1. Evolutionary Biology
  2. Microbiology and Infectious Disease
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Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

  1. Christopher JR Illingworth  Is a corresponding author
  2. William L Hamilton
  3. Ben Warne
  4. Matthew Routledge
  5. Ashley Popay
  6. Chris Jackson
  7. Tom Fieldman
  8. Luke W Meredith
  9. Charlotte J Houldcroft
  10. Myra Hosmillo
  11. Aminu S Jahun
  12. Laura G Caller
  13. Sarah L Caddy
  14. Anna Yakovleva
  15. Grant Hall
  16. Fahad A Khokhar
  17. Theresa Feltwell
  18. Malte L Pinckert
  19. Iliana Georgana
  20. Yasmin Chaudhry
  21. Martin D Curran
  22. Surendra Parmar
  23. Dominic Sparkes
  24. Lucy Rivett
  25. Nick K Jones
  26. Sushmita Sridhar
  27. Sally Forrest
  28. Tom Dymond
  29. Kayleigh Grainger
  30. Chris Workman
  31. Mark Ferris
  32. Effrossyni Gkrania-Klotsas
  33. Nicholas M Brown
  34. Michael P Weekes
  35. Stephen Baker
  36. Sharon J Peacock
  37. Ian G Goodfellow
  38. Theodore Gouliouris
  39. Daniela de Angelis
  40. M Estée Török
  1. MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, United Kingdom
  2. Institut für Biologische Physik, Universität zu Köln, Germany
  3. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, United States
  4. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, United Kingdom
  5. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, United Kingdom
  6. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, United Kingdom
  7. Public Health England Field Epidemiology Unit, Cambridge Institute of Public Health, Forvie Site, Cambridge Biomedical Campus, United Kingdom
  8. University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, United Kingdom
  9. Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, United Kingdom
  10. Wellcome Sanger Institute, Wellcome Trust Genome Campus, United Kingdom
  11. MRC Epidemiology Unit, University of Cambridge, Level 3 Institute of Metabolic Science, United Kingdom
  12. University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, United Kingdom
  13. Public Health England, National Infection Service, United Kingdom
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Cite this article as: eLife 2021;10:e67308 doi: 10.7554/eLife.67308

Abstract

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

eLife digest

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, presents a global public health challenge. Hospitals have been at the forefront of this battle, treating large numbers of sick patients over several waves of infection. Finding ways to manage the spread of the virus in hospitals is key to protecting vulnerable patients and workers, while keeping hospitals running, but to generate effective infection control, researchers must understand how SARS-CoV-2 spreads.

A range of factors make studying the transmission of SARS-CoV-2 in hospitals tricky. For instance, some people do not present any symptoms, and, amongst those who do, it can be difficult to determine whether they caught the virus in the hospital or somewhere else. However, comparing the genetic information of the SARS-CoV-2 virus from different people in a hospital could allow scientists to understand how it spreads.

Samples of the genetic material of SARS-CoV-2 can be obtained by swabbing infected individuals. If the genetic sequences of two samples are very different, it is unlikely that the individuals who provided the samples transmitted the virus to one another. Illingworth, Hamilton et al. used this information, along with other data about how SARS-CoV-2 is transmitted, to develop an algorithm that can determine how the virus spreads from person to person in different hospital wards.

To build their algorithm, Illingworth, Hamilton et al. collected SARS-CoV-2 genetic data from patients and staff in a hospital, and combined it with information about how SARS-CoV-2 spreads and how these people moved in the hospital . The algorithm showed that, for the most part, patients were infected by other patients (20 out of 22 cases), while staff were infected equally by patients and staff. By further probing these data, Illingworth, Hamilton et al. revealed that 80% of hospital-acquired infections were caused by a group of just 21% of individuals in the study, identifying a ‘superspreader’ pattern.

These findings may help to inform SARS-CoV-2 infection control measures to reduce spread within hospitals, and could potentially be used to improve infection control in other contexts.

Introduction

Reducing the spread of SARS-CoV-2 is a crucial priority for controlling and limiting the impact of the COVID-19 pandemic. Key metrics in assessing transmission are the basic and effective reproduction R numbers, which describe the mean number of infections caused by a typical infected individual in totally and partially susceptible populations, respectively (Anderson and May, 1992). However, individual variations from this mean can be of vital importance (Lloyd-Smith et al., 2005); a study of SARS-CoV-2 in Hong Kong suggested that 80% of transmission events resulted from only 19% of cases (Adam et al., 2020). Superspreader events are widely reported to play a key role in the spread of the virus in community settings (Shen et al., 2004; Kucharski and Althaus, 2015; Hamner, 2020; Ebrahim and Memish, 2020; Lemieux et al., 2020).

Transmission within hospitals has been identified as a critical concern in managing the COVID-19 pandemic (Iacobucci, 2020). Studying transmission in the hospital environment requires care in distinguishing cases acquired in the community from cases of nosocomial infection (Sikkema et al., 2020). The identification of outbreaks may be complicated by the potential for asymptomatic carriage of the virus (Rivett et al., 2020). As such, testing of asymptomatic health care workers (HCW) has been proposed as a means to reduce viral spread and protect the workforce and patients (Black et al., 2020; Jones et al., 2020).

Evaluating SARS-CoV-2 transmission in a hospital context is not a trivial task. Factors such as the date of symptom onset relative to the date of admission can be used to identify cases of likely nosocomial transmission (Rickman et al., 2021; Price et al., 2021). Phylogenetic methods can be used to identify putative clusters of infection occurring within a single ward or other location within a hospital setting (Meredith et al., 2020). Epidemiological methods can be used to look at potential contacts and opportunities for transmission between cases of infection (Cluster Track, Camart Ltd, Cambridge, UK). However, these approaches do not always provide the detail of who infected whom within a single outbreak or cluster, lacking the resolution to resolve the fine detail of transmission clusters.

Viral genome sequences provide a valuable resource for evaluating nosocomial transmission. At the most basic level, highly distinct sequences are unlikely to be related via transmission. Multiple approaches have been proposed to infer transmission patterns from genome sequences. Typically, phylogenetic reconstruction is used to infer relationships between sequences, an evolutionary model being combined with epidemiological data to infer a network of transmission events (Volz and Frost, 2013; Ypma et al., 2012; Didelot et al., 2014; Hall et al., 2015). Modelling approaches have been extended to include factors such as unsampled hosts (De Maio et al., 2016), the availability of multiple samples per patient (Wymant et al., 2018; Worby et al., 2016), incomplete epidemics (Didelot et al., 2017), and deep sequence data (Ratmann et al., 2019).

Here, we evaluated patterns of viral transmission occurring in epidemiological clusters in Cambridge University Hospitals NHS Foundation Trust (CUH), United Kingdom, where multiple patients with suspected hospital-acquired COVID-19 infections and/or HCW working on the same wards tested positive for SARS-CoV-2 within a 2-week period. Using a novel approach to combine genetic and epidemiological data, we inferred networks of SARS-CoV-2 transmission between these individuals. The tight clustering of genome sequences collected within a single ward places an imperative on the exploitation of non-genetic information to identify potential transmission events. We did this by combining an evolutionary model with symptom and location data for individuals considered, and knowledge of SARS-CoV-2 infection dynamics. Examining data from the largest clusters of infection identified within the hospital, we showed that the spread of infection in these clusters was driven by a small set of superspreader individuals.

Results

We developed a method to infer networks of transmission events between individuals within CUH. Our method combines knowledge of SARS-CoV-2 infection dynamics with viral genome sequences and data describing the movements of patients and HCWs within the hospital.

Applying our method to these data, we generated maximum likelihood reconstructions of the pattern of transmission events occurring within five infection clusters, each of which was centred on a ward at CUH. For reasons of data protection we term these wards A to E. These five wards were chosen as they contained the largest number of patients with hospital-onset infections and/or healthcare worker infections in CUH up to the end of the study period. Of the wards analyzed, wards A to D were ‘green’ wards (designated for patients who had not tested positive for SARS-CoV-2), while ward E was a ‘red’ ward (designated for known COVID-19 patients). Although referred to here as a ‘ward’ for simplicity, one of the green wards was a number of neighbouring clinical areas within the hospital. A preliminary analysis of the data, treating individuals in a pairwise manner, suggested that transmission events between the identified wards was unlikely (Figure 1).

