who had residual blood samples collected between Jan 1 and May 28, 2022, and without known laboratory-confirmed diagnosis or previous adenovirus testing). Controls were frequency-matched on sex, age band, sample months, and nation or supra-region with randomised selection. We explored temporal associations between frequency of circulating viruses identified through routine laboratory pathogen surveillance and occurrence of cases by linear regression. SARS-CoV-2 seropositivity of cases was examined against residual serum from age-matched clinical comparison groups.Findings: Between Jan 1 and July 4, 2022, 274 cases were identified (median age 3 years [IQR 2-5]). 131 (48%) participants were male, 142 (52%) were female, and one (<1%) participant had sex data unknown. Jaundice (195 [83%] of 235) and gastrointestinal symptoms (202 [91%] of 222) were common. 15 (5%) children required liver transplantation and none died. Adenovirus was detected in 172 (68%) of 252 participants tested, regardless of sample type; 137 (63%) of 218 samples were positive for adenovirus in the blood. For cases that were successfully genotyped, 58 (81%) of 72 had Ad41F, and 57 were identified as positive via blood samples (six of these were among participants who had undergone a transplant). In the case-control analysis, adenoviraemia was associated with hepatitis case-status (adjusted OR 374 [95% CI 155-903]). Increases in the detection of adenovirus from faecal samples, but not other infectious agents, in routine laboratory pathogen surveillance correlated with hepatitis cases 4 weeks later, which independently suggested an association (beta 006 [95% CI 002-011]). No association was identified for SARS-CoV-2 antibody seropositivity.Interpretation: We observed an association between adenovirus 41F viraemia and paediatric acute hepatitis. These results can inform diagnostic testing recommendations, clinical management, and exploratory in vitro or clinical studies of paediatric acute hepatitis of unknown aetiology. The role of potential co-factors, including other viruses and host susceptibility, requires further investigation.

Luke Carter-Brzezinski , Elizabeth Davies , Jane Norman, Lourdes Rubio, Christopher Dixon, Sarah Brett, Tony Dunne, Shahid Iqbal, Fiona L. Dignan, Shazaad Ahmad, Nicholas Machin, Henry Morriss, Anthony Wilson and Eleni Tholouli Department of Clinical Haematology, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK; Department of Critical Care Medicine, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK; Department of Virology, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK

Here we describe a retrospective clinical evaluation of the QIAGEN artus® SARS-CoV-2 Prep&Amp UM RT-PCR assay that detects SARS-CoV-2 RNA without the need for a nucleic acid eluate extraction procedure. Using Roche SARS-CoV-2 RT-PCR on the cobas® 8800 platform as a reference standard, a total of 225 confirmed SARS-CoV-2 positive and 320 negative nasopharyngeal swabs in viral transport media, were used to evaluate the artus® assay. Using the RT-PCR cycle threshold as a semi-quantitative marker of viral load, an assessment of over 370,000 SARS-CoV-2 RT-PCR positive results was used in the design of the reference positive specimen cohort. The viral load of all reference positive specimens used in the evaluation was a unique and accurate representation of the range and levels of SARS-CoV-2 positivity observed over a 13-month period of the COVID-19 pandemic. The artus® RT-PCR detects the presence of SARS-CoV-2 RNA, an internal control, and the human RNase P gene to ensure specimen quality. The diagnostic sensitivity of artus® was 92.89% with a specificity of 100%. To assess the analytical sensitivity, a limit of detection was performed using the 1st WHO NIBSC SARS-CoV-2 international standard, recording a 95% LOD of 1.1 × 103 IU/ml. The total invalid rate of specimens was 7.34% due to a lack of detectable RNase P (Ct >35). The artus® SARS-CoV-2 Prep&Amp UM RT-PCR assay is a new rapid RT-PCR assay, which may be considered to produce acceptable levels of diagnostic sensitivity and specificity whilst potentially halving the laboratory processing time.

The COVID-19 pandemic has led to the rapid development of a plethora of molecular diagnostic assays with real-time polymerase chain reaction (RT-PCR) at the forefront. In this review, we will discuss the history and utility of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) molecular diagnostics and the associated current and future regulatory process in Europe. We will assess the performance characteristics of a range of the most common SARS-CoV-2 molecular tests currently used in Europe with a focus on as rapid molecular platforms, stand-alone RT-PCR kits, the role of low-throughput and high-throughput end-to-end testing platforms, and the rapidly evolving field of SARS-CoV-2 variant of concern identification.

Large scale screening of health care workers and the general population for asymptomatic COVID-19 infection requires modalities that are amenable to testing at scale while retaining acceptable levels of sensitivity and specificity. This study evaluated a novel COVID-19 Direct-RT LAMP assay using saliva samples in asymptomatic individuals by comparison to RT-PCR. Additional studies were performed using VTM collected from routine diagnostic testing. Analytical sensitivity was determined for Direct RT-LAMP assay using the WHO International Standard. Finally, quantified results from RT-PCR testing of 9177 nose and throat swabs obtained from routine diagnostic testing were used to estimate the sensitivity of Direct RT-LAMP using the limit of detection curve obtained from the analytical sensitivity data. Results from saliva testing demonstrated a sensitivity of 40.91% and a specificity of 100% for Direct RT-LAMP. The sensitivity and specificity for nose and throat swabs were 44.85% and 100% respectively. The 95% limit of detection (LOD) for Direct RT-LAMP was log 7.13 IU/ml (95% 6.9-7.5). The estimated sensitivity for Direct-RT LAMP based on the results of 9117 nose and throat swabs was 34% and 45% for saliva and VTM respectively. The overall diagnostic sensitivity of Direct RT-LAMP was low compared to RT-PCR. Testing of nose and throat swabs and estimating the sensitivity based on a large cohort of clinical samples demonstrated similar results. This study highlights the importance of utilising the prospective collection of samples from the intended target population in the assessment of diagnostic sensitivity.

