Viral sequencing has played an important role in developing our understanding of the emergence and evolution of the SARS-CoV-2 pandemic (Oude Munnink et al., 2021). Sequencing technologies can now be used for local outbreak investigation in near real time, and this was implemented by some research centres for evaluation of nosocomial transmission from the early stages of the pandemic (Meredith et al., 2020). It has been demonstrated that sequencing can provide additional information on outbreak characteristics and transmission routes in comparison to traditional epidemiological investigation alone (Meredith et al., 2020; Lucey et al., 2021; Snell et al., 2022b). However, limited data are available on the feasibility of routine use of sequencing for infection prevention and control (IPC), or on its direct impact on IPC actions and nosocomial transmission rates.

Throughout the pandemic, nosocomial transmission of SARS-CoV-2 has been a major concern (Abbas et al., 2021), with hospital-acquired infections (HAIs) accounting for more than 5% of lab-confirmed cases from March to August 2020 in the UK (Bhattacharya et al., 2021) and representing 11% of COVID-19 cases within hospitals in this period (Read et al., 2021). HAIs also frequently occur within a very vulnerable population with high levels of mortality (Bhattacharya et al., 2021; Oliver, 2021b; Ponsford et al., 2021). There is therefore an unmet need to develop interventions that can reduce the occurrence of nosocomial transmission. The aims of this study were to determine the effectiveness of SARS-CoV-2 sequencing in informing acute IPC actions and reducing the incidence of HAIs when used prospectively in routine practice, and to record the impact of sequencing reports on the actions of IPC teams.

When this study was planned in the summer of 2020, there was imperfect knowledge regarding the dominant mode of transmission of SARS-CoV-2 (Greenhalgh et al., 2021), and it was not possible to predict the future course of the pandemic. In conducting this study, substantial difficulties were encountered in implementing the intervention and in responding effectively to any insights generated. As such, this report serves as a record of the challenge of conducting research within a pandemic as well as being a conventional study summary report.

We conducted a prospective multiphase non-randomised trial to evaluate the implementation and impact of SARS-CoV-2 sequencing for IPC within 14 acute NHS hospital groups in the UK. All sites were linked to a COG-UK sequencing hub, 13 were university hospitals and 1 a district general hospital. We implemented a bespoke sequence reporting tool (SRT) intervention, developed and previously evaluated for this study (Stirrup et al., 2021), and assessed the importance of turnaround time for sequencing and reporting. The study included integral health economic and qualitative process evaluation (Flowers et al., 2021).

The study design comprised a planned 4-week baseline data collection period, followed by intervention periods defined by the time from diagnostic sampling to return of sequence data to IPC teams, comprising 8 weeks of ‘rapid’ (<48 hr) turnaround sequencing and 4 weeks of ‘longer’ (5–10 days) turnaround sequencing for each site. Target turnaround time was 48 hr from diagnostic sampling to return of the SRT report during the ‘rapid’ sequencing phase, and 5–10 days for the ‘longer-turnaround’ phase. Eight sites implemented ‘rapid’ followed by ‘longer-turnaround’ phases with five doing the opposite. One site did not implement longer-turnaround sequencing because they considered it a reduction in their standard practice, comprising outbreak sequencing with weekly meetings to discuss phylogenetic analyses; they nonetheless completed the baseline phase of the study without use of the SRT or automated feedback to IPC teams on all hospital-onset COVID-19 infection (HOCI) cases. The order of the intervention phases was pragmatically determined in some sites by the need to first run the ‘longer-turnaround’ phase to develop sample transport and sequencing procedures before attempting the ‘rapid’ sequencing phase, and the ordering was decided in the remaining sites to ensure a mixture of intervention phases over calendar time – there was no randomisation process in deciding the order of study phases.

Data were recorded in all phases for all patients meeting the definition of a HOCI, that is, first confirmed test for SARS-CoV-2 >48 hr after admission and without suspicion of COVID-19 at the time of admission. During the intervention phases, and for at least 3 weeks prior to any intervention period to enable linkage to recent cases, participating sites aimed to sequence all SARS-CoV-2 cases including both HOCI and non-HOCI cases.

The SRT aimed to integrate sequence and patient data to produce concise and immediately interpretable feedback about cases to IPC teams via a one-page report. Sites were also able to apply other methods (e.g. phylogenetics) to the sequence data generated, where this was part of their usual practice. Guidance regarding IPC actions was not specified as part of this study. Sites were expected to follow current national guidelines, which evolved throughout the course of the pandemic. Sequencing data from healthcare workers (HCWs) could be utilised in the SRT system, and this was implemented by 8/14 sites. Whether this was done depended on availability of HCW samples for each lab as staff testing was generally managed separately to patient testing. HCW testing protocols followed national guidelines.

Data collection on patient characteristics and on implementation and impact of the intervention was conducted using a central study-specific database. Ethical approval for the study was granted by NHS HRA (REC 20/EE/0118), and the study was prospectively registered (ClinicalTrials.gov Identifier: NCT04405934).

The primary outcomes of the study as defined in the protocol (Blackstone et al., 2022) were (1) incidence of IPC-defined SARS-CoV-2 HAIs per week per 100 currently admitted non-COVID-19 inpatients, and (2) for each HOCI, identification of linkage to individuals within an outbreak of SARS-CoV-2 nosocomial transmission using sequencing data as interpreted through the SRT that was not identified by pre-sequencing IPC evaluation during intervention phases. The second outcome used all observed HOCI cases as the denominator, and so represented the proportion of cases in which sequencing provided information regarding potential transmission routes where none had been previously uncovered.

Secondary outcomes were (1) incidence of IPC-defined SARS-CoV-2 hospital outbreaks per week per 1000 non-COVID-19 inpatients; (2) for each HOCI, any change to IPC actions following receipt of SRT report during intervention phases; and (3) any recommended change to IPC actions (regardless of whether changes were implemented). There was considered to be an impact on IPC actions if this was recorded for any of a number of predefined outcomes (e.g. enhanced cleaning, visitor and staffing restrictions, provision of personal protective equipment), or if it was stated that the report had effected any change to IPC practice on that ward or elsewhere within the hospital. The proportion of HOCI cases for which IPC reported the SRT report to be ‘useful’ was added as a further outcome.

To support standardisation across sites, ‘IPC-defined SARS-CoV-2 HAIs’ were considered to be all HOCIs with ≥8 days from admission to symptom onset (if known) or sample date (i.e. UK Health Security Agency definition of a probable/definite HAI; Public Health England, 2020).

An IPC-defined SARS-CoV-2 hospital outbreak was defined as at least two HOCI cases on the same ward, with at least one having ≥8 days from admission to symptom onset or sample date. Outbreak events were considered to be concluded once there was a period of 28 days prior to observation of another HOCI (Public Health England, 2020).

Further details of outcome definitions are given in Appendix 1.

A total of 2170 HOCIs were recorded for the study between 15 October 2020 and 26 April 2021. These cases had median age of 76.7 (interquartile range [IQR] 64.4–85.6) years, and 80% had at least one clinically significant comorbidity (Table 1).

All 14 sites completed baseline and rapid sequencing intervention phases (Appendix 1—figure 1). Thirteen sites completed the longer-turnaround sequencing intervention phase. 49.2% (650/1320) SRT reports for HOCIs were returned in the intervention phases, with only 9.3% (123/1320) returned within the target time frames (Table 2). This figure was greater in the longer-turnaround phase at 21.2% (79/373) than in the rapid phase (4.6%; 44/947). The median turnaround time from diagnostic sampling for reports returned was 5 days in the rapid phase and 13 days in the longer-turnaround phase, substantially longer than the targets of 48 hr and 5–10 days, respectively. A detailed breakdown of reporting turnaround times is reported separately (Colton et al., 2022).

Ordering the sites by proportion of cases with sequencing results returned and median turnaround time during the rapid phase (Figure 1) identified no obvious clustering of highest vs. lowest performing sites. We therefore also carried out a ’per protocol’ sensitivity analysis on the seven highest performing sites; these sites returned ≥40% of SRTs within a median time from diagnostic sample of ≤8 days within their rapid phase. The criteria for this analysis were decided after data collection but prior to data analysis, as per the statistical analysis plan (SAP). However, we acknowledge that the ‘higher performing sites’ did not meet the target turnaround time for reporting in the rapid phase; criteria were therefore set to split the sites into upper and lower 50% based on the level of implementation.