Preliminary analysis of the data with A2B-Covid.

Squares indicate the extent to which an individual-to-individual transmission event is consistent with the data collected, when considered on a pairwise level. Our analysis highlighted multiple potential transmission events occurring within each ward, but transmission between individuals on different wards was uniformly assessed as unlikely. Further analyses of the data considered wards as independent and isolated locations.

Reconstructed transmission networks for the four green wards are shown in Figure 2. Our method requires each transmission in a network to be consistent with a statistical model of pairwise viral transmission (Illingworth, 2020). As such, a broad range of possibilities could in theory be inferred. At one extreme, the infections on a ward could all arise from a single introduction of the virus, with all cases arising via transmission from a single individual. At the other extreme, the infections could all be entirely independent of one another, with no transmission between cases at all. Our approach uses sequence data and epidemiological information to identify cases that are plausibly linked by direct transmission, before inferring the maximum likelihood network reconstruction of events. Across the green wards, the majority of cases were inferred to be connected to at least one other via transmission, with 42 out of 54 cases being joined into networks. These networks involved between 2 and 11 cases each (mean 5.9 cases). This contrasts with results from the single red ward (Figure 3), in which only 9 of 19 cases were inferred to be linked to others via transmission (mean inferred network size 2.3 cases). Our result corresponds to the nature of the wards studied; the repeated transfer of infected patients onto a red ward leads to an increased number of independent introductions.

Figure 2 with 2 supplements see all
Maximum likelihood transmission networks for wards A to D.

Circles represent individuals and arrows show transmission events. White circles represent patients while grey circles represent health care workers. Individuals for which no transmission events were inferred are represented as isolated circles.

Maximum likelihood transmission network for ward E.

Circles represent individuals and arrows show transmission events. White circles represent patients while grey circles represent health care workers. Individuals for which no transmission events were inferred are represented as isolated circles.

Individuals in our study were divided into patients and HCWs allowing for the estimation of rates of transmission between these categories. Of the 38 transmission events in the maximum likelihood networks, 20 were patient-to-patient, 8 were from patient to HCW, 8 were HCW-to-HCW, and just 2 were from HCW to patient (Figure 2—figure supplement 1) These results suggest that patients were significantly more likely to be infected by other patients than by health care workers (p-value 6.1 x 10−5, one-tailed binomial test). By contrast, HCWs were at approximately equal risk of being infected by patients and other HCWs.

Some of the wards analysed appeared to show uneven patterns of viral transmission, with a small number of individuals responsible for most of the infections observed. For example, in the maximum likelihood reconstruction derived for ward A, the majority of individuals infected did not pass on the virus, while individuals A6 and A10 were the sources, respectively, of four and five transmission events (Figure 2A).

A statistical analysis of the inferred networks provided evidence for a role for superspreading behaviour during transmission. As a first step in evaluating this, we calculated the level of uncertainty in each inferred network, combining data from the maximum likelihood network with that from other plausible, but lower-likelihood networks. Figure 2—figure supplement 2 shows statistical ensembles of networks inferred for the green wards. In this figure, the width of an arrow is proportional to the probability that transmission occurred between each pair of individuals. The maximum likelihood network inferred for Ward E was the only plausible solution given by our reconstruction method.

In a second step, we fitted models to data from these ensembles, calculating probability distributions of the number of individuals infected by each person in the dataset. A negative binomial model, in which the extent of viral spreading was overdispersed, gave a better explanation of the data, measured using the Bayesian Information Criterion, than a simpler model in which all individuals transmitted equally (Figure 4). In the best-performing model of viral spreading, 87% of individuals either did not transmit the virus, or transmitted only to one other. Taken across all individuals, 21% of individuals were responsible for 80% of viral transmission, a result very similar to that found among the general population (Adam et al., 2020). A repeat of this calculation for the green wards alone gave similar statistics (Figure 4—figure supplement 1), with 23% of individuals in these wards being responsible for 80% of transmission (Figure 4—figure supplement 1).

Figure 4 with 5 supplements see all
Models of viral transmission.

(A) Fit of the output of the Poisson model (black dots) to the ensemble data (yellow bars). The weighted number of transmissions per individual reflects the uncertainty in the network reconstruction across the ensemble. (B) Fit of the output of the negative binomial model (black dots) to the ensemble data (yellow bars). (C) Proportions of individuals causing different proportions of infections. A negative binomial model (red line) fitted to all ward data produces a result similar to that of Adam et al., 2020 (blue dot), with 20% of individuals being responsible for 80% of infections. A Poisson model fitted to the same data (dashed grey line) has 20% of individuals being responsible for 60% of infections.

Figure 4—source data 1

Distributions of number of individuals infected by each individual and fits to these data using Poisson and Negative Binomial models.

https://cdn.elifesciences.org/articles/67308/elife-67308-fig4-data1-v2.xlsx

In our maximum likelihood reconstructions, a total of five individuals infected three or more others, including one HCW and four patients. Clinical characteristics of these individuals were explored, but are not described in detail or assigned to their anonymised ward clusters to preserve patient anonymity. Of the four patients, all had suspected hospital-acquired COVID-19 and significant comorbidities: two had a history of chronic liver disease, and two had previous haematological malignancies, one of whom was still on immunosuppressive treatment. Immunosuppression has been associated with prolonged viral shedding (Italiano et al., 2020; Avanzato et al., 2020). One superspreader was confused and mobile on the ward. Another had a fever for several days before being tested for SARS-CoV-2, which had been attributed to a pre-existing community-acquired bacterial infection. The only HCW superspreader exclusively infected other HCWs, and shared accommodation with several of these individuals. Cycle threshold (Ct) values of samples collected from identified superspreader individuals were not statistically distinct from those from individuals in the study in general (Figure 4—figure supplement 2).

Inferred timings of transmission events caused by superspreaders showed a variety of patterns (Figure 4—figure supplements 35). In ward B, the initial three infections of HCWs by the individual B0 are likely to have occurred within a short period of the SARS-CoV-2 virus entering the ward (within 4 days with 95% certainty), suggesting that an outbreak caused by superspreading may spread rapidly to multiple individuals. However, the inferred timings on other wards were less conclusive; in wards A and C the inferred distributions of infection times were more diffuse. We note simply that where superspreader events occur, the potential exists for multiple transmissions to occur within a short space of time.

Discussion

We have here outlined a novel approach for the inference of transmission networks. Our approach combines an evolutionary model with specific information about SARS-CoV-2 transmission dynamics to identify the most probable set of transmission events linking a set of cases of infection. Our approach builds upon previous approaches to analysing SARS-CoV-2 data from hospital settings, going beyond the identification of clusters to infer directional networks of viral transmission.

The multiple forms of data used by our method each play a critical role in network inference. Where individuals in a ward become symptomatic at similar times, sequence data provide a strong indication of whether these infections are linked via transmission or arise from completely independent events. However, the potentially short time spanned by a local outbreak may be insufficient for substantial genetic variation to accumulate in the virtual population; in such cases, other information, such as dates of symptom onset, become critical for network reconstruction.

The inference of networks allows for detailed study of how SARS-CoV-2 can spread within a clinical environment. Contacts between patients appear to be crucial, as they are primarily infected by other patients rather than through transmission from HCWs. This finding has potential implications for the application of protective measures within a hospital environment, wherease face mask usage was enforced for individuals in outpatients and for HCWs in all areas of the hospital, inpatients were not at the time of data collection subject to the same precautions. A recent study has suggested SARS-CoV-2 aerosolisation to be high in areas where patients with COVID-19 are coughing (Hamilton, 2021).