Introduction Viral sequencing of SARS-CoV-2 has been used for outbreak investigation, but there is limited evidence supporting routine use for infection prevention and control (IPC) within hospital settings. Methods We conducted a prospective non-randomised trial of sequencing at 14 acute UK hospital trusts. Sites each had a 4-week baseline data-collection period, followed by intervention periods comprising 8 weeks of ‘rapid’ (<48h) and 4 weeks of ‘longer-turnaround’ (5-10 day) sequencing using a sequence reporting tool (SRT). Data were collected on all hospital onset COVID-19 infections (HOCIs; detected ≥48h from admission). The impact of the sequencing intervention on IPC knowledge and actions, and on incidence of probable/definite hospital-acquired infections (HAIs) was evaluated. Results A total of 2170 HOCI cases were recorded from October 2020-April 2021, with sequence reports returned for 650/1320 (49.2%) during intervention phases. We did not detect a statistically significant change in weekly incidence of HAIs in longer-turnaround (IRR 1.60, 95%CI 0.85-3.01; P= 0.14) or rapid (0.85, 0.48-1.50; P= 0.54) intervention phases compared to baseline phase. However, IPC practice was changed in 7.8% and 7.4% of all HOCI cases in rapid and longer-turnaround phases, respectively, and 17.2% and 11.6% of cases where the report was returned. In a per-protocol sensitivity analysis there was an impact on IPC actions in 20.7% of HOCI cases when the SRT report was returned within 5 days. Conclusion While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days.

Background: Viral sequencing of SARS-CoV-2 has been used for outbreak investigation, but there is limited evidence supporting routine use for infection prevention and control (IPC) within hospital settings. Methods: We conducted a prospective non-randomised trial of sequencing at 14 acute UK hospital trusts. Sites each had a 4-week baseline data collection period, followed by intervention periods comprising 8 weeks of ‘rapid’ (<48 hr) and 4 weeks of ‘longer-turnaround’ (5–10 days) sequencing using a sequence reporting tool (SRT). Data were collected on all hospital-onset COVID-19 infections (HOCIs; detected ≥48 hr from admission). The impact of the sequencing intervention on IPC knowledge and actions, and on the incidence of probable/definite hospital-acquired infections (HAIs), was evaluated. Results: A total of 2170 HOCI cases were recorded from October 2020 to April 2021, corresponding to a period of extreme strain on the health service, with sequence reports returned for 650/1320 (49.2%) during intervention phases. We did not detect a statistically significant change in weekly incidence of HAIs in longer-turnaround (incidence rate ratio 1.60, 95% CI 0.85–3.01; p = 0.14) or rapid (0.85, 0.48–1.50; p = 0.54) intervention phases compared to baseline phase. However, IPC practice was changed in 7.8 and 7.4% of all HOCI cases in rapid and longer-turnaround phases, respectively, and 17.2 and 11.6% of cases where the report was returned. In a ‘per-protocol’ sensitivity analysis, there was an impact on IPC actions in 20.7% of HOCI cases when the SRT report was returned within 5 days. Capacity to respond effectively to insights from sequencing was breached in most sites by the volume of cases and limited resources. Conclusions: While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days. Funding: COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) (grant code: MC_PC_19027), and Genome Research Limited, operating as the Wellcome Sanger Institute. Clinical trial number: NCT04405934 .

Introduction The risks to surgeons of carrying out aerosol generating procedures during the COVID pandemic are unknown. To start to define these risks, in a systematic manner, we investigated the presence of SARS-CoV-2 virus in the abdominal fluid and lower genital tract of patients undergoing surgery. Methods We carried out a prospective cross sectional observational study of 113 patients undergoing abdominal surgery or instrumentation of the lower genital tract. We took COVID swabs from the peritoneal cavity and from the vagina from all eligible patients. Results were stratified by pre operative COVID status. Results In patients who were presumed COVID negative at the time of surgery SARS-CoV-2 virus RNA was detected in 0/102 peritoneal samples and 0/98 vaginal samples. Peritoneal and vaginal swabs were also negative in one patient who had a positive nasopharyngeal swab immediately prior to surgery. Conclusions The presence of SARS-CoV-2 RNA in the abdominal fluid or lower genital tract of presumed negative patients is nil or extremely low. These data will inform surgeons of the risks of restarting laparoscopic surgery at a time when COVID19 is endemic in the population.