Figure 1

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We did not detect a statistically significant change in weekly incidence of HAIs in the longer-turnaround (incidence rate ratio 1.60, 95% CI 0.85–3.01; p=0.14) or rapid (0.85, 0.48–1.50; 0.54) intervention phases in comparison to baseline phase across the 14 sites (Table 3), and incidence rate ratios were comparable in our ‘per protocol’ analysis. Similarly, there was only weak evidence for an effect of phase on incidence of outbreaks in both intention-to-treat and ‘per protocol’ analyses, with wide confidence intervals inclusive of no difference in incidence (Table 3).

We compared HOCI-level impacts of the sequence report between phases. Nosocomial linkage to other individual cases, where initial IPC investigation had not correctly identified any such linkage, was identified in 6.7 and 6.8% of all HOCI cases in the rapid and longer-turnaround phases, respectively (OR for ‘rapid vs. longer-turnaround’ 0.98, 95% CI 0.46–2.08; p=0.95) (Table 2) and in 11.4 and 12.6% respectively of cases where the report was returned. For 25 cases in the rapid and 5 cases in the longer-turnaround phase, phylogenetic trees were used for sequences with <90% genome coverage, with 3 from the rapid phase showing previously unidentified linkage.

IPC practices were changed in 7.8 and 7.4% of all HOCI cases in the rapid and longer-turnaround phases, respectively (OR for ‘rapid vs. longer-turnaround’ 1.07, 0.34–3.38; p=0.90), and 17.2 and 11.6%, respectively, of cases where the report was returned. No one specific change to IPC action dominated those recorded among the options included within study reporting forms (Appendix 1—table 2, Appendix 1—table 3). When restricted to higher performing sites (i.e. ‘per protocol’), IPC practice was changed in a greater proportion of all HOCI cases in the rapid (9.9%) in comparison to the longer-turnaround (0.7%) sequencing phase (OR for ‘rapid vs. longer-turnaround’ 15.55, 1.30–1.85; p=0.01) and 16.7 and 1.1%, respectively, of cases where SRT reports were returned. The impact of phase on detecting nosocomial linkage was similar.

IPC teams more commonly reported finding the sequence reports useful in the rapid sequencing, 303/428 (70.8%) compared to the longer-turnaround phase, 107/215 (49.8%) (although this association was reversed on analysis within the multi-level mode specified, OR 0.82 rapid vs. longer-turnaround, 0.12–5.46; p=0.82), and the difference was more pronounced in the ‘per protocol’ analysis (79.0 vs. 27.2%, respectively; OR 3.44, 0.28–42.61; p=0.41). We explored this association further using the actual time to return of the reports, going beyond the analyses pre-specified in the SAP (Figure 2). In the ‘per protocol’ analysis, an impact on IPC actions was observed in 20.7% (45/217) of HOCI cases in which the SRT report was returned within 5 days, but in very few cases beyond this, with this trend less apparent when data from all sites were considered. Figure 2 also displays a strong decline in reported usefulness of the SRT with increasing turnaround time, both across all sites and in the ‘per protocol’ analysis. Sequence reports were considered useful in 79.1% (182/230) of cases if returned within 5 days for all sites (169/216, 78%, in ‘per protocol’ analysis). However, we note that many of the HOCI cases with SRT returned within 5 days were from a single study site, and some sites did not seem to have clearly differentiated ‘useful’ SRT reports when completing data collection (Appendix 1—figure 2 and Appendix 1—figure 3).

Figure 2

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SRT reports suggested that 91.3% of HOCI patients had acquired their infection post-admission (580/635, Table 2). In 91.9%, (589/641, Appendix 1—table 2) of cases, the reports were interpreted as supportive of IPC actions already taken. SRT reports also suggested post-admission infection in the majority of indeterminate HAIs (diagnosed 3–7 days from admission) (176/223, 78.9%).

Our analysis models reveal important findings beyond the effect of the intervention. The analysis model for the incidence of HAIs identified independent positive associations with the proportion of current SARS-CoV-2-positive inpatients, the local community incidence of new SARS-CoV-2 cases (which peaked from December 2020 to January 2021, Appendix 1—figure 4 and Appendix 1—figure 5), and calendar time (modelled as ‘study week’). Adding the proportion of local community cases that were Alpha (lineage B.1.1.7) variant did not lead to a statistically significant improvement in model fit (p=0.78). The observed weekly HOCI incidence rates varied substantially from 0 to 7.6 per 100 SARS-CoV-2 negative inpatients, with peaks aligning with those for local community incidence (Appendix 1—figure 4).

From modelling outbreaks, positive associations were similarly found for both hospital prevalence and community incidence of SARS-CoV-2 (Appendix 1—figure 6). The median number of HOCIs per IPC-defined outbreak event was four, with the largest observed outbreak including 43 HOCIs (Appendix 1—table 1).

Extensive qualitative analyses (Mapp et al., under review; Flowers et al., 2022) found high levels of acceptability for the SRT sequencing reports, which supported decision-making about IPC activity (e.g. stand down some IPC actions or continue as planned). In several sites, the major barriers to embedding and normalising the SRT within existing systems and processes were overcome. The SRT did provide new and valued insights into transmission events, outbreaks, and wider hospital functioning but mainly acted to offer confirmation and reassurance to IPC teams. Critically, given the context of the study within the pandemic timeline, the capacity to generate and respond to these insights effectively on a case-by-case basis was breached in most sites by the volume of HOCIs, and the limits of finite human and physical resource (e.g. hospital layout).

This study constitutes the largest prospective multicentre evaluation study of viral whole-genome sequencing (WGS) for acute IPC investigation of nosocomial transmission conducted to date. The study was run as part of routine practice within the NHS, and the challenges faced in implementing the intervention reflected the context and barriers in winter 2020–2021 in the UK. We did not demonstrate a direct impact of sequencing on the primary outcome of the incidence of HAIs, either on full analysis or when restricted to the higher performing sites, and the overall proportion of cases with nosocomial transmission linkage identified using sequencing that had been missed by IPC investigation was <10% in the intervention phases. However, on post hoc exploratory investigation among those sites with the most effective implementation of the sequencing intervention we showed that feedback within 5 days of diagnosis allowed for maximal impact on IPC actions. IPC teams, particularly in the ‘per protocol’ analysis, were almost all positive in their perception of the utility of viral sequencing for outbreak investigation.

The study was undertaken during a period of extreme strain on the NHS, with hospitals described as being ‘in the eye of a COVID-19 storm’ (Oliver, 2021a). Sites reported that they lacked the additional resources, in terms of staff and bed space, needed to respond effectively to insights generated by sequencing. Furthermore, if the study were repeated now then IPC teams would have more evidence-backed tools at their disposal, such as increasing respirator usage. As such, we do not believe that the null result for the impact on incidence of nosocomial transmission should be taken as strong evidence for a general lack of effectiveness of viral WGS for IPC.

Outbreak investigations are inherently complex and must take account of uncertainty regarding transmission links, even in the presence of high-quality genomic data (Lindsey et al., 2022). Interventions centred on IPC practices often need to be evaluated at the hospital level in order to allow for impacts on transmission across an institution as a whole (O’Hara et al., 2019), meaning that large multicentre studies are required to generate high-quality evidence. Standardisation of data collection with complex structures across multiple hospital sites is a considerable challenge. A review of IPC practice guidelines conducted prior to the SARS-CoV-2 pandemic found that most recommendations were based on evidence from descriptive studies, expert opinion, and other low-quality evidence (Mitchell et al., 2020).

The use of viral WGS for public health surveillance has become firmly established in the UK for SARS-CoV-2 (The COVID-19 Genomics UK consortium, 2020). This enabled early detection of the increased transmissibility and health impact of the Alpha variant (Volz et al., 2021) and subsequent monitoring of the Delta (Mishra et al., 2021) and Omicron variants (Andrews et al., 2022). However, whilst viral WGS for acute outbreak investigation has been shown for both SARS-CoV-2 (Meredith et al., 2020; Snell et al., 2022b; Lumley et al., 2021; Li et al., 2021) and other viruses (Brown et al., 2019; Houldcroft et al., 2018; Roy et al., 2019) to better identify sources of hospital-acquired infections and transmission chains, its impact on the management and outcome of nosocomial infection has not previously been quantified. Our study provides a substantial body of evidence regarding the introduction of viral WGS into routine IPC practice, its potential usage for outbreak management, and the challenges that need to be overcome to achieve implementation across the UK.