Our study is biased in its consideration of the largest clusters of infection identified in CUH wards during the first wave of the pandemic. Examining data from these clusters, we identified a pattern of superspreading, in which a small proportion of individuals were responsible for the majority of nosocomial transmission events. Our result is interesting in the context of previous studies of superspreading (Shen et al., 2004; Kucharski and Althaus, 2015), providing an example of this in a hospital context, and a case in which a small proportion of ‘superspreader individuals’ drive ‘superspreader events’. We note that, while prolonged or increased viral shedding would increase the chance of an individual becoming a superspreader, behavioural and environmental factors may also be influential.

A key feature of SARS-CoV-2 that makes infection prevention and control (IPC) particularly challenging is its significant infectivity prior to the onset of symptoms. This means that isolating staff or patients once symptoms are recognised is not sufficient to prevent transmission. The superspreaders identified here illustrate several principles for limiting the spread of SARS-CoV-2 in hospitals. First, scrupulous adherence to infection control practices including use of appropriate personal protective equipment (PPE) at all times, even on green wards and in non-clinical hospital areas, is required to limit transmission between asymptomatic patients and staff in which COVID-19 is not suspected. Use of masks by patients, including on green wards, should be instituted if tolerated, particularly when staff are present in patient bed spaces. Second, healthcare professionals must be vigilant to the possibility of hospital-onset COVID-19 and have a low threshold for testing inpatients, even where an alternative differential diagnosis for the patients' symptoms exists. Third, as soon as positive cases are confirmed, appropriate isolation and PPE precautions should be used, along with contact tracing, testing and isolation. Patients who have been in direct contact with confirmed cases on green wards should be isolated. Fourth, regular screening of asymptomatic individuals can help to identify patients and staff that may be unsuspectingly infected with COVID-19 and infectious, either pre-symptomatic, pauci-symptomatic or asymptomatic, prompting isolation and contact tracing (Jones et al., 2020). Fifth, ventilation should be improved to reduce the risk of aerosol dispersal. Of note, our recommendations concur with the current UK guidance for COVID-19 infection prevention and control (Public Health England, 2020), which have evolved during the course of the pandemic. Finally, our identification of transmission from patients to HCWs highlights the use of higher grade respiratory precaution to protect HCWs (such as FFP3 respirators) as an important topic for future research.

The potential for superspreading enhances the difficulty of controlling hospital-acquired infection, particularly as most transmission events from superspreaders to other people inferred in this study occurred within a relatively short time period. By the time a second linked case in a ward is identified, the potential exists for an index case to have infected multiple individuals, making it too late to prevent a broader outbreak. While this study does not allow for a complete characterisation of superspreading individuals, it may suggest possible risk factors in these instances such as immunosuppression (associated with prolonged shedding), more mobile behaviour that may have contributed to increased risk of transmission, and extended symptoms (fever) prior to testing and isolation (due to fever being attributed to an alternative cause).

Our inferences of transmission were performed on the basis of a largely complete dataset. Sampling of infections within wards was likely very close to complete, with sample collection from symptomatic patients and health care workers being conducted in parallel to asymptomatic screening of hospital staff. A screening programme for asymptomatic HCW was set up at CUH in April 2020 (Rivett et al., 2020) and voluntary weekly screening is currently offered to all HCW. SARS-CoV-2 seroprevalence among staff tested in CUH between 10th June and 7th August 2020 was 7.2% (Cooper, 2020). The five outbreaks described here occurred earlier in the pandemic (March to June 2020), when staff seroprevalence would have been lower. The proportion of staff with neutralising antibodies during the outbreaks was therefore low, and likely played a minor role in transmission dynamics. Sequencing was attempted for all positive samples; across the green wards data was of high quality for 55 out of 71 individuals for whom data was collected (>80% unambiguous nucleotides with no more than one ambiguous nucleotide at at a variant site) consensus viral genome in addition to data describing their location during the period of the study.

We acknowledge several limitations to our study. There is potential for missing or incomplete data, with some aspects of the data more vulnerable than others to omission. Asymptomatic screening was offered to all staff working on the five wards analysed in the study during the outbreaks. It is theoretically possible that HCW could have caught COVID-19 early on in the outbreaks and cleared the virus quickly, becoming negative at time of testing, or caught the virus asymptomatically after the screening test, or had levels of virus below the detection limit of the assay (and thus have been false negatives). However, levels of SARS-CoV-2 RNA below the assay detection limit are unlikely to be infectious (and thus not significant for the inferred transmission networks), and overall HCW testing coverage was high. Testing of asymptomatic patients varied by ward. Asymptomatic screening was done for all patients on Wards A and B during the outbreaks, and all patients entering Ward E (the only ‘red’ ward included in the study) were known SARS-CoV-2 positives. However, for Wards C and D, systematic asymptomatic screening of all patients on the ward during the outbreaks was not performed, and it is possible some asymptomatic infections (that could have contributed to the transmission networks) were missed. Data describing the wards on which patients were treated is likely to be complete, but the same ward data for HCWs may miss the potential for interactions between workers outside of their base wards for example in communal non-clinical areas within the hospital. Missing location data would lead to the non-identification of genuine contacts; our approach may therefore underestimate the number of infections caused by transmission between health care workers. Where data were missing our method does not attempt to identify cases of indirect transmission, for example invoking the presence of unobserved individuals. Only potential cases of transmission that were compatible with a model of direct transmission were included in our networks. The number of superspreader individuals identified in this study (five) is too small to draw general conclusions on superspreader characteristics. Moreover, it is not possible to disentangle whether superspreading was driven mainly by individual factors (such as infectivity or behaviour) or environmental factors (such as patient placement and ventilation at time of peak infectivity), or a combination of these. Ct values can vary for many reasons including the timing of sampling during COVID-19 infection, sampling type and technique, viral transport, sample preparation and variability between PCR runs. The finding that Ct values did not vary significantly between superspreader and non-superspreader individuals should therefore be interpreted with caution.

In conclusion, we have here applied a combined statistical approach to infer and examine SARS-CoV-2 transmission networks within a hospital environment during the first wave of the pandemic in the United Kingdom. For the largest ward outbreaks of hospital-onset COVID-19, the majority of transmission was driven by a small proportion of individuals. Future developments could include exploring the impact of variables that may be associated with an increased transmission risk. Examples would include novel SARS-CoV-2 variants such as B1.1.7 and B1.617.2, which appear to be more readily transmissible (Rambaut et al., 2020), patient characteristics such as immunosuppression which are associated with prolonged viral shedding (Avanzato et al., 2020) and environmental factors such as patient placement and room ventilation. Nevertheless, this unusually comprehensive dataset has provided detailed insights into the processes of hospital-based transmission. Combining data from multiple sources into a single analysis provides increased resolution and insight into the pervasive problem of nosocomial viral transmission.

Materials and methods

Study design, setting, and participants

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Prospective surveillance studies of COVID-19 infection in patients and healthcare workers (HCW) were conducted at Cambridge University Hospitals NHS Foundation Trust (CUH), as previously described (Rivett et al., 2020; Meredith et al., 2020). Nasopharyngeal swab samples were collected and submitted to the Public Health England (PHE) Clinical Microbiology and Public Health Laboratory (CMPHL) or the Department of Medicine, University of Cambridge for SARS-CoV-2 diagnostic testing, as detailed below. Samples included in this study were collected during the first epidemic wave, between 22nd March and 14th June 2020. Clinical, laboratory, and patient location data were extracted from the hospital information system (EPIC Systems Corporation, Verona, USA). HCW ward location data were collected by members of the HCW screening team. PHE recommendations for COVID-19 infection prevention and control, including PPE use, were followed throughout the course of the study.

Testing criteria

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Patients and HCW had separate testing criteria and sample workflows. HCW were tested in the CUH HCW screening programme, which included both asymptomatic screening and symptomatic testing arms. Asymptomatic screening at the time of this study was focused on staff working on COVID-19 ‘red’ wards (designated for patients with confirmed COVID-19 infection), wards with hospital-acquired infection outbreaks, and wards with high rates of staff positivity. Suggested symptomatology to prompt staff testing are described in Rivett et al., 2020, divided into ‘major’ criteria (e.g. fever and/or new persistent cough) and ‘minor’ criteria (e.g. coryzal symptoms, headache, myalgia).