Introduction: Rapid, high throughput diagnostics are a valuable tool, allowing the detection of SARS-CoV-2 in populations, in order to identify and isolate people with asymptomatic and symptomatic infections. Reagent shortages and restricted access to high throughput testing solutions have limited the effectiveness of conventional assays such as reverse transcriptase quantitative PCR (RTqPCR), particularly throughout the first months of the pandemic. We investigated the use of LamPORE, where loop mediated isothermal amplification (LAMP) is coupled to nanopore sequencing technology, for the detection of SARS CoV 2 in symptomatic and asymptomatic populations. Methods: In an asymptomatic prospective cohort; health care workers across four sites (Birmingham, Southampton, Basingstoke and Manchester) self swabbed with nasopharyngeal swabs weekly for three weeks and supplied a saliva specimen daily. These samples were tested for SARS CoV 2 RNA using the Oxford Nanopore LamPORE system and a reference RTqPCR assay on extracted sample RNA. A second retrospective cohort of 848 patients with influenza like illness from March 2020 to June 2020, were similarly tested from nasopharyngeal swabs. Results: In the asymptomatic cohort a total of 1200 participants supplied 23,427 samples (3,966 swab, 19,461 saliva) over a three-week period. The incidence of SARS CoV 2 was 0.95% using LamPORE. Diagnostic sensitivity and specificity was > 99.5% in both swab and saliva asymptomatic samples as compared to the reference RTqPCR test. In the retrospective symptomatic cohort, the incidence was 13.4% and the sensitivity and specificity were 100%. Conclusions: LamPORE is a highly accurate methodology for the detection of SARS CoV 2 in both the symptomatic and asymptomatic population settings and can be used as an alternative to RTqPCR.

Dried blood spot samples (DBS) provide an alternative sample type to venous blood samples for antibody testing. DBS are used by NHS for diagnosing Hepatitis C and by Public Health England for large scale HIV and Hepatitis C serosurveillance; the applicability of DBS based approaches for SARS-CoV-2 antibody detection is uncertain. The study aimed to compare antibody detection in DBS eluates using the Roche Elecsys ® immunoassay with antibody detection in paired plasma samples, using the same assay. The study was in one Police and one Fire & Rescue facility in England; it comprised of 195 participants within a larger sample COVID-19 serodiagnostics study of keyworkers, EDSAB-HOME. Outcome measures were sensitivity and specificity of DBS (the index test) relative to plasma (the reference test), at an experimental cut-off; quality of DBS sample collected; estimates of relative sensitivity of DBS vs. plasma immunoassay in a larger population. 18/195 (9.2%) participants tested positive using plasma samples. DBS sample quality varied markedly by phlebotomist, and low sample volume significantly reduced immunoassay signals. Using an experimental cut-off, sensitivity and specificity of DBS were 89.0% (95% CI 67.2, 96.9%) and 100.0% (95% CI 97.9, 100%) respectively compared with using plasma. The limit of detection for DBS is about 30 times higher than for plasma. DBS use for SARS-CoV-2 serology, though feasible, is insensitive relative to immunoassays on plasma. Sample quality impacts on assay performance. Alternatives, including the collection of capillary blood samples, should be considered for screening programs.

On 26th November 2021, a novel SARS-CoV-2 variant B.1.1.529 (Omicron variant) was designated as a variant of concern by the World Health Organisation. Using data from the Virology laboratory at the Manchester Medical Microbiology Partnership (MMMP, a partnership between UKHSA and the Manchester Foundation Trust), we have extracted a real-time feed of Omicron samples from hospitals across Greater Manchester, an area of the United Kingdom with a population size of approximately three million individuals. Omicron hospital samples are growing exponentially across Greater Manchester (doubling time 2.7 days (95% CI: 2.1, 3.7)). The proportion of Omicron in hospital samples follows a similar trajectory to the SGTF proportion in cases, but with a two-day offset. This is consistent with the delay from testing positive to hospital admission, implying a similar proportion of Omicron cases are converting to hospital admissions as for Delta cases. Comparing the Greater Manchester data to national hospitalisation data, similar tends are observed. Therefore, there is no signal of a substantial reduction in hospital admission risk with Omicron, and Omicron epidemics are likely to place a substantial burden on public health infrastructure.

Understanding the effectiveness of infection control methods in reducing and preventing SARS-CoV-2 transmission in healthcare settings is of high importance. We sequenced SARS-CoV-2 genomes for patients and healthcare workers (HCWs) across multiple geographically distinct UK hospitals, obtaining 173 high-quality SARS-CoV-2 genomes. We integrated patient movement and staff location data into the analysis of viral genome data to understand spatial and temporal dynamics of SARS-CoV-2 transmission. We identified eight patient contact clusters (PCC) with significantly increased similarity in genomic variants compared to non-clustered samples. Incorporation of HCW location further increased the number of individuals within PCCs and identified additional links in SARS-CoV-2 transmission pathways. Patients within PCCs carried viruses more genetically identical to HCWs in the same ward location. SARS-CoV-2 genome sequencing integrated with patient and HCW movement data increases identification of outbreak clusters. This dynamic approach can support infection control management strategies within the healthcare setting.