There are several limitations that may have impacted the results of this study. The study was conducted between October 2020 and April 2021. In this period, the local community incidence for the study sites ranged from <50 to >1200 weekly cases per 100,000 people. There were corresponding large variations in the healthcare burden of COVID-19, with several sites recording weeks when more than half of all inpatients were SARS-CoV-2 positive. High community infection rates and associated increases in the incidence of HOCI cases contributed to difficulties for site research teams in generating good quality viral sequences and reports for all HOCI cases within target time frames.

Our qualitative analyses also found that the capacity of sites to react to information generated by the sequencing intervention was breached by the volume of HOCI and admitted COVID-19 cases (“we’ve been basically deluged”, IPC staff) in combination with the finite personnel resources and limited physical space for isolation that was available (“The trouble is when you have so many wards going down and such a high prevalence of COVID, your actions are kind of the same regardless”, IPC staff). It may therefore be more achievable to develop effective systems for rapid viral WGS and feedback for endemic respiratory viruses at lower and more consistent levels, and more timely reporting of results might be associated with greater impact on IPC actions. As well as acute changes to IPC actions, there is the potential for routine pathogen sequencing to allow prospective IPC practice and policies to be refined. This could enable a longer-term reduction in the incidence of nosocomial infection at any given site, and such effects would be less dependent on turnaround time of sequencing in any given case. However, the capacity of sites to make such informed adjustments to IPC practice was limited during peaks in incidence of SARS-CoV-2 over the time scale of this study.

Planning this study and developing the data collection forms during the early stages of a novel viral pandemic was challenging, as in the summer of 2020 there were still ongoing debates around the primary mode of viral transmission and optimal IPC practice, and global supply chains for personal protective equipment were strained. In the planning of an equivalent study now, there would be a greater focus on adjustments to ventilation (Allen and Ibrahim, 2021), air filtration (Conway Morris et al., 2022), and respirator (Ferris et al., 2021) usage. It would also be possible to be more prescriptive and standardised regarding the recommended changes to IPC practice in response to sequencing findings, with the potential that our improved knowledge and available tools might facilitate a measurable impact on the incidence of nosocomial transmission.

The peak in SARS-CoV-2 levels from December 2020 to January 2021 corresponded to the rise of the highly transmissible Alpha variant in the UK (Volz et al., 2021). We did not find that the local prevalence of the Alpha variant was associated with the incidence rate of HAIs, beyond any effect mediated by higher community incidence. This matches the conclusions of a previously reported sub-study analysis using data from our sites (Boshier et al., 2021).

The study intervention made use of a bespoke SRT (Stirrup et al., 2021). The SRT combined both patient metadata and sequencing data, providing a single-page, easily interpretable report for IPC teams. It also facilitated standardisation of data collection across sites. Interestingly, while HOCIs diagnosed 3–7 days after admission are generally excluded from assessments of nosocomial SARS-CoV-2 infections (Oliver, 2021b), because of difficulty in distinguishing them from community-acquired infections, the SRT reported the majority (78.9%) of these indeterminate HAIs as being hospital-acquired. This confirms findings from a retrospective study using genomic linkage (Lumley et al., 2021), and may reflect a shorter incubation time for the Alpha variant compared to earlier variants (Snell et al., 2022a; although this remains uncertain; Blanquart et al., 2022), indicating that definitions used for monitoring and reporting may need to be kept under active review. Variants with shorter incubation times would lead to a greater importance for the rapidity of feedback in informing adjustments to IPC actions.

A number of limitations of the SRT were recognised, and work is ongoing to rectify these for future studies. The SRT’s probability calculations did not include patient and HCW movements. The SRT gave feedback on cases that could plausibly form part of the same outbreak but did not identify direct transmission pairs or networks, as has been done in other studies (Lindsey et al., 2022; Illingworth et al., 2022). HCW sequencing data could not be incorporated at all sites due to logistical and data management and access constraints. Implementation of an improved tool with these features might help to better identify routes of transmission within a hospital that could be interrupted, for example, through changes to the management of ward transfers for patients, isolation policies or identification of areas within the hospital linked to high risk of transmission. Finally, samples with less than 90% genome coverage were not included within the reporting system, despite the fact that they may still be useful for phylogenetic analyses.

The study sites varied in their ability to process sequence and metadata and generate and distribute reports in a timely manner (Figure 1), and the targeted turnaround times for reporting were not achieved at any of the sites for the majority of HOCIs in either the ‘rapid’ or ‘longer turnaround’ phases. Sites that had established teams with existing genomics expertise and on-site sequencing facilities were generally more successful at implementing the SRT into clinical practice (Mapp et al., under review). There is a need to focus on how sequencing and reporting processes can be integrated within local infrastructure and tailoring of local processes to ensure clear chains of communication from diagnostic labs through to the IPC team. Precisely understanding the barriers to achieving rapid turnaround times is key to future IPC use of viral WGS and is currently being analysed in a follow-up secondary analysis. Standardising and automating more of the SRT production pipeline will also help reduce the implementation burden at sites.

The study covered a period in which a national vaccination programme was initiated for HCWs and the elderly population in the UK, commencing with those ≥80 years from 8 December 2020. We had planned to include data on the proportion of HCWs who had received at least one vaccine dose as a variable in the analysis of incidence outcomes. This was subsequently not included because data was only available from 10 sites, for which rollout of HCW vaccination was broadly consistent. As such, any effect of HCW or patient vaccination on the incidence outcomes would form part of the estimated association with calendar time.

With the sequencing technology now available and high levels of interest in viral genomics for public health, there is the potential to incorporate viral WGS into routine IPC practice. Many publications have already highlighted the utility of viral sequence data for changing IPC policy and auditing the management of outbreaks (Meredith et al., 2020; Snell et al., 2022b; Lumley et al., 2021; Li et al., 2021; Brown et al., 2019; Houldcroft et al., 2018; Roy et al., 2019). We did not demonstrate an effect of our sequencing intervention on our primary outcome of the incidence of HAIS, and there were challenges in the implementation of the intervention. However, our study provides the first prospective evidence that with faster turnaround times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; on post hoc exploratory analysis results returned within ≤5 days from sampling to result changed the actions of IPC teams in around 20% of cases. The SRT, by rapidly combining sequence and patient metadata, was also better able than standard IPC definitions alone to distinguish hospital and community-acquired infections within a clinically relevant time scale. The difference in the cost of rapid compared with longer-turnaround hospital sample sequencing is low relative to the overall cost level at present (Appendix 1—table 4). Assuming SARS-CoV-2 sequencing for public health purposes continues, the added cost of rapid sequencing for IPC purposes could potentially be offset by the benefits accrued.

While we did not show an impact of sequencing on the numbers of HAIs or outbreaks, the evidence that these correlated with the high community SARS-CoV-2 rates suggests that factors beyond the control of IPC were influential. Our study nonetheless provides valuable evidence regarding the implementation and utility of this technology for IPC, and potentially it will have a greater positive impact on IPC practice outside of the burdens and resource constraints imposed by a pandemic. Importantly for future research, we provide a wealth of data on why the study worked better at some sites than others, and the challenges that would need to be overcome to make full use of viral genome sequencing for IPC practice more widely. It remains to be demonstrated that viral sequencing can have a direct impact on clinical outcomes such as the incidence of HAIs, and further prospective studies with refined implementation of similar interventions are required to address this.