For all five of the outbreaks described in this study, all staff working on the outbreak wards were invited for screening by the CUH HCW screening team (i.e. tested regardless of any symptoms or if asymptomatic) during the outbreak periods (prompted by the outbreak investigations and/or high rates of staff positivity on the wards).

There was no systematic asymptomatic screening for inpatients in CUH during the study period, but targeted patient screening on wards with hospital-onset COVID-19 outbreaks was performed. Ward E was a COVID-19 ‘red’ ward; all patients on this ward had tested positive prior to placement there. Wards A to D were all ‘green’ wards (designated for non-COVID-19 patients) at the time the hospital-onset COVID-19 outbreaks started. For Ward A, symptomatic contacts of confirmed cases were tested initially, and as the outbreak grew and more cases were confirmed, ultimately all patients on the ward were screened (including asymptomatics). For Ward C, contacts of confirmed positives and/or patients who developed symptoms were screened, and for Ward D, symptomatic contacts of the index case were tested. Thus, for Wards C and D, systematic asymptomatic screening of all patients on the ward during the outbreaks was not performed. For the Ward B outbreak, when the index patient (case B0) tested positive, all staff members who had worked on the cluster of clinical areas referred to as ‘Ward B’ within the preceding 2 weeks plus all patients on those wards were screened (regardless of any symptoms). Thus, there is high confidence for Wards A, B and E that all infections among both staff and patients were detected (providing the amount of SARS-CoV-2 RNA was sufficient for detection).

Visitor restrictions were introduced on 25th March 2020 and so were present for almost all of this study (first positive swab was for Ward E, collected a few days before this). After 25th March, visitors to adult patients were only permitted in exceptional circumstances: for patients at the end of life or visitors with a direct care role for the patient.

Laboratory methods

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Samples underwent nucleic acid extraction and were tested for presence of SARS-CoV-2 using a validated in-house RT qPCR assay, as previously described (Price et al., 2021). The test was reported as SARS-CoV-2 PCR positive if the cycle threshold (Ct) value was less than or equal 36. A 15 microlitre aliquot of the RNA extract of each positive sample was transferred to the Department of Pathology, University of Cambridge, for sequencing.

Sequencing and genomic analysis

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Samples were assigned COG-UK sequencing codes and sequenced using a multiplex PCR based approach according to the modified ARTIC v2 protocol with v3 primer set (artic-ncov, 2019; Quick, 2020). Amplicon libraries were sequenced using MinION flow cells v9.4.1 (Oxford Nanopore Technologies, Oxford, UK). Genomes were assembled using reference-based assembly and a bioinformatic pipeline (Artic Network, 2021). All sequences underwent quality control (QC) filtering, including a 20x minimum coverage cut-off for any region of the genome and 50.1% cut-off for calling single nucleotide polymorphisms (SNP).

Sample selection

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Patients from CUH were determined to have indeterminate, suspected or definite hospital-acquired infections (HAI) on the basis of days from admission to first positive SARS-CoV-2 test, using the same definitions as in Meredith et al., 2020; Price et al., 2021: indeterminate = positive test after 48 hr and less than 7 days post admission; suspected = positive test 7 to 14 days post admission; definite = positive test greater than 14 days post admission. Wards were ranked by their combined number of indeterminate, suspected and definite HAI cases plus HCW cases (taking the HCW ward to be any ward each HCW had worked on within the preceding 14 days before testing positive). 15 wards at CUH had two or more HAI and HCW cases occur within 14 days of each-other. The five wards with the largest number of combined HAI and HCW cases were chosen for this study and named wards A to E (Supplementary file 1).

Each of the selected wards had 10 or more HAI plus HCW cases, therefore representing the largest HAI ward-based clusters in CUH during the study period, which encompasses the ‘first wave’ of the pandemic in the East of England region. Four were ‘green’ wards (A to D), intended to house patients who did not have COVID-19, and one was a ‘red’ ward (E), intended to house confirmed COVID-19 cases. In four out of five wards, the majority of cases were HCW. Ward characteristics cannot be described in detail in order to preserve confidentiality. They were typically organised into bays, with four to six patients per bay, and a limited number of side rooms (which are critical for infection control purposes). In summary, Ward A had 30 beds with three side rooms; Ward C had 27 beds with three side rooms; Ward D had 30 beds with four side rooms; Ward E had 26 beds with three side rooms. The Ward B outbreak was focused around a ward with 32 beds of which 12 were side rooms, although several adjacent clinical areas were screened as part of the outbreak investigation (as staff were shared between these wards).

An initial network analysis of patient bed movements was conducted to add patients to each ward cluster that may have been in contact with the HAI cases, either with community onset infections on the same ward or while they were co-located on other wards outside of the ‘outbreak wards’ themselves. This analysis was undertaken in two steps using SQL v18.5.1 and FoodChain-Lab (an extension of the Knime Analytics Platform v3.6.1). The first step involved utilising SQL to process case and ward movements data from CUH patients, creating a list of ward-based case co-locations that were within the set parameters of the network analysis model. These parameters were (1) an infectious period that included the 4 days prior to symptom onset up until 7 days after symptom onset, and (2) a susceptibility period of 14 days prior to symptom onset. Where symptom onset date was not available, the case positive specimen date was used instead. The second step was to import the case co-locations data into FoodChain-Lab, which was used to draw a social network diagram of cases and their ward co-locations with one another that met the set parameters. This network diagram was used to identify any clustering of cases by ward that had not met the original criteria of HAI and HCW cases but could have been involved in shared transmission with those individuals based on their co-location within the infection period of the virus. This yielded the final case set for each ward cluster taken forward for analysis using the transmission reconstruction model.

Data collection

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Data were collected on COVID-19 symptom onset dates for all included individuals from the five ward clusters. These were collected separately for HCW and patients. HCW testing was part of the CUH HCW screening programme, and testing criteria are described in Rivett et al (Sikkema et al., 2020). (Table 1). Staff who tested positive were then contacted by members of the HCW screening team and asked retrospectively about their symptoms, and onset dates were recorded by the HCW screening team and used in this analysis. For patients, symptom onset dates were collected by retrospective review of patients’ electronic hospital records (EPIC Systems), usually noted at presentation by the clerking doctor but all records were examined for any suggestion of symptoms. Symptom definitions followed the standard national recommendations at the time: initially fever, breathlessness, and new continuous cough; muscle aches were added in early May and loss of taste or smell was included from mid-May. If the infection was asymptomatic, or symptom onset dates could not be determined, then the date of first positive SARS-CoV-2 test was used instead.

Table 1
Case numbers in the five major ward clusters.

Total cases before network analysis’ were derived by adding patients with potential hospital-acquired COVID-19 infections and HCW cases from each ward. The five wards with the largest combined number of HAI and HCW cases within the study period were analysed, with anonymised ward names A to E. ‘Ward type’ refers to whether wards were ‘green’ (intended for patients negative for COVID-19), or ‘red’ (intended for COVID-19 patients). The breakdown of HAI and HCW cases for each of the included wards is shown in columns ‘HAI cases before network analysis’ and ‘HCW cases before network analysis’, respectively. The ‘network analysis’ at this stage identified additional patients that could have been involved in transmission with the HAI patients on the basis of co-location on the same or other wards within a plausible timeframe for SARS-CoV-2 transmission (described in Materials and methods). This yielded the final cases analysed for each ward cluster using the transmission reconstruction model. The final column shows the number of cases from each ward cluster for which genomic data were available. In total, there were 98 cases with genomic data and 129 SARS-CoV-2 genomes analysed in the study (the larger number of genomes than cases is because of multiple samples per patient that underwent SARS-CoV-2 sequencing). Three patients were included in two ward clusters each (which is why the total of the ‘Cases after network analysis with genomic data’ column is 101). HAI = hospital-acquired infection (definition in Methods); HCW = healthcare worker.