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Understanding the effectiveness of infection control methods in reducing and preventing SARS-CoV-2 transmission in healthcare settings is of high importance. We sequenced SARS-CoV-2 genomes for patients and healthcare workers (HCWs) across multiple geographically distinct UK hospitals, obtaining 173 high-quality SARS-CoV-2 genomes. We integrated patient movement and staff location data into the analysis of viral genome data to understand spatial and temporal dynamics of SARS-CoV-2 transmission. We identified eight patient contact clusters (PCC) with significantly increased similarity in genomic variants compared to non-clustered samples. Incorporation of HCW location further increased the number of individuals within PCCs and identified additional links in SARS-CoV-2 transmission pathways. Patients within PCCs carried viruses more genetically identical to HCWs in the same ward location. SARS-CoV-2 genome sequencing integrated with patient and HCW movement data increases identification of outbreak clusters. This dynamic approach can support infection control management strategies within the healthcare setting. Introduction The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic continues to place a significant burden on healthcare services worldwide (Miller et al., 2020; Propper et al., 2020; Maringe et al., 2020). Reducing the spread and outbreak of SARS-CoV-2 infections is particularly important in hospitals and care homes where individuals at high risk of developing severe responses to infection are vulnerable to transmission due to close and regular contact between patients and healthcare workers (HCWs) (Clark et al., 2020; Nguyen et al., 2020; Rivett et al., 2020). Whilst the recent development of promising SARS-CoV-2 vaccines may reduce risk to individuals in hospitals who receive the vaccine (Walsh et al., 2020; Anderson et al., 2020; Keech et al., 2020; Lodge, 2020), risk of infection will not be completely mitigated. Standard methods of infection control will continue to be required to ensure patient and HCW safety. Regular and iterative testing for SARS-CoV-2, in both patients and HCWs, underpins approaches that quantify and control nosocomial transmission (Black et al., 2020) but will not provide insights into how the virus may have spread throughout an institution, alone. Since the degree to which different groups (patients, HCW) propagate SARS-CoV-2 transmission remains uncertain, the utility of screening approaches to prospectively prevent nosocomial spread is difficult to evaluate. It is well established that HCWs are an important component in pathogen outbreak investigations (Peacock et al., 2018; Wenger et al., 1995; de Swart et al., 2000). Here, we have integrated viral genome sequencing with patient admissions records and staff workplace information to investigate SARS-CoV-2 nosocomial outbreaks. We demonstrate that such an approach can be used to identify highly likely nosocomial transmission events of SARS-CoV-2 between HCWs and the patients in their care. Results Sample demographics We generated high-quality sequencing datasets for 173 samples that had been collected from inpatient wards, accident and emergency departments (A and E) and from HCWs across geographically distinct hospitals. All samples were collected between calendar week 11 (commencing 8 March 2020) and calendar week 23 (ending 6 June 2020). Of the 173 high-quality sequenced samples, 39 (23%) were HCW samples. The remaining 134 samples were collected from patients admitted to a total of 31 wards and units situated across the five hospitals. Forty-four (25%) of the 173 high-quality samples were from three hospital locations, which had seen sudden rises in SARS-CoV-2-positive cases; an additional 35 samples from these locations failed to meet our quality criteria for sequencing quality (Figure 1—figure supplement 1). Forty-seven (35%) of 134 patient samples were from A and E departments. The median age of the 134 patients with sequenced samples was 81 years (mean = 75 years; range = 6 weeks–100 years). Sequencing metrics Comparison of SARS-CoV-2 amplicon yield versus target coverage found that samples consistently met 10× minimum coverage thresholds when a total yield of >400 ng was obtained (median = 846; range = 86–3667; Figure 1—figure supplement 1). Where RT-qPCR cycle threshold (Ct) values were provided, we found samples with Ct values of 31 or more resulted in lower amplicon yield and frequently failed to meet sequencing coverage thresholds (Figure 1—figure supplement 1). In the 173 high-quality samples, we identified 268 genomic variants in comparison to MN908947.3, of which 86 were recurrent variants across samples (range = 2–126 samples) and 182 were unique to single samples. Global lineage assignment We utilised Pangolin for placement of the 173 high-quality viral genome sequences within the global SARS-CoV-2 phylogenetic tree. Eight-seven percent (151/173) of sequenced genomes were confidently assigned to an existing lineage (SH-alrt > 80%, UFbootstrap > 90%). We identified 11 distinct lineages in our cohort, with a bias towards viral genomes assigned to lineage B.1.1 (71%, 122/173; Figure 1—figure supplement 2). There were no samples assigned to lineage A. Incorporating the calendar week of sample collection into this analysis suggested a constant relative frequency of viral lineage B.1.1 over time (Figure 1—figure supplement 2). Viral lineage B.1.1 was present in samples collected from 29 different wards, including A and E, and in HCWs, and was present in our cohort between calendar weeks 12–23 (Figure 1—figure supplement 2). Local phylogenetic networks implicate nosocomial transmission within hospital wards To understand how the viral genomes collected from different areas across hospital sites related to one another we created a local phylogenetic tree rooted to a SARS-CoV-2 genome originally sequenced in Wuhan, China (MN908947.3) (Minh et al., 2020; Wu et al., 2020). We overlaid locational origins of the samples (i.e. ward and units from which samples were collected) onto the phylogenetic tree (Figure 1—figure supplement 3). We observed clusters within the phylogenetic tree that were formed predominantly from viral genome sequences taken in individual wards (Figure 1—figure supplement 3). For example, 19/31 of the