Appendix 1 contains further details of statistical analysis methods, and supplementary figures and tables describing additional study data.

menbreg n_HAIs i.study_phase_analysis prop_cov_sp_* study_week_sp_* community_inc_sp_*, exposure(exposure_pw) || site_anon: || study_phase:, irr dispersion(constant)

Results

Appendix 1—figure 1

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Appendix 1—figure 2

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Appendix 1—figure 3

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Appendix 1—figure 4

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Appendix 1—figure 5

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Appendix 1—figure 6

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Appendix 1—figure 7

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Appendix 1—table 1

	Study phase			Total
	Baseline	Longer-turnaround	Rapid
IPC-defined outbreak events
n outbreak events	129	33	114	276
n/N (%) of HOCI cases part of outbreak event	682/850 (80.2)	314/373 (84.2)	763/947 (80.6)	1759/2170 (81.1)
Number of HOCIs per outbreak event, median (IQR, range)	5.0 (3–8, 2–43)	5.0 (3–9, 2–24)	4.0 (2–7, 2–31)	4.0 (2–8, 2–43)
Proportion of HCWs on sick leave due to COVID-19, median (IQR, range) [n]	0.13 (0.00–0.35, 0.00–0.50) [13]	0.05 (0.00–0.18, 0.00–0.30) [7]	0.20 (0.08–0.33, 0.00–0.89) [14]	0.13 (0.00–0.31, 0.00–0.89) [34]
IPC + sequencing-defined outbreak events
n outbreak events	–	41	135	176
n/N (%) of HOCI cases part of outbreak event	–	292/373 (78.3)	705/947 (74.4)	997/1320 (75.5)
Number of HOCIs per outbreak event, median (IQR, range)	–	5.0 (2-8, 2-23)	3.0 (2-6, 2-29)	3.0 (2-7, 2-29)
For first HOCI in outbreak:
SRT changed IPC practice, n/N (%, 95% CI)	–	4/41 (10.4, 0–21.0)	19/133 (14.9, 6.6–23.2)	23/174 (13.2)
SRT changed IPC practice for ward, n/N (%)	–	2/35 (5.7)	6/82 (7.3)	8/117 (6.8)
SRT used in IPC decisions beyond ward, n/N (%)	–	2/35 (5.7)	10/82 (12.2)	12/117 (10.3)
IPC team reported SRT to be useful, n/N (%)
Yes	–	20/35 (57.1)	51/82 (62.2)	71/117 (60.7)
No	–	9/35 (25.7)	15/82 (18.3)	24/117 (20.5)
Unsure	–	6/35 (17.1)	16/82 (19.5)	22/117 (18.8)
SRT would ideally have changed IPC practice, n/N (%*, 95% CI)	–	4/41 (9.8)	19/133 (14.3)	23/174 (13.2)

1. Odds ratio of SRT changed IPC practice for ‘rapid vs. longer-turnaround’ phases 1.54 (95% CI 0.37–6.44; p=0.52).

2. HCW, healthcare worker; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; SRT, sequence reporting tool.

3. *

   Estimated marginal value from mixed effects model, not raw %, evaluated on intention-to-treat basis with lack of SRT report classified as ‘No’.

Appendix 1—table 2

	Study phase
	Longer-turnaround sequencing			Rapid turnaround sequencing
N HOCI cases	373			947
Review of IPC actions already taken	Support	Refute	Missing	Support	Refute	Missing
SRT results support or refute IPC actions already taken*	200/213 (93.9)	7/213 (3.3)	2	389/428 (90.9)	9/428 (2.1)	7
Changes to IPC practice following SRT	To enhanced	To routine	No change	To enhanced	To routine	No change
Change to cleaning protocols on ward	2/185 (1.1)	0/185 (0.0)	183/185 (98.9)	7/341 (2.1)	0/341 (0.0)	334/341 (97.9)
	To greater	To fewer	No change	To greater	To fewer	No change
Change to visitor restrictions	1/186 (0.5)	0/186 (0.0)	185/186 (99.5)	1/340 (0.3)	0/340 (0.0)	339/340 (99.7)
	To ‘cohort nursing’	To ‘other restrictions’	No change	To ‘cohort nursing’	To ‘other restrictions’	No change
Change to staffing restrictions on ward	0/186 (0.0)	1/186 (0.5)	185/186 (99.5)	0/336 (0.0)	1/336 (0.3)	335/336 (99.7)
	Increase	Decrease	No change	Increase	Decrease	No change
Hand hygiene audit frequency	1/185 (0.5)	0/185 (0.0)	184/185 (99.5)	10/335 (3.0)	0/335 (0.0)	325/335 (97.0)
IPC staff visits to ward	1/185 (0.5)	0/185 (0.0)	184/185 (99.5)	14/335 (4.2)	0/335 (0.0)	321/335 (95.8)
Assessment of alcogel stocks	0/185 (0.0)	0/185 (0.0)	185/185 (100.0)	2/335 (0.6)	0/335 (0.0)	333/335 (99.4)
Assessment of soap stocks	0/185 (0.0)	0/185 (0.0)	185/185 (100.0)	2/334 (0.6)	0/334 (0.0)	332/334 (99.4)
Assessment of aseptic non-touch technique compliance	0/185 (0.0)	0/185 (0.0)	185/185 (100.0)	8/335 (2.4)	0/335 (0.0)	327/335 (97.6)
Assessment of PPE supply	1/185 (0.5)	0/185 (0.0)	184/185 (99.5)	9/336 (2.7)	0/336 (0.0)	327/336 (97.3)
Availability of doffing and donning buddy	0/185 (0.0)	0/185 (0.0)	185/185 (100.0)	1/333 (0.3)	0/333 (0.0)	332/333 (99.7)
IPC signage assessment	1/185 (0.5)	0/185 (0.0)	184/185 (99.5)	12/336 (3.6)	0/336 (0.0)	324/336 (96.4)
IPC signage implementation	1/185 (0.5)	0/185 (0.0)	184/185 (99.5)	11/336 (3.3)	0/336 (0.0)	325/336 (96.7)
Training on IPC procedures	0/185 (0.0)	0/185 (0.0)	185/185 (100.0)	8/336 (2.4)	0/336 (0.0)	328/336 (97.6)

1. Data shown as n or n/N (%). Overall impact on IPC actions per HOCI case is given in Table 2.

2. HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; PPE, personal protective equipment; SRT, sequence reporting tool.

3. *

   Sites could select ‘Yes’ or ‘No’ for both ‘Support’ and ‘Refute’ as these were entered as separate data items.

Appendix 1—table 3

	Study phase
	Longer-turnaround sequencing			Rapid turnaround sequencing
N HOCI cases	41			135
Review of IPC actions already taken	Support	Refute	Missing	Support	Refute	Missing
SRT results support or refute IPC actions already taken*	30/35 (85.7)	3/35 (8.6)	0	71/82 (86.6)	5/82 (6.1)	2
Changes to IPC practice following SRT	To enhanced	To routine	No change	To enhanced	To routine	No change
Change to cleaning protocols on ward	1/34 (2.9)	0/34 (0.0)	33/34 (97.1)	1/70 (1.4)	0/70 (0.0)	69/70 (98.6)
	To greater	To fewer	No change	To greater	To fewer	No change
Change to visitor restrictions	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	1/70 (1.4)	0/70 (0.0)	69/70 (98.6)
	To ‘cohort nursing’	To ‘other restrictions’	No change	To ‘cohort nursing’	To ‘other restrictions’	No change
Change to staffing restrictions on ward	0/34 (0.0)	1/34 (2.9)	33/34 (97.1)	0/70 (0.0)	1/70 (1.4)	69/70 (98.6)
	Increase	Decrease	No change	Increase	Decrease	No change
Hand hygiene audit frequency	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	3/70 (4.3)	0/70 (0.0)	67/70 (95.7)
IPC staff visits to ward	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	5/70 (7.1)	0/70 (0.0)	65/70 (92.9)
Assessment of alcogel stocks	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	2/70 (2.9)	0/70 (0.0)	68/70 (97.1)
Assessment of soap stocks	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	2/70 (2.9)	0/70 (0.0)	68/70 (97.1)
Assessment of aseptic non-touch technique compliance	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	3/70 (4.3)	0/70 (0.0)	67/70 (95.7)
Assessment of PPE supply	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	3/70 (4.3)	0/70 (0.0)	67/70 (95.7)
Availability of doffing and donning buddy	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	1/70 (1.4)	0/70 (0.0)	69/70 (98.6)
IPC signage assessment	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	4/70 (5.7)	0/70 (0.0)	66/70 (94.3)
IPC signage implementation	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	4/70 (5.7)	0/70 (0.0)	66/70 (94.3)
Training on IPC procedures	0/34 (0.0)	0/34 (0.0)	34/34 (100.0)	4/70 (5.7)	0/70 (0.0)	66/70 (94.3)

1. Data shown as n or n/N (%). Overall impact on IPC actions per HOCI case is given in Appendix 1—table 1.

2. HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; PPE, personal protective equipment; SRT, sequence reporting tool.

3. *

   Sites could select ‘Yes’ or ‘No’ for both ‘Support’ and ‘Refute’ as these were entered as separate data items.