Ward nameWard typeTotal cases before network analysisHAI cases before network analysisHCW cases before network analysisCases after network analysisCases after network analysis with genomic data
AGreen141221615
BGreen11291512
CGreen12572019
DGreen144101616
ERed133104739

Patient ward movement data through the hospital was downloaded from the hospital electronic records system (EPIC). Data on HCW shift patterns were collected manually using hospital shift rostering information and, in some cases, directly contacting the HCW. HCW were defined as being either present or absent on each ‘outbreak’ ward within each 24 hr period (i.e. time of day or shift length was not taken into account). No HCW worked on multiple outbreak wards, although staff contact outside of wards (during lunch, outside of work etc) cannot be excluded.

Each anonymised patient code was linked to COG-UK sequencing codes, with sample collection date and laboratory receive dates recorded for each sample that was sequenced. Dates of symptom onset and sample collection for each individual are shown in Figure 5.

Figure 5 with 5 supplements see all
Overview of events on different wards.

Blue squares show days on which individuals became symptomatic, while green squares show inferred days of individuals becoming symptomatic when these dates were unknown or not applicable. Red circles show days on which samples were collected from individuals for genome sequencing. Dates within each ward are normalised so that the first event on any ward is day zero. We note that not all collected samples led to genome sequences of sufficient quality to be useful in this study.

Model development

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Given a set of data from infected individuals, comprising viral genome sequences collected from each infection, dates on which individuals became symptomatic, and when individuals were co-located, we sought to identify whether and how these cases are linked by transmission. We noted that, prior to analysis, we did not know whether the cases for which we had data were connected via transmission; a set of cases may be anything from completely unlinked to a single connected transmission network.

As a first stage in our method, we partitioned our data into subsets of cases that might plausibly be linked by transmission events. This step was conducted using a pairwise measure of the consistency of the data observed from two individuals with the hypothesis of direct transmission having occurred between these two individuals; calculation of this measure was previously implemented in the A2B-COVID software package (Illingworth, 2020). The mathematical principles of this pairwise measure of consistency are explained fully elsewhere (Illingworth, 2020); below we provide a summary.

Pairwise likelihood of transmission

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Our pairwise likelihood function combines information from a broad variety of sources. Firstly, dates on which individuals first reported symptoms give an indication of the likelihood of transmission between individuals. Previous publications have characterised the infectivity profile of SARS-CoV-2 (i.e. the time between becoming symptomatic to transmitting the virus to another person conditional on causing infection), by fitting a shifted gamma distribution to data from confirmed infection, inferring the distribution parameters α=97.185, β=0.2689, and shift s=25.625 (Ashcroft et al., 2020; He et al., 2020). Further, the time between an individual being infected with SARS-CoV-2 and developing symptoms has been described as a lognormal distribution, with inferred parameters μ=1.434 and σ=0.6612 (He et al., 2020; Li et al., 2020). We use these distributions to assess the extent to which the observed symptom-onset times are compatible with transmission. Specifically, we denote by XT the event that transmission from i to j occurred on day T, and by X the event that transmission from i to j occurred at all. For convenience we collect together parameters, writing θ1={α, β, s, μ, σ}. Supposing that individuals i and j became symptomatic on the days Si and Sj respectively, we then derive the expression:

P(T|θ1,Si,X)PSj|θ1,XT=T-Si-0.5T-Si+0.5e-(x+s)/β(x+s)α-1β-αΓαdxSj-T-0.5Sj-T+0.5e-logx-μ22σ2xσ2πdx

where a time of becoming symptomatic was unknown, the date at which that individual tested positive was used, corrected by a constant term estimated from the distribution of known times between positive test dates and dates of becoming symptomatic (Illingworth, 2020).

We elaborate on this model by incorporating data describing the location of individuals, noting that transmission can only occur when people are co-located, and viral genome sequences collected from individuals in the study.

Incorporating location data

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We have, for individuals in our study, information describing the wards on which patients were hosted, and the wards on which HCWs worked shifts. Given individuals i and j we defined a contact metric wij(T) equal to the probability that i and j were co-located on any given day T.

Ward data describing patient locations and health care worker shift dates were used to calculate values wij for each pair of individuals. For any given ward, we set the value wi(W,T) to equal one if the individual i was known to be on ward W on day T. If a healthcare worker worked a shift on ward W on day T, we further assigned a minimum value of 0.5 to the values wi(W,T-1) and wi(W,T+1), accounting for night shifts overlapping days, and the potential for fomite transmission. Removing these additional days led to some small alterations in the inferred networks (Figure 5—figure supplement 1), but did not substantially affect our primary result, with again 80% of cases being caused by 21% of individuals (Figure 5—figure supplement 2). For all other dates, we set wi(W,T) to be zero. In the event that location data was unknown, we assigned a value for wi(W,T) of 1 for patients for the most common ward on which individuals in a cluster were located, and a value for wi(W,T) of 4/7 for health care workers, reflecting common shift patterns of work. The value wij(T) for any two individuals i and j was then defined as the maximum of the product of wi(X,T) and wj(X,T) calculated across all wards X for that value of T.

Incorporating viral genome sequences

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Our pairwise likelihood accounted for data from viral genome sequencing using a separate likelihood function. For each pair of individuals, sequences were assessed according to the number of nucleotides by which they differed from a pairwise consensus, calculated from the two sequences, with reference to the broader set of sequences collected from a ward. At a given genome position, the consensus was defined as the nucleotide shared by the two sequences if they were identical, or by the most common nucleotide in the broader set if the two sequences differed.

The Hamming distances between each individual sequence and the pairwise consensus were measured. Each distance reflects both the potential evolution of the virus and the extent of measurement error inherent to the sequencing process. Using data from CUH, an estimate of the measurement error was previously calculated as E=0.414 nucleotides per pair of sequences, or 0.212 nucleotides per sequence (Illingworth, 2020). To model viral evolution we adopted the rate γG=0.0655 nucleotide substitutions per day, equal to the global rate of viral adaptation (Hadfield et al., 2018). We then derived a Poisson model for the observed number of nucleotide changes resulting from evolution and error. We denote the days on which viral sequence data was collected from the individuals i and j as D = {Di, Dj}, and the Hamming distances of each sequence from their consensus as Hi and Hj, and again use a shorthand expression θ2={E, γG}. We then have the result that

PHi,Hj|θ2,D,XT=E/2+γGPiHie-E/2+γGPiHi!E/2+γGDj-QiHje-E/2+γGDj-QiHj!

We now combine the above terms, writing y = {Si, Sj, Hi, Hj}, θ = {θ12}, and denoting by X the event that transmission occurred from i to j at any time to obtain

P(y|θ,D,X)=TP(T|Si,θ1)PSj|θ1,XTwij(T)PHi,Hj|θ2,D,XT

This expression was used to assess the consistency of the data y from each pair of individuals with the hypothesis of transmission from i to j.

Pairwise thresholds

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The value p(y|θ,D,X) is defined over a discrete space of symptom times and sequence measurements. Calculating this over all feasible terms within this discrete space, we derived threshold values, conditional upon D, for which 95% and 99% of genuine transmission events would obtain values greater than the threshold. In this way, potential transmission events were classified into ‘consistent’ events (probability greater than the 95% threshold), ‘borderline’ events (probability between the 95% and 99% thresholds) and ‘unlikely’ events (probability below the 99% threshold).

The pairwise analysis, described to this point, was used to assess the data for potential between-ward transmission events.

Inferring a maximum likelihood transmission network

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As a first step in identifying transmission networks in our data, we clustered individuals using the pairwise thresholds. By way of notation we denote the transmission event from individual i to individual j as i→j.

Beginning with a single subset containing an arbitrary individual, we added an individual j to the subset S if for some individual i in S, either i→j or j→i has a likelihood that was ‘consistent’ or ‘borderline’ under the pairwise likelihood. If no such subset existed, j was placed in a new subset, repeating the process until all individuals were clustered into sets between which transmission events were unlikely.