samples in one of these identified clusters were collected from a single hospital ward (H2_W7, Figure 1—figure supplement 3). The phylogenetic clusters were supported by maximum-likelihood and consensus (10,000 ultrafast bootstraps) approaches for tree creation. Incorporating staff and patient movement enhances identification of nosocomial transmissions Patient contact clusters Utilising patient admissions data over the period of the pandemic, we first created a network of potential direct or indirect patient–patient contacts, inferred through the presence of two individuals on the same ward on the same calendar day. We adopted national guidelines for definition of nosocomial infection to identify contacts between individuals relative to the date of sample collection for a positive SARS-CoV-2 test. Using a contact window of 3–7 days prior to a positive SARS-CoV-2 test (termed herein as likely period of infection), we developed a network of patient–patient contacts (Figure 1). We identified eight significant clusters of individuals (patient contact clusters [PCCs]), defined by multiple potential contacts between two or more individuals within the likely period of infection for each individual (A–H, Figure 1). We assessed the pairwise similarity of viral genomes in identified clusters, demonstrating significantly higher viral genetic similarity within clusters compared to non-clustered samples (Figure 2, p<0.001). This trend was further supported by overlaying the PCCs onto the local phylogenetic tree (Figure 1—figure supplement 4). We identified areas of the phylogeny where there was a 20-fold increase, than expected by chance, in potential contacts between a patient and six or more patients with the most closest genetically related viral genome samples. Consecutive windows of increased (20-fold) patient–patient contacts were merged to identify seven distinct clusters of individuals defined by high level of genetic relatedness of the viral genome sequence and a high degree of potential patient–patient contacts during the likely period of infection (Figure 1—figure supplement 4). Figure 1 with 4 supplements see all Download asset Open asset Incorporating healthcare worker (HCW) and patient admissions data into the analysis of viral genetic relatedness improves certainty of nosocomial outbreaks. (a) Network of direct and indirect potential patient–patient contacts within the window of likely infection (3–7 days prior to positive SARS-CoV-2 test) defines eight significant patient contact clusters (PCCs, overlaid boxes); (b) network including HCW interactions one week prior to positive SARS-CoV-2 test and patient infection classification. Nodes represent individual patients or HCWs, with ordinal numbers representing their position in the constructed local phylogenetic tree. Edges indicate presence on the same hospital ward on the same calendar day. Inclusion of HCWs brings together originally disparate PCCs (b) and (c) increases the number of individuals within viral clusters (VCs) – defined as clusters of identical viral samples or derived viral samples which differ by a single genomic variant. We identified 44 individuals within VCs in the newly defined HCW contact clusters (HCW_A, HCW_B, HCW_C), 21 of whom were not identified within VCs using PCCs alone. The shape of symbols within the enlarged boxes displays the classification of SARS-CoV-2 infection in patients: community, community-acquired infection (positive test within 2 days of hospital admission); possible, possible hospital-acquired infection (positive test 3–7 days after hospital admission); probable, probable hospital-acquired infection (positive test 8–14 days after hospital admission); definite, definite hospital-acquired infection (positive test >14 days after hospital admission). The presence of several patients with definite and probable hospital-acquired infections within the PCC and HCW interaction clusters further reinforces the risk of SARS-CoV-2 transmission events between patients and HCWs on the same hospital wards. Figure 2 Download asset Open asset SARS-CoV-2 viral genomes are more similar in groups of patients and healthcare workers (HCWs) who have been in contact prior to a positive SARS-CoV-2 test. (a) Pairwise genomic variant similarity comparisons of SARS-CoV-2 genomes by the status within patient contact clusters (PCCs) demonstrates increased genetic similarity when patients have been in direct or indirect contact with one another 3–7 days prior to positive SARS-CoV-2 test. Pairwise comparisons within PCCs (n = 544) were tested against all pairwise comparisons that were not defined within the PCCs (n = 29,212). (b) Pairwise genomic variant similarity comparisons of SARS-CoV-2 genomes by patient–HCW interactions in the week prior to positive SARS-CoV-2 test (n, yes = 98, no = 11,836). (c) Pairwise genomic variant similarity comparisons of SARS-CoV-2 genomes by presence within PCCs including interactions with HCWs. Pairwise comparisons within HCW clusters (n = 846) were tested against pairwise comparisons that were not defined within the PCCs including HCW interactions (n = 28,168). Colours of boxplots reflect the PCCs identified in Figure 1, and asterisks indicate significance level determined through a two-sided Wilcoxon rank-sum test (*<0.05; **<0.01; ***<0.001 after Bonferroni correction for multiple testing). Contact clusters including patients and HCWs Next, we created networks of potential interactions between HCWs and patients (Figure 1). This was inferred through the presence of patients in the direct workplace of staff members for at least one calendar day in the 7 days prior to a patient testing positive for SARS-CoV-2. We identified 49 potential contacts between HCWs and patients, including 10 HCWs and 18 patients. Incorporating this information onto the local phylogeny expanded the density of contacts within hotspots (Figure 1—figure supplement 4) and altered the structure of the PCCs within the likely period of infection (Figure 1). Overall, we identified significantly increased genetic similarity in viral samples between patients and HCWs in the same ward locations in comparison to patients and HCWs from different wards (Figure 2, p<0.001). Moreover, we observed greater genetic similarity within each of the PCCs with HCW interactions incorporated (Figure 2, p<0.001). For example, HCW_A