Appendix 1—table 4

Laboratories	Lab 1	Lab 2	Lab 3	Lab 4	Lab 5	Lab 6	Lab 7	Lab 8	Lab 9	Lab 10
Rapid turnaround sequencing
Sequencing platform	Illumina MiSeq	Nanopore MinION/ GridiON	Nanopore GridiON	Nanopore GridiON	Nanopore GridiON	Nanopore MinION/ GridiON	Nanopore GridiON	Nanopore GridiON	Illumina MiSeq	Illumina MiSeq	Mean
Batch size	24	24	24	96	24	24	24	24	96	96
Equipment	£45.11	£26.06	£19.34	£4.38	£12.38	£24.66	£11.99	£11.26	£5.91	£6.13	£16.72
Consumables	£69.14	£54.56	£87.07	£31.11	£79.06	£28.84	£62.09	£46.02	£14.37	£39.63	£51.19
Staff	£6.11	£20.25	£24.66	£7.93	£11.16	£5.66	£12.16	£8.45	£2.20	£3.45	£10.20
Total per-sample cost	£120.36	£100.87	£131.07	£43.43	£102.60	£59.17	£86.23	£65.73	£22.48	£49.21	£78.11
Total cost (including overheads calculated at 20%)	£144.43	£121.04	£157.28	£52.11	£123.12	£71.01	£103.48	£78.88	£26.97	£59.05	£93.74
Longer-turnaround sequencing
Sequencing platform	Illumina MiSeq	Nanopore MinION/ GridiON	Nanopore GridiON	Nanopore GridiON	Nanopore GridiON	Nanopore MinION/ GridiON	Nanopore GridiON	Nanopore GridiON	Nanopore MinION	Illumina MiSeq	Mean
Batch size	24	24	24	96	24	24	24	96	24	96
Equipment	£40.60	£22.15	£17.02	£3.94	£11.88	£22.44	£11.27	£2.81	£2.54	£5.76	£14.04
Consumables	£61.53	£48.56	£77.49	£27.69	£70.36	£25.67	£55.26	£11.51	£33.75	£35.27	£44.71
Staff	£4.95	£15.19	£16.52	£2.78	£2.23	£4.53	£12.04	£8.45	£11.85	£3.32	£8.19
Total per-sample cost	£107.08	£85.89	£111.03	£34.41	£84.48	£52.65	£78.56	£22.77	£48.13	£44.34	£66.94
Total cost (including overheads calculated at 20%)	£128.50	£103.07	£133.24	£41.29	£101.38	£63.18	£94.28	£27.33	£57.76	£53.21	£80.32

Appendix 1—table 5

	Study phase			IRR† (95% CI, p-value)
	Baseline	Longer-turnaround	Rapid	Longer-turnaround vs. baseline	Rapid vs. baseline
All sites
n HOCI cases	850	373	947	–	–
n IPC-defined HAIs	488	207	576	–	–
Weekly incidence of IPC-defined HAIs per 100 inpatients, mean (median, IQR, range)* [primary outcome]	1.0 (0.5, 0.0–1.4, 0.0–5.6)	0.7 (0.3, 0.0–0.7, 0.0–7.6) ‡	0.6 (0.3, 0.0–0.8, 0.0–5.3) ‡	0.49 (0.21–1.19, 0.12)	0.47 (0.21–1.08, 0.07)
n IPC-defined outbreak events	129	33	114	–	–
Weekly incidence of IPC-defined outbreak events per 100 inpatients, mean (median, IQR, range)*	0.3 (0.1, 0.0–0.4, 0.0–2.3)	0.1 (0.0, 0.0–0.1, 0.0–0.9) ‡	0.1 (0.0, 0.0–0.0, 0.0–0.9) ‡	0.25 (0.10–0.66, 0.005)	0.23 (0.10–0.54, 0.001)

1. IPC-defined HAIs are considered to be ‘probable’ or ‘definite’ HAIs.

2. HAI, hospital-acquired infection; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; IRR, incidence rate ratio.

3. *

   Descriptive data over all week-long periods at all study sites.

4. †

   Without adjustment for proportion of current inpatients at site that are COVID-19 cases, community incidence rate, and calendar time.

5. ‡

   Not including data from the first week of each intervention period, or in the week following any break in the intervention period.

A fully anonymised version of the dataset generated and analysed for this study is available on the UCL Research Data Repository (https://doi.org/10.5522/04/20769637.v1).

1. 1. Oliver S
   2. James B
   3. Andrew C
   4. Judith B

   (2022) Dyrad Digital Repository

   COG-UK hospital-onset COVID-19 infection study dataset.

   https://doi.org/10.5522/04/20769637.v1

1. 1. Abbas M
   2. Robalo Nunes T
   3. Martischang R
   4. Zingg W
   5. Iten A
   6. Pittet D
   7. Harbarth S

   (2021) Nosocomial transmission and outbreaks of coronavirus disease 2019: the need to protect both patients and healthcare workers

   Antimicrobial Resistance and Infection Control 10:7.

   https://doi.org/10.1186/s13756-020-00875-7

   • PubMed
   • Google Scholar
2. 1. Allen JG
   2. Ibrahim AM

   (2021) Indoor air changes and potential implications for SARS-cov-2 transmission

   JAMA 325:2112–2113.

   https://doi.org/10.1001/jama.2021.5053

   • PubMed
   • Google Scholar
3. 1. Andrews N
   2. Stowe J
   3. Kirsebom F
   4. Toffa S
   5. Rickeard T
   6. Gallagher E
   7. Gower C
   8. Kall M
   9. Groves N
   10. O’Connell AM
   11. Simons D
   12. Blomquist PB
   13. Zaidi A
   14. Nash S
   15. Iwani Binti Abdul Aziz N
   16. Thelwall S
   17. Dabrera G
   18. Myers R
   19. Amirthalingam G
   20. Gharbia S
   21. Barrett JC
   22. Elson R
   23. Ladhani SN
   24. Ferguson N
   25. Zambon M
   26. Campbell CNJ
   27. Brown K
   28. Hopkins S
   29. Chand M
   30. Ramsay M
   31. Lopez Bernal J

   (2022) Covid-19 vaccine effectiveness against the omicron (B.1.1.529) variant

   The New England Journal of Medicine 386:1532–1546.

   https://doi.org/10.1056/NEJMoa2119451

   • PubMed
   • Google Scholar
4. 1. Bhattacharya A
   2. Collin SM
   3. Stimson J
   4. Thelwall S
   5. Nsonwu O
   6. Gerver S
   7. Robotham J
   8. Wilcox M
   9. Hopkins S
   10. Hope R

   (2021) Healthcare-Associated COVID-19 in England: a national data linkage study

   The Journal of Infection 83:565–572.

   https://doi.org/10.1016/j.jinf.2021.08.039

   • PubMed
   • Google Scholar
5. 1. Blackstone J
   2. Stirrup O
   3. Mapp F
   4. Panca M
   5. Copas A
   6. Flowers P
   7. Hockey L
   8. Price J
   9. Partridge D
   10. Peters C
   11. de Silva T
   12. Nebbia G
   13. Snell LB
   14. McComish R
   15. Breuer J
   16. COVID-19 Genomics UK Consortium

   (2022) 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 UK NHS hospitals

   BMJ Open 12:e052514.

   https://doi.org/10.1136/bmjopen-2021-052514

   • PubMed
   • Google Scholar
6. 1. Blanquart F
   2. Hozé N
   3. Cowling BJ
   4. Débarre F
   5. Cauchemez S

   (2022) Selection for infectivity profiles in slow and fast epidemics and the rise of SARS-cov-2 variants

   eLife 11:e75791.

   https://doi.org/10.7554/eLife.75791

   • Google Scholar
7. 1. Boshier FAT
   2. Venturini C
   3. Stirrup O
   4. Guerra-Assunção JA
   5. Alcolea-Medina A
   6. Becket AH
   7. Byott M
   8. Charalampous T
   9. Filipe ADS
   10. Frampton D
   11. Glaysher S
   12. Khan T
   13. Kulasegara-Shylini R
   14. Kele B
   15. Monahan IM
   16. Mollett G
   17. Parker M
   18. Pelosi E
   19. Randell P
   20. Roy S
   21. Taylor JF
   22. Weller SJ
   23. Wilson-Davies E
   24. Wade P
   25. Williams R
   26. Copas AJ
   27. Cutino-Moguel T
   28. Freemantle N
   29. Hayward AC
   30. Holmes A
   31. Hughes J
   32. Mahungu TW
   33. Nebbia G
   34. Nastouli E
   35. Partridge DG
   36. Pope CF
   37. Price JR
   38. Robson SC
   39. Saeed K
   40. Shin GY
   41. de Silva TI
   42. Snell LB
   43. Thomson EC
   44. Witney AA
   45. Breuer J
   46. COG-UK HOCI Variant Substudy consortium, The COVID-19 Genomics UK consortium