If clustering identified individuals that were alone within a subset, these individuals were removed from the analysis at this point, giving further attention to clusters that contained at least two individuals.

Network likelihood of transmission

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Building upon our pairwise method, we calculated the likelihood of a transmission network. A transmission network must contain at least one transmission event. We consider the network N, comprising transmission events i1→j1, i2→j2, etc, in which the transmission even ik→jk occurs on day Tk. We here write T* = {Tk} to represent the set of all transmission times.

Similar to the calculation above, we consider symptom onset times, the locations of individuals, and sequence components, using these to derive an expression for a particular network of transmission events. Considering the non-genetic data, we make the assumption that the intrinsic dynamics of each transmission are independent of one another, so that the joint distribution of the observed symptom times Si given a particular network and set ot transmission times XTk, is

PSi|θ1,XTk=kwikjkTkP(Tk|θ1,Sik)PSjk|θ1,XTk

For the sequence component of the likelihood, we consider the transmission tree defined by our network. Adopting an infinite sites model (Kimura, 1969), we assume that each observed nucleotide substitution occurred only once, with the reversion of substitutions being impossible. Further, we assumed that, while a substitution that was observed in only one sequence could have arisen from measurement error, substitutions that were observed in two or more sequences could not have resulted from error. Under these assumptions, we classified potential substitutions by the sequences they would be observed in, then adopted a Poisson likelihood model, comparing the periods of time in which sets of substitutions could have occurred to the numbers of substitutions that were observed in the data (Felsenstein, 1981).

We first describe this likelihood for a transmission network in which all times of transmission are known. From the sequence data, we identified nucleotide substitutions occurring in viral sequences relative to the consensus sequence. We denote the number of substitutions observed in a sequence or group of sequences I by MI. Windows of time were then assigned to substitutions in reverse order. Diagrams illustrating this process are shown in Figure 5—figure supplement 3.

We first consider the final transmission event to occur (Figure 5—figure supplement 3A). We suppose that this transmission is A→B, occurring at time tAB. It is clear that the time of sample collection DB occurs no earlier than tAB; we divide the scenario into two possibilities.

Firstly, if DA ≥ tAB, we have that substitutions observed only in individual B must have occurred in the DB - tAB days between tAB and DB, while substitutions observed only in A must have occurred in the DA - tAB days between tAB and DA. We note that substitutions observed only in single individuals are treated as potentially arising from either evolution or noise. We therefore calculated the likelihood of having observed MA substitutions from a Poisson distributed variable with expectation E/2 + γG(DA - tAB), and MB substitutions from a Poisson distributed variable with expectation E/2 + γG(DB - tAB).

Alternatively, if DA < tAB, then there exists a time P, equal to the latest of the set of times including DA and all other times of transmission tAX from A to an individual X other than B. We note that mutations observed only in B can then have occurred in the DB - P days of this interval, while mutations that were observed only in A can only have arisen through sequencing error. We therefore calculated the likelihood of having observed MA substitutions from a Poisson distributed variable with expectation E/2 + γG(DA - P), and MB substitutions from a Poisson distributed variable with expectation E/2. We note that the inclusion of a non-zero rate of sequencing error allows for A to transmit to B at a time after the observation of a viral consensus sequence from A that contains a substitution not observed in the viral consensus from B.

A similar logical process was carried for each transmission event working backwards in time. A more general case, representing a joining of two branches of the tree, is shown in Figure 5—figure supplement 1B. We here consider the individuals A, B, C, and D, noting that these may not be terminal branches in the tree. That is, if a mutation occurs early in the infection of individual C, it will be observed both in the sequence collected from C and in sequences collected from individuals to which C later transmits the virus.

In this case, there are a variety of possibilities here to enumerate. If DA ≥ tAD, then mutations which are observed precisely in A, D, and individuals downstream of A and D, must occur in the window between tAB and tAD. If DA < tAD but DA ≥ tAB, then the same mutations must occur in the window between tAB and DA.

Finally, if DA < tAB, it is impossible for mutations to be observed in precisely A, D, and individuals downstream of A and D. However, there exists a point P, equal to the latest of a set of times including DA and all other times of transmission from A to another individual prior to tAB. In this scenario, substitutions occurring between P and tAB will be observed in B, C, D, and all individuals of these.

In all the above scenarios, mutations which are observed in B and C, plus individuals downstream of B and C, must occur in the window between tAB and the latter of DB and tBC. In each case, we calculate the likelihood of having observed the given number of substitutions from a Poisson distributed variable with expectation given by the rate of evolution γG multiplied by the time available for substitutions to occur, plus the measurement error E/2 where a variant was observed in only one sequence.

In this manner, we derived a phylogenetic hierarchy, dividing the transmission tree into sets of individuals that would be expected to share genetic substitutions; we denote these sets Ia, for a=0, 1, 2, etc. For each such set, we calculate the length of time in which these substitutions would have to occur; we denote these times ta, counting the number of substitutions in each set, denoted Ma. We then have that

PIa,ta,Ma|θ2=aδaE/2+γGtaMae-δaE/2+γGtaMa!

where the term δa is equal to one if Ia contains a single individual and 0 otherwise.

We now construct our final expression. We write G = {{Ia}, {ta}, {Ma}}, and note that G, through the values ta, is dependent on the times of transmission T* and the times of sample collection, which we denote more generally by D = {Dk}. Denoting by y the set of all data collected from the individuals in the network, we then have the result

Py|θ,D,XN=T*=Tkk=1nwikjkTkP(Tk|θ1,Sik)PSjk|θ1,XTkPG|θ2,D,XT*

where XN is the event that the transmission events in the network occurred at some set of times. We convert this into a log likelihood, calculating the likelihood of a given network N:

logLN|y=logPy|θ,D,XN

We find the maximum likelihood network N, and using likelihoods calculated for multiple networks generate statistical ensembles of networks, to estimate, for example, the number of individuals infected by a specific person on a ward.

Identifying plausible networks and orderings

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The likelihood calculation above presupposes the existence of an ordered transmission network with times assigned to each transmission event. Calculations were performed in order to derive these networks.

Plausible networks describe sets of transmission events that could potentially describe a pattern of individuals within a subset. Four criteria were used to identify such networks. Firstly and secondly, networks had to be acyclic, and span all individuals in a subset. Thirdly, networks had to be likely, with each transmission event in the network having a pairwise likelihood that was classified as ‘consistent’ with the pairwise likelihood model. Where computational time allowed it, that is, for all but ward A, this criterion was relaxed to also consider transmission events that were classified as ‘borderline’; details on computability are given later in the Methods. Fourthly, networks had to be consistent with the observed sequence data.

Consistency with sequence data can be described in terms of patterns of shared substitutions observed in sequences from different individuals. We consider a set Ia of all individuals in the subset with viral sequences that share a substitution or set of substitutions. Under a maximum parsimony assumption that variants are gained only once and cannot revert, consistency requires that (i) there can only be one transmission event i→j with i ∉ Ia and j ∈ Ia and (ii) with the exception of this specific individual j in Ia, there can be no transmission events j→k with j ∈ Ia and k ∉ Ia (Figure 5—figure supplement 4). This has to apply for all such sets Ia.

For each subset of individuals created using the pairwise likelihood an exhaustive search of plausible networks was conducted. In the event that no such network was identified, the assumption that the network must span all individuals in the subset was gradually relaxed, reducing the number of individuals required successively by one until at least one plausible network was identified. In some cases, this relaxation led to the identification of multiple plausible networks involving non-overlapping sets of individuals; we return to this later in the Methods section.

Given a plausible network, we next considered orderings of the transmission events that it comprised. To achieve this, transmission events were given a nominal index. In an ordering the placement of the transmission ia→ja before ib→jb implied that ia→ ja occurred before or at an identical time to ib→jb if it was true of the indices that a < b, but implied that ia→ja occurred strictly before ib→jb if a > b.