illustrates that the addition of HCWs created a previously hidden link between PCC_A and PCC_C (Figure 1); these newly identified connections increased the number of individuals within viral clusters (VCs), defined as clusters of identical viral samples or viral samples that differed by just a single genomic variant (Figure 1). Within the newly defined contact clusters including HCWs and patients, we identified six genetically identical VCs including 18 individuals, and 24 individuals with viral sequences differing by a single genomic variant. The number of individuals in VCs was expanded by including patient–HCW contact networks, over and above those identified using patients alone (Figure 1). Temporal patterns within identified patient and HCW contact clusters In order to establish the most likely SARS-CoV-2 transmission pathways, we examined temporal patterns within proposed nosocomial outbreaks. We created a median joining network for each of the patient and HCW contact networks and incorporated time of sample collection. As multiple entry points into the outbreaks may complicate inference, we cleaned the data to create median joining networks for the 10 most genetically related viral samples within each cluster (inferred from position in local phylogeny, Figure 1—figure supplement 3). This identified trends in the datasets that further reinforced the likelihood of nosocomial infection (Figure 3). We observed that all identified contact clusters could be rooted back to potential 'founder' viral samples that occurred early during the suspected outbreak. For example, in PCC_B and PCC_D (Figure 3), which occurred on the same hospital ward at different time points, the original 'founder' viral samples contained the novel (at time of sample collection) genomic variant MN908947.3–3228-T-G. Eighteen (95%) of 19 viral samples subsequently collected from this hospital ward also contained the same novel variant, at least 16 of these cases were probable or definite hospital-acquired infections. Reducing the quality threshold for sequencing datasets (≥50% coverage at ≥10× coverage) identified the MN908947.3–3228-T-G variant in an additional five samples collected from this hospital location (83%, n = 6 additional samples included with modified criteria). These data highlight that samples excluded from our analyses due to sequencing quality criteria may be missing links within SARS-CoV-2 transmission pathways. Figure 3 Download asset Open asset Temporal patterns in SARS-CoV-2 genomic similarity identify potential viral transmission pathways within patient contact clusters (PCCs) including healthcare worker (HCW) interactions. For each of the highlighted contact clusters, a median joining network is presented with size of nodes representing number of samples and numbers indicating day of nasal or throat swab collection. The presented network suggests a possible path of viral transmission within each contact cluster, hatches represent single genomic variants that differ between viral clusters. The top scatterplot shows that the number of genomic variants identified against the MN908947.3 reference genome increases over time. The bottom scatterplot shows the number of other samples within the contact cluster that are identical or expected to be derived from samples collected at specific calendar days – these are defined as other samples that are identical but with the presence of additional genomic variants. The observed trends show that samples collected early during the suspected outbreaks have a greater number of derived or identical samples than those collected at a later day. These data support that the samples collected early during the highlighted contact clusters are early founder events during a nosocomial outbreak. We observed further trends indicative of nosocomial outbreaks, including the number of genomic variants identified against the MN908947.3 reference genome increasing over time within each of the contact networks, and consistently observed that samples collected early during the suspected outbreaks had a greater number of derived or identical samples within the outbreak than those collected at a later day (Figure 3). Both of these trends would be expected if single ancestral SARS-CoV-2 sequences were original founders of an outbreak. Discussion In this study, we obtained high-quality SARS-CoV-2 genomic sequences for 173 individuals across five hospitals in the North West of the UK, including both patients and HCWs. We incorporated potential contacts between the two groups into a phylogenetic analysis and comparison of pairwise genetic similarity. Overall, this demonstrated that inclusion of contact data increased confidence in the characterisation of nosocomial outbreaks. This work was undertaken across multiple, geographically separate hospitals in the UK with responsibility for the care of large numbers of SARS-CoV-2-positive patients. At the time of analysis, there was no routine SARS-CoV-2 screening of asymptomatic HCWs. Similar to other UK hospitals, the different locations within the hospitals were assigned as either green (SARS-CoV-2-negative) or red (SARS-CoV-2-positive) zones. This strategy, in combination with additional infection control measures such as staff bubbles, is widespread as a method to reduce nosocomial infection. However, as patients tested positive after spending prolonged periods of time in green areas, it became apparent that there were unrecognised transmission events between the two areas or from the community into green zones. Viral genome sequencing offers a realistic possibility to track and identify root causes of nosocomial transmissions (Lucey et al., 2020; Meredith et al., 2020). Here, we applied genome sequencing to throat and nasal swabs obtained from HCWs and patients. We identified 268 unique genomic variants in the 173 high-quality samples and placed our samples within recognised global lineages (Figure 1—figure supplement 2). The predominance of a single SARS-CoV-2 lineage meant that precise differentiation of viral samples from individual hospital wards was not possible at this level (Figure 1—figure supplement 2). We therefore created a local phylogeny for our sequenced genomes rooted to MN908947.3 (Figure 1—figure supplement 3) and calculated pairwise similarity in the genomic variants identified in each of the