   (2021) The alpha variant was not associated with excess nosocomial SARS-cov-2 infection in a multi-centre UK hospital study

   The Journal of Infection 83:693–700.

   https://doi.org/10.1016/j.jinf.2021.09.022

   • PubMed
   • Google Scholar
8. 1. Brown JR
   2. Roy S
   3. Shah D
   4. Williams CA
   5. Williams R
   6. Dunn H
   7. Hartley J
   8. Harris K
   9. Breuer J

   (2019) Norovirus transmission dynamics in a pediatric hospital using full genome sequences

   Clinical Infectious Diseases 68:222–228.

   https://doi.org/10.1093/cid/ciy438

   • PubMed
   • Google Scholar
9. 1. Colton H
   2. Parker MD
   3. Stirrup O
   4. Blackstone J
   5. Loose M
   6. McClure CP
   7. Roy S
   8. Williams C
   9. McLeod J
   10. Smith D
   11. Taha Y
   12. Zhang P
   13. Hsu SN
   14. Kele B
   15. Harris K
   16. Mapp F
   17. Williams R
   18. COG-UK HOCI Investigators, The COVID-19 Genomics UK (COG-UK) Consortium
   19. Flowers P
   20. Breuer J
   21. Partridge DG
   22. de Silva TI

   (2022) Factors affecting turnaround time of SARS-cov-2 sequencing for inpatient infection prevention and control decision making: analysis of data from the COG-UK HOCI study

   The Journal of Hospital Infection S0195-6701(22)00318-8.

   https://doi.org/10.1016/j.jhin.2022.09.022

   • PubMed
   • Google Scholar
10. 1. Conway Morris A
    2. Sharrocks K
    3. Bousfield R
    4. Kermack L
    5. Maes M
    6. Higginson E
    7. Forrest S
    8. Pereira-Dias J
    9. Cormie C
    10. Old T
    11. Brooks S
    12. Hamed I
    13. Koenig A
    14. Turner A
    15. White P
    16. Floto RA
    17. Dougan G
    18. Gkrania-Klotsas E
    19. Gouliouris T
    20. Baker S
    21. Navapurkar V

    (2022) The removal of airborne severe acute respiratory syndrome coronavirus 2 (SARS-cov-2) and other microbial bioaerosols by air filtration on coronavirus disease 2019 (COVID-19) surge units

    Clinical Infectious Diseases 75:e97–e101.

    https://doi.org/10.1093/cid/ciab933

    • PubMed
    • Google Scholar
11. 1. Ferris M
    2. Ferris R
    3. Workman C
    4. O’Connor E
    5. Enoch DA
    6. Goldesgeyme E

    (2021) Efficacy of FFP3 respirators for prevention of SARS-cov-2 infection in healthcare workers

    eLife 10:e71131.

    https://doi.org/10.7554/eLife.71131

    • Google Scholar
12. Preprint

    1. Flowers P
    2. Mapp F
    3. Blackstone J
    4. Stirrup O
    5. Breuer J

    (2021) Developing Initial Programme Theory: COVID-19 Genomics UK Consortium Hospital-Onset COVID-19 Study (COG-UK HOCI)

    SocArXiv.

    https://doi.org/10.31235/osf.io/ysm35

    • Google Scholar
13. Preprint

    1. Flowers P
    2. McLeod J
    3. Mapp F
    4. Stirrup O
    5. Blackstone J
    6. Snell L

    (2022) How Acceptable Is Rapid Whole Genome Sequencing for Infectious Disease Management in Hospitals? Perspectives of Those Involved in Managing Nosocomial SARS-CoV-2

    medRxiv.

    https://doi.org/10.1101/2022.06.15.22276423

    • Google Scholar
14. 1. Greenhalgh T
    2. Jimenez JL
    3. Prather KA
    4. Tufekci Z
    5. Fisman D
    6. Schooley R

    (2021) Ten scientific reasons in support of airborne transmission of SARS-cov-2

    Lancet 397:1603–1605.

    https://doi.org/10.1016/S0140-6736(21)00869-2

    • PubMed
    • Google Scholar
15. 1. Houldcroft CJ
    2. Roy S
    3. Morfopoulou S
    4. Margetts BK
    5. Depledge DP
    6. Cudini J
    7. Shah D
    8. Brown JR
    9. Romero EY
    10. Williams R
    11. Cloutman-Green E
    12. Rao K
    13. Standing JF
    14. Hartley JC
    15. Breuer J

    (2018) Use of whole-genome sequencing of adenovirus in immunocompromised pediatric patients to identify nosocomial transmission and mixed-genotype infection

    The Journal of Infectious Diseases 218:1261–1271.

    https://doi.org/10.1093/infdis/jiy323

    • PubMed
    • Google Scholar
16. 1. Illingworth CJR
    2. Hamilton WL
    3. Jackson C
    4. Warne B
    5. Popay A
    6. Meredith L
    7. Hosmillo M
    8. Jahun A
    9. Fieldman T
    10. Routledge M
    11. Houldcroft CJ
    12. Caller L
    13. Caddy S
    14. Yakovleva A
    15. Hall G
    16. Khokhar FA
    17. Feltwell T
    18. Pinckert ML
    19. Georgana I
    20. Chaudhry Y
    21. Curran M
    22. Parmar S
    23. Sparkes D
    24. Rivett L
    25. Jones NK
    26. Sridhar S
    27. Forrest S
    28. Dymond T
    29. Grainger K
    30. Workman C
    31. Gkrania-Klotsas E
    32. Brown NM
    33. Weekes MP
    34. Baker S
    35. Peacock SJ
    36. Gouliouris T
    37. Goodfellow I
    38. Angelis DD
    39. Török ME

    (2022) A2B-COVID: a tool for rapidly evaluating potential SARS-cov-2 transmission events

    Molecular Biology and Evolution 39:msac025.

    https://doi.org/10.1093/molbev/msac025

    • PubMed
    • Google Scholar
17. 1. Kahan BC
    2. Rushton H
    3. Morris TP
    4. Daniel RM

    (2016) A comparison of methods to adjust for continuous covariates in the analysis of randomised trials

    BMC Medical Research Methodology 16:42.

    https://doi.org/10.1186/s12874-016-0141-3

    • PubMed
    • Google Scholar
18. 1. Li P
    2. Redden DT

    (2015) Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials

    BMC Medical Research Methodology 15:38.

    https://doi.org/10.1186/s12874-015-0026-x

    • PubMed
    • Google Scholar
19. 1. Li KK
    2. Woo YM
    3. Stirrup O
    4. Hughes J
    5. Ho A
    6. Filipe ADS
    7. Johnson N
    8. Smollett K
    9. Mair D
    10. Carmichael S
    11. Tong L
    12. Nichols J
    13. Aranday-Cortes E
    14. Brunker K
    15. Parr YA
    16. Nomikou K
    17. McDonald SE
    18. Niebel M
    19. Asamaphan P
    20. Sreenu VB
    21. Robertson DL
    22. Taggart A
    23. Jesudason N
    24. Shah R
    25. Shepherd J
    26. Singer J
    27. Taylor AHM
    28. Cousland Z
    29. Price J
    30. Lees JS
    31. Jones TPW
    32. Lopez CV
    33. MacLean A
    34. Starinskij I
    35. Gunson R
    36. Morris STW
    37. Thomson PC
    38. Geddes CC
    39. Traynor JP
    40. Breuer J
    41. Thomson EC
    42. Mark PB
    43. COVID-19 Genomics UK consortium

    (2021) Genetic epidemiology of SARS-cov-2 transmission in renal dialysis units-a high risk community-hospital interface

    The Journal of Infection 83:96–103.

    https://doi.org/10.1016/j.jinf.2021.04.020

    • PubMed
    • Google Scholar
20. 1. Lindsey BB
    2. Villabona-Arenas CJ
    3. Campbell F
    4. Keeley AJ
    5. Parker MD
    6. Shah DR
    7. Parsons H
    8. Zhang P
    9. Kakkar N
    10. Gallis M
    11. Foulkes BH
    12. Wolverson P
    13. Louka SF
    14. Christou S
    15. State A
    16. Johnson K
    17. Raza M
    18. Hsu S
    19. Jombart T
    20. Cori A
    21. Evans CM
    22. Partridge DG
    23. Atkins KE
    24. Hué S
    25. de Silva TI
    26. Sheffield COVID-19 Genomics Group
    27. COVID-19 Genomics UK consortium
    28. CMMID COVID-19 working group