Plausible orderings were required to fulfil three criteria. Firstly, on a logical principle, ia→ja must occur before ib→jb if ja = ib; an individual must be infected before transmitting the virus. We insisted that an individual who had received the virus via transmission could not themselves transmit until at least one day after they had been infected. Secondly, orderings had to fulfil characteristics imposed upon them by the likelihood function. If the first day on which the pairwise likelihood LP describing transmission between ib and jb was greater than zero was after the last day on which the equivalent likelihood for the transmission between ia and ja was greater than zero, it followed that ia→ja must occur before ib→jb.

A third criterion was imposed by the existence of shared substitutions in the sequence data. Returning to the criteria imposed for network plausibility we suppose that for the set of sequences Ia that contain shared substitutions we have identified the transmission event i→j for which i ∉ Ia but j ∈ Ia, and that there exist a series of transmission events in the network j→k for this j. In this case, the last transmission for which k ∉ Ia must occur before the first transmission for which k ∈ Ia, the substitution event necessarily occurring in individual j between these two times (Figure 5—figure supplement 5). All plausible orderings were stored for each plausible network; we note that the existence of a plausible network did not imply the existence of a plausible ordering of transmission events.

Potential times for transmission events were determined by the first and last time points for which each event had a non-zero pairwise symptomatic likelihood. These times, in addition to the ordering constraints, impose a combinatorial set of possible times at which transmission occurred.

Finally, the likelihood of a transmission network was calculated as the sum over all orderings of the sum over all sets of times of the timings-dependent network likelihood. Denoting the set of all orderings of transmission events as O, and the set of possible sets T* as T**, we have

logLn=logOT**LnT**

Maximum likelihood and uncertainty

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Calculating likelihoods for plausible transmission networks, the maximum likelihood network was identified. Uncertainty about properties of the network, for example whether a network includes a particular transmission event, and the number of individuals infected by each individual, was quantified by Bayesian methods. Assuming a uniform prior over the space of possible networks, the posterior probability that each potential network is the true network is obtained as the likelihood of this specific network divided by the sum of the likelihoods of all possible networks. Further statistics were calculated using these network probabilities. For example, the probability that individual A infected individual B was calculated as the sum of the probabilities of networks for which A infected B, while the probability that A infected a total of n other individuals was calculated as the sum of the probabilities of the networks in which A infected n other individuals.

Where in the initial subsetting of individuals disjoint subsets were identified, independent network reconstructions were calculated for each subset. Where the largest complete network in a subset omitted more than one individual from the subset, repeat calculations, removing those individuals inferred to be in that network, were performed, aiming to identify transmission events between other individuals in the subset.

Computational considerations

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Our algorithm runs at a combinatorial level of complexity dependent upon the number of individuals and the number of plausible paths through the network. We adopted different computational methods to identify maximum likelihood networks and statistical properties. For wards other than A, a rapid and comprehensive calculation of likelihoods for all plausible networks was possible. Uncertainty about properties of the network was then computed over the complete space of possible networks. For ward A, a first calculation was performed, generating likelihoods for a systematically chosen set of 0.2% of the plausible networks. From these networks, a heuristic likelihood threshold was chosen, identifying 10 networks with the highest likelihood values. These networks were used as starting points for independent downhill optimisation calculations. Given each network, the set of adjacent networks was identified, each constructed by the breaking and replacement of a single transmission event. Likelihoods were calculated for these networks, choosing the maximum likelihood network among these before repeating the calculation. Across these independent calculations, convergence to the reported maximum likelihood network was observed.

Uncertainty calculations for ward A were performed initially over the systematic sample of network space, plus networks sampled in the downhill optimisation, plus all networks one or two steps adjacent from the maximum likelihood network; this included a total of 14,040 networks. A second calculation of statistics describing network uncertainty was performed across all these networks but also including any remaining networks three steps adjacent from the maximum likelihood network; this included a total of 19,238 networks. Statistics derived from the smaller and larger ensembles of networks were then compared. The high degree of similarity between these statistics suggests convergence towards the true statistical average (Figure 5—figure supplement 5).

Evaluating superspreading

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Estimates of the distribution of the number of individuals infected by each person in each ward were used to assess the existence of superspreader individuals, comparing the fit of two models to the data.

Our network inference provided for each individual a distribution of the number of people infected, so we could say that each individual i infected j others with probability pij. We used these values to define ‘latent data’ to which we can fit Poisson models describing different hypotheses about heterogeneity in transmission rates. Under a model with no superspreading, the number of individuals infected by each person would be expected to follow a Poisson distribution with some rate R of transmission. The marginal likelihood of this model, defined as the Poisson likelihood integrated over the distribution of the latent number of individuals infected by each person, is derived as

logL1=ilogjpijrje-rj!

This was maximised to estimate the common transmission rate r. We compared this hypothesis to an alternative, negative binomial model. This is equivalent to a model where the transmission rate is different for each person, so that each person infects a number of people described by a Poisson with a random rate drawn from a gamma distribution. We found parameters p and r maximising the marginal likelihood

logL2=ilogjpijr+j-1r-11-pjpr

The marginal likelihoods L1 and L2 were then compared using the Bayesian Information Criterion (Schwarz, 1978) to account for the additional parameters in the second model.

Source of patient infections

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In the maximum likelihood networks, of 22 cases of patients being infected, two were inferred to originate from HCWs, with 20 of these being infected by other patients. We compared these values using a one-tailed test, calculating the cumulative density function of a binomial distribution with N=22 and probability 0.5 for the observed sample.

Data availability statement

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This study utilised 129 SARS-CoV-2 genomes from 98 individuals across the five outbreak wards. The COG-UK and GISAID sequence IDs for these samples are shown in Supplementary files 1 and 2. Genomic data are publicly accessible through the COG-UK website data section (https://www.cogconsortium.uk/data/) and GISAID (https://www.gisaid.org/). Sequences generated through the COG-UK consortium have associated public metadata, including age, sex, collection date (if available), and location to the level of UK county. COG-UK samples are sequenced under statutory powers granted to the UK Public Health Agencies. Matched patient data is securely released to the COG-UK consortium under a data sharing framework which strictly controls the handling of patient data. Information on whether individuals are healthcare workers or patients, and groupings of patients into their shared ward locations in hospital, are not for public release linked to their sequencing identifiers (eg. COG-UK sequence codes). This is because of the risk of deductive disclosure, potentially compromising study participant anonymity. However, code to fully reproduce the transmission network analysis using anonymised metadata and altered SARS-CoV-2 sequences is available via GitHub at https://github.com/cjri/a2bnetwork (Illingworth, 2021; copy archived at swh:1:rev:2c08d1a789b7f1a9ce758a86db27fc3d78b9d003) If a researcher requires access to restricted metadata (including healthcare worker status and patient ward locations) linked to the COG-UK sequence codes, then this will require a formal data sharing agreement with the COG-UK Consortium and Cambridge University Hospitals NHS Foundation Trust (CUH). Data will only be shared for public health and research purposes, not for commercial enterprise, and only to individuals working at reputable research and public health institutions for which data security can be assured. Should this be required researchers should contact the study corresponding authors in the first instance.

Data availability

The SARS-CoV-2 genomic data used in this project are available via the COVID-19 Genomics Consortium UK https://www.cogconsortium.uk/data/ and the GISAID Initiative https://www.gisaid.org/ (Supplementary files 1 and 2). Source data files have been provided for Figures, including original data where figures are made from processed data, and where possible (some original files are very large). Symptom onset and location data for individual patients and health care workers are available from the authors on request; we are not able to share any data publicly which could lead to the identification of individuals in the study. The code used in this project is available from https://github.com/cjri/a2network (copy archived at https://archive.softwareheritage.org/swh:1:rev:2c08d1a789b7f1a9ce758a86db27fc3d78b9d003).

References

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    1. Hamner L
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    High SARS-CoV-2 Attack Rate Following Exposure at a Choir Practice — Skagit County, Washington, March 2020. MMWR
    Morbidity and Mortality Weekly Report.