SARS-CoV-2 genomes. It has been previously noted that the low genetic diversity of SARS-CoV-2 causes complexity in the identification of nosocomial outbreaks, as samples may be genetically identical by chance rather than through transmission between individuals (Meredith et al., 2020; Gudbjartsson et al., 2020). Our data reemphasise this low genetic diversity of SARS-CoV-2, with a median number of 11 (range = 2–16) variants identified per sample, and an average pairwise similarity of 61.5% (Figure 2). Integrating our analyses with patient admissions record (available for 104/134 patients for at least one day prior to SARS-CoV-2-positive test) and location of staff workplaces (available for 31/39 HCWs) identified clusters of individuals who had interacted during their most likely periods of infection. These analyses confirmed that individuals who had been in contact during this period were more likely to have genetically more similar viral samples than individuals who had not been in contact (Figure 2). While we cannot exclude that viral samples are genetically identical or similar by chance due to the low genetic diversity of SARS-CoV-2, the spatial and temporal patterns of viral genetic relatedness that we observe provide strong evidence for nosocomial transmission amongst both patients and HCWs. These trends are observed in at least five distinct clusters, across three geographically distinct hospitals. These are further reinforced by the presence of novel genomic variants (at the time of analysis) transmitting through identified clusters (Figure 1—figure supplement 3). We suggest these data support the widespread adoption of iterative screening strategies for HCWs who may be pre-symptomatic or asymptomatic shedders of SARS-CoV-2 (Black et al., 2020; Arons et al., 2020; Buitrago-Garcia et al., 2020). Pre-symptomatic or asymptomatic individuals have been demonstrated to be important contributors to SARS-CoV-2 outbreaks (Rivett et al., 2020; Kasper et al., 2020; Letizia et al., 2020). Others have shown that comprehensive characterisation of outbreak clusters can be hindered by the existence of hidden links between individuals, even where all individuals within clusters are known and completely isolated from external contacts (Sekizuka et al., 2020). In our data, we observed the presence of cohort-specific genomic variants shared between individuals but without a known connection between the sampled individuals. Here, it is likely that missing individuals or connections between surveyed individuals are adding complexity to our analyses. While the use of digital contact tracing is difficult in hospital environments, track and tracing smartphone software could be useful to extend the characterisation of contacts between individuals and to understand the accuracy of the assumptions enforced in this study (Firth et al., 2020; Ferretti et al., 2020). Here, we infer contacts between individuals through their presence on the same hospital ward on the same calendar day. This approach identifies individuals within our cohort that are likely to have been in face-to-face contact. We note that this approach has imperfect assumptions but is likely to dilute rather than inflate the statistical significance of the investigations reported in this study (Figures 1–3). Future work may enable additional co-factors to be considered in models for network creation such as infection control measures in place on hospital wards (e.g. personal protective equipment utilised), symptomatic status of individuals, and the length, type, and the proximity of physical contacts between individuals. These approaches are supported by recent data demonstrating that over half of SARS-CoV-2 transmissions occur when individuals are pre-symptomatic and that transmission likelihood increases with the duration and proximity of contact (Sun et al., 2021). Collecting data to incorporate these factors into network models in the healthcare setting may enable the generation of more precise binary contact clusters according to specified parameters or the development of weighted networks biased by the relative importance placed on co-factors (Firth et al., 2020). Understanding the concordance of empirical datasets of SARS-CoV-2 transmission, as reported here, and computational models of transmission is an important avenue for future work to identify the most influential factors to decrease the likelihood of SARS-CoV-2 transmission in both healthcare and community settings. Our data demonstrate that SARS-CoV-2 genome sequencing alongside patient admission and staff workplace information can identify transmission events within the healthcare setting. Looking forward, we expect that the adoption of genomic approaches in real time, for example within 48 hr, alongside consideration of patient movement datasets will enable rapid identification of linked hospital-acquired SARS-CoV-2 infections. Such approaches could optimise infection control management strategies, lead to targeted interventions, reduce nosocomial transmission, and ultimately prevent avoidable harm to vulnerable individuals who acquire COVID-19 whilst in the healthcare setting. Materials and methods Sample selection Request a detailed protocol Throat and nasal swab samples were collected from patients and healthcare professionals based at MFT hospital sites. Diagnostic SARS-CoV-2 RT-qPCR assays were performed by the Clinical Virology Department of the Manchester Medical Microbiology Partnership (MMMP; Manchester, UK). RT-qPCR-positive samples were selected for SARS-CoV-2 whole-genome analyses at the Manchester Centre for Genomic Medicine (MCGM; Manchester, UK). We attempted to sequence all available SARS-CoV-2-positive samples from hospital wards highlighted by infection control surveillance officers as potential outbreaks within our sample collection period due to sudden rises in positive cases. Demographic, hospital location, and laboratory data were included with each referral. All ward names have been anonymised for publication. Sample and NGS library preparation Request a detailed protocol Nucleic acid re-extraction was performed using the chemagic Viral DNA/RNA 300 Kit on the chemagic 360 instrument (PerkinElmer Inc, Waltham, MA). All extracted RNA samples underwent cDNA synthesis using either LunaScript RT SuperMix kit (New England Biolabs, Ipswich, MA) or