    (2022) Characterising within-hospital sars-cov-2 transmission events using epidemiological and viral genomic data across two pandemic waves

    Nature Communications 13:671.

    https://doi.org/10.1038/s41467-022-28291-y

    • PubMed
    • Google Scholar
21. 1. Lucey M
    2. Macori G
    3. Mullane N
    4. Sutton-Fitzpatrick U
    5. Gonzalez G
    6. Coughlan S
    7. Purcell A
    8. Fenelon L
    9. Fanning S
    10. Schaffer K

    (2021) Whole-Genome sequencing to track severe acute respiratory syndrome coronavirus 2 (SARS-cov-2) transmission in nosocomial outbreaks

    Clinical Infectious Diseases 72:e727–e735.

    https://doi.org/10.1093/cid/ciaa1433

    • PubMed
    • Google Scholar
22. 1. Lumley SF
    2. Constantinides B
    3. Sanderson N
    4. Rodger G
    5. Street TL
    6. Swann J
    7. Chau KK
    8. O’Donnell D
    9. Warren F
    10. Hoosdally S
    11. O’Donnell A-M
    12. Walker TM
    13. Stoesser NE
    14. Butcher L
    15. Peto TE
    16. Crook DW
    17. Jeffery K
    18. Matthews PC
    19. Eyre DW
    20. OUH Microbiology laboratory
    21. OUH Infection Prevention and Control team

    (2021) Epidemiological data and genome sequencing reveals that nosocomial transmission of SARS-cov-2 is underestimated and mostly mediated by a small number of highly infectious individuals

    The Journal of Infection 83:473–482.

    https://doi.org/10.1016/j.jinf.2021.07.034

    • PubMed
    • Google Scholar
23. 1. Meredith LW
    2. Hamilton WL
    3. Warne B
    4. Houldcroft CJ
    5. Hosmillo M
    6. Jahun AS
    7. Curran MD
    8. Parmar S
    9. Caller LG
    10. Caddy SL
    11. Khokhar FA
    12. Yakovleva A
    13. Hall G
    14. Feltwell T
    15. Forrest S
    16. Sridhar S
    17. Weekes MP
    18. Baker S
    19. Brown N
    20. Moore E
    21. Popay A
    22. Roddick I
    23. Reacher M
    24. Gouliouris T
    25. Peacock SJ
    26. Dougan G
    27. Török ME
    28. Goodfellow I

    (2020) Rapid implementation of SARS-cov-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study

    The Lancet Infectious Diseases 20:1263–1272.

    https://doi.org/10.1016/S1473-3099(20)30562-4

    • PubMed
    • Google Scholar
24. 1. Mishra S
    2. Mindermann S
    3. Sharma M
    4. Whittaker C
    5. Mellan TA
    6. Wilton T
    7. Klapsa D
    8. Mate R
    9. Fritzsche M
    10. Zambon M
    11. Ahuja J
    12. Howes A
    13. Miscouridou X
    14. Nason GP
    15. Ratmann O
    16. Semenova E
    17. Leech G
    18. Sandkühler JF
    19. Rogers-Smith C
    20. Vollmer M
    21. Unwin HJT
    22. Gal Y
    23. Chand M
    24. Gandy A
    25. Martin J
    26. Volz E
    27. Ferguson NM
    28. Bhatt S
    29. Brauner JM
    30. Flaxman S
    31. COVID-19 Genomics UK Consortium

    (2021) Changing composition of SARS-cov-2 lineages and rise of delta variant in England

    EClinicalMedicine 39:101064.

    https://doi.org/10.1016/j.eclinm.2021.101064

    • PubMed
    • Google Scholar
25. 1. Mitchell BG
    2. Fasugba O
    3. Russo PL

    (2020) Where is the strength of evidence? A review of infection prevention and control guidelines

    The Journal of Hospital Infection 105:242–251.

    https://doi.org/10.1016/j.jhin.2020.01.008

    • PubMed
    • Google Scholar
26. 1. O’Hara LM
    2. Blanco N
    3. Leekha S
    4. Stafford KA
    5. Slobogean GP
    6. Ludeman E
    7. Harris AD
    8. CDC Prevention Epicenters Program

    (2019) Design, implementation, and analysis considerations for cluster-randomized trials in infection control and hospital epidemiology: a systematic review

    Infection Control and Hospital Epidemiology 40:686–692.

    https://doi.org/10.1017/ice.2019.48

    • PubMed
    • Google Scholar
27. 1. Oliver D

    (2021a) David oliver: could we do better on hospital acquired covid-19 in a future wave?

    BMJ 372:70.

    https://doi.org/10.1136/bmj.n70

    • PubMed
    • Google Scholar
28. 1. Oliver D

    (2021b) David oliver: deaths from hospital acquired covid are everyone’s problem

    BMJ 373:1492.

    https://doi.org/10.1136/bmj.n1492

    • PubMed
    • Google Scholar
29. 1. Oude Munnink BB
    2. Worp N
    3. Nieuwenhuijse DF
    4. Sikkema RS
    5. Haagmans B
    6. Fouchier RAM
    7. Koopmans M

    (2021) The next phase of SARS-cov-2 surveillance: real-time molecular epidemiology

    Nature Medicine 27:1518–1524.

    https://doi.org/10.1038/s41591-021-01472-w

    • PubMed
    • Google Scholar
30. 1. Ponsford MJ
    2. Ward TJC
    3. Stoneham SM
    4. Dallimore CM
    5. Sham D
    6. Osman K
    7. Barry SM
    8. Jolles S
    9. Humphreys IR
    10. Farewell D

    (2021) A systematic review and meta-analysis of inpatient mortality associated with nosocomial and community COVID-19 exposes the vulnerability of immunosuppressed adults

    Frontiers in Immunology 12:744696.

    https://doi.org/10.3389/fimmu.2021.744696

    • PubMed
    • Google Scholar
31. Website

    1. Public Health England

    (2020) COVID-19: epidemiological definitions of outbreaks and clusters in particular settings

    Accessed August 7, 2020.

    https://www.gov.uk/government/publications/covid-19-epidemiological-definitions-of-outbreaks-and-clusters
32. 1. Read JM
    2. Green CA
    3. Harrison EM
    4. Docherty AB
    5. Funk S
    6. Harrison J
    7. Girvan M
    8. Hardwick HE
    9. Turtle L
    10. Dunning J
    11. Nguyen-Van-Tam JS
    12. Openshaw PJ
    13. Baillie JK
    14. Semple MG
    15. ISARIC4C investigators

    (2021) Hospital-acquired SARS-cov-2 infection in the UK’s first COVID-19 pandemic wave

    Lancet 398:1037–1038.

    https://doi.org/10.1016/S0140-6736(21)01786-4

    • PubMed
    • Google Scholar
33. 1. Roy S
    2. Hartley J
    3. Dunn H
    4. Williams R
    5. Williams CA
    6. Breuer J

    (2019) Whole-Genome sequencing provides data for stratifying infection prevention and control management of nosocomial influenza A

    Clinical Infectious Diseases 69:1649–1656.

    https://doi.org/10.1093/cid/ciz020

    • PubMed
    • Google Scholar
34. 1. Snell LB
    2. Awan AR
    3. Charalampous T
    4. Alcolea-Medina A
    5. Douthwaite ST
    6. Edgeworth JD
    7. Nebbia G

    (2022a) SARS-cov-2 variants with shortened incubation periods necessitate new definitions for nosocomial acquisition

    The Journal of Infection 84:248–288.

    https://doi.org/10.1016/j.jinf.2021.08.041

    • PubMed
    • Google Scholar
35. 1. Snell LB
    2. Fisher CL
    3. Taj U
    4. Stirrup O
    5. Merrick B
    6. Alcolea-Medina A
    7. Charalampous T
    8. Signell AW
    9. Wilson HD
    10. Betancor G
    11. Kia Ik MT
    12. Cunningham E
    13. Cliff PR
    14. Pickering S
    15. Galao RP
    16. Batra R
    17. Neil SJD
    18. Malim MH
    19. Doores KJ
    20. Douthwaite ST
    21. Nebbia G
    22. Edgeworth JD
    23. Awan AR
    24. COVID-19 Genomics UK consortium