SuperScriptIV (Thermo Fisher Scientific, Waltham, MA), in accordance with manufacturers protocols. SARS-CoV-2 whole-genome libraries were prepared using SureSelectXT Low Input kit CoVHuman6X enrichment capture-based method (Agilent Technologies, Santa Clara, CA) or the ARTIC tiled amplicon multiplex PCR protocol (version three primer set) with NEBNext Ultra II DNA Library Prep Kit (New England Biolabs). PCR and library preparation quality validations were obtained using TapeStation D1000 and HSD1000 (Agilent Technologies). Final libraries were sequenced using MinION flow cells version 9.4.1 (Oxford Nanopore Technologies, Oxford, UK) or MiSeq (Illumina, San Diego, CA) using reagent kits for 600 cycles (for tiled PCR SARS-CoV-2 amplification) or 300 cycles (for Agilent SureSelectXT enrichments) for paired end sequencing. Bioinformatics and analysis Request a detailed protocol Sequencing reads were deduplicated on instrument for Illumina MiSeq datasets or using Guppy for Oxford Nanopore datasets. Reads were aligned to the SARS-CoV-2 reference genome (MN908947.3) using BWA-MEM (Li, 2013) for Illumina MiSeq datasets and using Minimap2 (Li, 2018) for Oxford Nanopore MinIon datasets. Reads were filtered and variants identified using iVar v1.2.2 (Grubaugh et al., 2019). Samples with ≥75% of the MN908947.3 reference genome covered by ≥10 high-quality reads with at least 50 aligning nucleotides were included for downstream analysis. Variants with an allele fraction of at least 0.6 in high-quality mapped reads were identified in comparison to MN908947.3, and a consensus FASTA built using iVar. Multi-way alignments were performed using MAFFT v7.407 (Katoh et al., 2002), and maximum-likelihood trees rooted to MN908947.3 using 1000 bootstraps were generated with IQ-TREE v1.6.12 (Minh et al., 2020). Trees were visualised in Geneious Prime software v2020.1.2 (https://www.geneious.com). Pangolin v2.0 (Rambaut et al., 2020) was utilised for positioning of sequences within the global phylogenetic tree (lineages v2020-05-19). Median joining networks were created in Pop-ART (http://popart.otago.ac.nz/). Pairwise similarity analyses were performed using a bespoke script and calculated the number of exact matches in genomic variants between samples after adjusting for regions masked by low coverage. All high-quality genome sequences were shared with COG-UK (COVID-19 Genomics UK (COG-UK), 2020). Patient admissions and movement Request a detailed protocol We collected hospital admission data for all patients with high-quality sequenced genomes. For each patient, we identified other individuals within the cohort who were present on the same hospital wards on the same calendar day, leading to potential indirect or direct contacts between patients. This method for defining contacts assumes that close face-to-face contact is the most likely method for SARS-CoV-2 transmission between individuals and aims to identify individuals within our cohort who are most likely to have had such interactions. This assumption is supported through recent meta-analyses concluding that physical distancing of less than 1 m increases likelihood of SARS-CoV-2 transmission between individuals (Chu et al., 2020). We assessed all potential contacts for each patient in relation to the calendar day that the positive SARS-CoV-2 nasal or throat sample was collected from the patient. The windows of contacts are defined in accordance with national guidelines for SARS-CoV-2 nosocomial outbreak: community-acquired infection (positive test within 2 days of hospital admission); possible hospital-acquired infection (positive test 3–7 days after hospital admission); probable hospital-acquired infection (positive test 8–14 days after hospital admission); and definite hospital-acquired infection (positive test >14 days after hospital admission). For patient–patient contacts, we identified any potential contacts within the possible hospital-acquired infection period (3–7 days) and developed a binary matrix. We made additional assumptions for including HCWs in the contact networks, s

Background: Understanding the effectiveness of infection control methods in reducing and preventing SARS-CoV-2 transmission in healthcare settings is of high importance. Infection control is challenging in these environments due to regular contact between healthcare workers (HCWs) and patients. This is amplified by increased frequency of severe adverse responses to SARS-CoV-2 in patients with underlying health conditions. Methods: We sequenced SARS-CoV-2 genomes for patients and HCWs across multiple geographically distinct UK hospitals. All hospitals were actively enforcing zoning approaches (SARS-CoV-2 negative and SARS-CoV-2 positive areas) as an infection control measure. We integrated patient movement and staff location data into the analysis of viral genome data in order to understand geographical and temporal dynamics of SARS-CoV-2 transmission. Findings: We obtained 173 high-quality SARS-CoV-2 genomes from patients ( n =134) and HCWs ( n =39). The median number of genomic variants per sample of 11 (range=0-16), with a 61.5% average pairwise similarity in the variants (range=0-100%). Integration of patient movement identified eight patient contact clusters (PCC) with significantly increased similarity in genomic variants compared to non-clustered samples ( p <0.001). Incorporation of HCW location further increased the number of individuals within PCCs. Patients within PCCs carried viruses more genetically identical to HCWs in the same ward location ( p <0.001). Interpretation: SARS-CoV-2 genome sequencing integrated with patient and HCW movement data increases identification of outbreak clusters and improved understanding of the role of patient-HCW interactions. This dynamic approach to SARS-CoV-2 outbreak monitoring in a healthcare setting is able to support infection control management strategies within the healthcare setting. Funding: JME is funded by a postdoctoral research fellowship from Health Education England. WGN is supported by the Manchester NIHR BRC (IS-BRC-1215-20007).Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: The study was conducted to investigate hospital outbreak investigation/surveillance. Ethical approval was obtained from the Manchester Biomedical Research Centre COVID-19 rapid response group for viral genome analysis.