    (2022b) Combined epidemiological and genomic analysis of nosocomial SARS-cov-2 infection early in the pandemic and the role of unidentified cases in transmission

    Clinical Microbiology and Infection 28:93–100.

    https://doi.org/10.1016/j.cmi.2021.07.040

    • PubMed
    • Google Scholar
36. 1. Stirrup O
    2. Hughes J
    3. Parker M
    4. Partridge DG
    5. Shepherd JG
    6. Blackstone J

    (2021) Rapid feedback on hospital onset SARS-cov-2 infections combining epidemiological and sequencing data

    eLife 10:e65828.

    https://doi.org/10.7554/eLife.65828

    • Google Scholar
37. 1. The COVID-19 Genomics UK consortium

    (2020) An integrated national scale SARS-cov-2 genomic surveillance network

    The Lancet Microbe 1:e99–e100.

    https://doi.org/10.1016/S2666-5247(20)30054-9

    • Google Scholar
38. 1. Thompson JA
    2. Hemming K
    3. Forbes A
    4. Fielding K
    5. Hayes R

    (2021) Comparison of small-sample standard-error corrections for generalised estimating equations in stepped wedge cluster randomised trials with a binary outcome: a simulation study

    Statistical Methods in Medical Research 30:425–439.

    https://doi.org/10.1177/0962280220958735

    • PubMed
    • Google Scholar
39. 1. Volz E
    2. Mishra S
    3. Chand M
    4. Barrett JC
    5. Johnson R
    6. Geidelberg L
    7. Hinsley WR
    8. Laydon DJ
    9. Dabrera G
    10. O’Toole Á
    11. Amato R
    12. Ragonnet-Cronin M
    13. Harrison I
    14. Jackson B
    15. Ariani CV
    16. Boyd O
    17. Loman NJ
    18. McCrone JT
    19. Gonçalves S
    20. Jorgensen D
    21. Myers R
    22. Hill V
    23. Jackson DK
    24. Gaythorpe K
    25. Groves N
    26. Sillitoe J
    27. Kwiatkowski DP
    28. Flaxman S
    29. Ratmann O
    30. Bhatt S
    31. Hopkins S
    32. Gandy A
    33. Rambaut A
    34. Ferguson NM
    35. COVID-19 Genomics UK consortium

    (2021) Assessing transmissibility of SARS-cov-2 lineage B.1.1.7 in England

    Nature 593:266–269.

    https://doi.org/10.1038/s41586-021-03470-x

    • PubMed
    • Google Scholar

Author details

1. Oliver Stirrup

   Institute for Global Health, University College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8705-3281

2. James Blackstone

   The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4335-5269

3. Fiona Mapp

   Institute for Global Health, University College London, London, United Kingdom

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Alyson MacNeil

   The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom

   Contribution

   Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8409-2755

5. Monica Panca

   The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Alison Holmes

   Imperial College Healthcare NHS Trust, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Nicholas Machin

   Manchester University NHS Foundation Trust, Manchester, United Kingdom

   Contribution

   Supervision, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Gee Yen Shin

   University College London Hospitals NHS Foundation Trust, London, United Kingdom

   Contribution

   Supervision, Investigation, Writing – review and editing

   Competing interests

   has an unpaid role as Deputy Chair, British Medical Association London Regional Council. The author has no other competing interests to declare

9. Tabitha Mahungu

   Royal Free London NHS Foundation Trust, London, United Kingdom

   Contribution

   Supervision, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Kordo Saeed

    University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Tranprit Saluja

    Sandwell & West Birmingham Hospitals NHS Trust, Birmingham, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Yusri Taha

    Department of Virology and Infectious Diseases, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Nikunj Mahida

    Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

14. Cassie Pope

    St George’s University Hospitals NHS Foundation Trust, London, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Anu Chawla

    Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Maria-Teresa Cutino-Moguel

    Barts Health NHS Trust, London, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    received payment for anonymous interview conducted by Adkins Research Group. The author has no other competing interests to declare

17. Asif Tamuri

    Research Computing, University College London, London, United Kingdom

    Contribution

    Software, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

18. Rachel Williams

    Department of Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

19. Alistair Darby

    Centre for Genomic Research, University of Liverpool, Liverpool, United Kingdom

    Contribution

    Data curation, Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

20. David L Robertson

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6338-0221

21. Flavia Flaviani

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4210-0451

22. Eleni Nastouli

    University College London Hospitals NHS Foundation Trust, London, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    holds grants by NIHR, EPSRC, MRC-UKRI , H2020, ViiV Healthcare, Pfizer and Amfar, and has received grants to attend meetings from H2020 and ViiV Healthcare

23. Samuel Robson

    Centre for Enzyme Innovation and School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

24. Darren Smith

    Department of Applied Sciences, Northumbria University, Newcastle-upon-Tyne, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    holds the following grants that are not specifically for the present work: COG-UK, PHE test and trace funded the sequencing aspect. HOCI funded a technician to support sequencing during study period. The author has no other competing interests to declare

25. Matthew Loose

    School of Life Sciences, University of Nottingham, Nottingham, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

26. Kenneth Laing

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

27. Irene Monahan

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

28. Beatrix Kele

    Barts Health NHS Trust, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

29. Sam Haldenby

    Centre for Genomic Research, University of Liverpool, Liverpool, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

30. Ryan George

    Manchester University NHS Foundation Trust, Manchester, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

31. Matthew Bashton

    The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Northumbria University, Newcastle, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

32. Adam A Witney

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

33. Matthew Byott

    University College London Hospitals NHS Foundation Trust, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

34. Francesc Coll

    Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    received consulting fees from Next Gen Diagnostics LLC (during 2018/2019), received payment or honoria for lectures from University of Cambridge and Wellcome Genome Campus Advanced Courses, and received support for attending meeting and/or travel to meetings from European Congress of Clinical Microbiology & Infectious Diseases (ECCMID), The American Society for Microbiology (ASM), Microbiology Society, European Congress of Clinical Microbiology & Infectious Diseases (ECCMID), and the British Infection Association (BIA). The author has no other competing interests to declare

35. Michael Chapman

    Health Data Research UK Cambridge Hub, Cambridge, United Kingdom

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

36. Sharon J Peacock

    Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Writing – review and editing

    Competing interests

    received consultancy fees from Pfizer (Coronavirus External Advisory Board) and Melinta Therapeutics, received payment from SVB Leerink for a round table meeting and for Mary Strauss Distinguished Public Lecture from the Fralin Biomedical Research Institute, US, and support for attending ICPIC conference, Geneva and World Health Summit, Berlin in 2021, and hold stocks or stock options in Specific Technologies (European Union Scientific Advisory Board) and Next Gen Diagnostics (Scientific Advisory Board). SP also serves as Chair, Medical Advisory Committee, Sir Jules Thorn Charitable Trust, Board member of the Wellcome SEDRIC (Surveillance and Epidemiology of Drug Resistant Consortium), and Non-Executive Director of Cambridge University Hospitals NHS Foundation Trust. The author has no other competing interests to declare

    "This ORCID iD identifies the author of this article:" 0000-0002-1718-2782

37. COG-UK HOCI Investigators

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    Additional information

    Full list of investigators and their affiliations can be found in Supplementary file 1

38. The COVID-19 Genomics UK (COG-UK) consortium

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    Additional information

    Full list of consortium members and their affiliations can be found in Supplementary file 2

39. Joseph Hughes

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

40. Gaia Nebbia

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

41. David G Partridge

    Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom

    Contribution

    Conceptualization, Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0417-2016

42. Matthew Parker

    Sheffield Bioinformatics Core, University of Sheffield, Sheffield, United Kingdom

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

43. James Richard Price

    Imperial College Healthcare NHS Trust, London, United Kingdom

    Contribution

    Conceptualization, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

44. Christine Peters

    NHS Greater Glasgow and Clyde, Glasgow, United Kingdom

    Contribution

    Conceptualization, Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

45. Sunando Roy

    Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

46. Luke B Snell

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

47. Thushan I de Silva

    Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom

    Contribution

    Conceptualization, Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

48. Emma Thomson

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Conceptualization, Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

49. Paul Flowers

    School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom

    Contribution

    Conceptualization, Supervision, Methodology, Writing – review and editing

    Competing interests

    is a member of the SAGE hospital onset covid working group 2020-2022. The author has no other competing interests to declare

50. Andrew Copas

    Institute for Global Health, University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

51. Judith Breuer

    Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

    For correspondence

    j.breuer@ucl.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8246-0534

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