Decision letter

  1. Aleksandra M Walczak
    Senior Editor; École Normale Supérieure, France
  2. C Brandon Ogbunugafor
    Reviewing Editor; Yale University, United States
  3. Sarah E Cobey
    Reviewer; University of Chicago, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The reviewers agreed that your manuscript was an impressive piece of work, that provided a rigorous and granular perspective on infection dynamics that integrated data of various kinds: clinical, genomic, and demographic. Using these varied approaches, the study provides strong evidence for the importance of superspreading as a key feature of outbreaks. It is our hope that studies that similarly integrate data of various kinds might become the standard for resolving the particulars of outbreaks.

Decision letter after peer review:

Thank you for submitting your article "Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Aleksandra Walczak as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Sarah E Cobey (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Summary:

This study of COVID-19 outbreaks in a major hospital makes two major contributions. First, it suggests that most hospital-acquired infections do not lead to further spread, and that superspreading events occur infrequently. Second, it advances analytic methods for reconstructing transmission networks by combining data on symptoms, behavior, testing, and viral sequences. This will facilitate analyses of other pathogen outbreaks.

Essential Revisions:

Clarifications on some aspects of the HCWs as outlined by the reviewers. Though their inclusion was a strength and focus on their paper, there are certain aspects that were not immediately clear, that readers could benefit from understanding.

These include, amongst others, questions about the conditions under which the HCWs and patients were tested over time, questions about the immune status of HCWs, and questions about the location function for HCW.

Pay special attention to the detailed questions raised by Reviewer #2. I consider these changes to be essential, and I shared some of those curiosities. Make no mistake, however, the reviewers and reviewing editor agree that this manuscript has very strong potential. It is a compelling study with an impressive mix of data sources and methods.

Reviewer #1:

Illingsworth et al. write a compelling and well motivated manuscript on the importance of superspreading in hospital settings.

It is a very well written and important piece of work with methodological advances beyond the application presented here.

I am not an expert in viral phylogenetics and will not be able to speak to the robustness of the evolutionary analysis performed.

This work is particularly strong in its adaptation of statistical techniques to identify transmission networks in highly sampled environments. Even though this is of great interest for some settings I wonder whether the authors had the ability to test their models predictive performance (i.e., if the authors withheld some data and tried to explain future case trajectories in the hospital). This may well be out of scope for this analysis but would make the results be generalisable beyond one particular setting. It would also provide additional support for the robustness of the method. It is very hard to evaluate whether the transmission networks reconstructed are indeed explained by the model applied here given the large number of parameters and somewhat long time of infectiousness of SARS-CoV-2.

The authors state that the sampling of infections within wards was nearly complete which is a major strength of this paper. Usually there are large biases in genomic sampling. It is remarkable that even asymptomatics here are sampled and sequenced if/when positive. However, there is asymmetry in sampling: within the wards it is high but what could the contribution of visitors have been?

Whereas the authors work is very illustrative I wondered after reading it whether there should be more information about the patients characteristics (age, sex, symptoms etc.).

Overall, I enjoyed reading this work!

Reviewer #2:

This study investigates the spread of SARS-CoV-2 in several wards of a hospital in the spring of 2020, aiming to measure transmissibility among patients and healthcare workers (HCWs). The investigators ambitiously incorporate seemingly all relevant data sources into a unified statistical framework in their attempt to reconstruct transmission networks.

The conclusions of the study appear well supported by the data, assuming a few uncertainties in my reading (discussed elsewhere) are resolved. A strength of the analysis is that transmission events are linked by not only the spatiotemporal proximity of individuals' infections but also the genetic similarity of their viruses, and this is a relatively circumscribed population (i.e., patient movement and HCW shift assignments are well known). This is thus an unusually well resolved transmission network over an extended period.

One of the study's claims appears weakly supported by the current analysis. The authors investigate whether Ct values, a measure of viral load, correlate with superspreading. Since Ct values are well known to vary over the course of an infection, some adjustment should probably be made for time of symptom onset, and asymptomatics potentially excluded. Otherwise there is a risk that any correlation between Ct values and increased transmissibility will be masked by variability in relative sampling times. (Transmissibility should probably not be assigned a binary variable either.) Relatedly, correlations between transmissibility and presence of, e.g., fever, would require a larger analysis, and I suggest the authors qualify their language about "identified risk factors."

An important public health consideration is whether it is appropriate to describe these as superspreading individuals or superspreading events. My impression is that the two are not clearly identifiable here, but the authors seem well positioned to address this ongoing debate directly. Additionally, it seems possible that the statistical breakdown estimated here (80% of cases are caused by 20% of infections) is distorted by the investigators' decision to analyze the wards with the largest outbreaks; if other wards were included, the estimated contribution of superspreading might be lower.

This paper presents a real methodological contribution in showing how diverse data sources can be integrated in a statistically coherent way. This will be invaluable in future outbreak investigations and will (I hope) motivate better surveillance, especially collection of date of symptom onset + sequences. In addition to an unusually clear description of the methods, especially for analyses of this complexity, the authors have commendably made their code publicly available, and it appears to be well documented. Future work will likely relax some of the assumptions here, e.g., by allowing unobserved intermediate infections and potentially allowing greater variation in mutation rates and serial intervals in immunocompromised hosts.

This work appears technically sound, but the following clarifications/checks would be really useful:

1. Under what conditions were HCWs and patients tested in the wards over time? How confident can we be that unobserved infections did not occur? Was there regular screening for asymptomatics (as suggested in the Discussion, lines 268-270)? When were visitors allowed?

2. Did the authors confirm that separate models were justified for the wards? Was there evidence of transmission b/w wards, perhaps involving the same HCWs?

3. In interpreting the transmissibility and susceptibility of HCWs and patients, is there any reason to think that many of the HCWs (especially in the red ward) might already have immunity from prior infection?

4. Are the results affected substantially if we assume that the location function for the HCWs is +/- 0.5 d instead of 1 d? The 1-d assumption could implicate HCWs unnecessarily. Fomite transmission seems hard to justify.

Further clarifications suggested for the text:

5. Please include symptom definitions and whether they varied by case type (HAI, HCW) and also describe the wards a bit more fully, if possible (square footage? patients per room?)

6. It would be really nice to have a time series of infections by ward relative to some arbitrary day 0. I think there should be some way to do this that would not violate privacy.

7. In the Discussion (lines 257-278), the authors recommend masks and hand hygiene. Are there any citations for the latter? If not, I might say something more vague (or mention only the former, with references).

8. I found the description of the network analysis of patient bed movements hard to follow (paragraph starting l. 373). What "network analysis" does FoodChain Lab do exactly? The listed criteria seemed straightforward to apply.

9. Line 431: Separate references and CIs here would help.

10. Line 445: Typo in the lognormal component → "((log(x) -mu)^2)"

11. Lines 658-661: Subscript typo in this criterion?

12. Lines 693, 696: There seems to be a major contradiction between "all possible networks" and all networks meeting the condition where individual i infects n others.

13. Is there any way to add schematics for the later steps of the analysis? I greatly appreciated what the authors already provided.

https://doi.org/10.7554/eLife.67308.sa1

Author response

Essential Revisions:

Clarifications on some aspects of the HCWs as outlined by the reviewers. Though their inclusion was a strength and focus on their paper, there are certain aspects that were not immediately clear, that readers could benefit from understanding.

These include, amongst others, questions about the conditions under which the HCWs and patients were tested over time, questions about the immune status of HCWs, and questions about the location function for HCW.

Pay special attention to the detailed questions raised by Reviewer #2. I consider these changes to be essential, and I shared some of those curiosities. Make no mistake, however, the reviewers and reviewing editor agree that this manuscript has very strong potential. It is a compelling study with an impressive mix of data sources and methods.

We thank the reviewers for their comments on our work. In addition to the responses to

reviewers noted below we note a slight change in the underlying A2B-COVID software

package, which has been in development in parallel to this project and is used for the

pairwise assessment of events. This change leads to our method being fractionally more

inclusive when assessing events in a pairwise manner, leading to more transmission events being inferred, but without a substantial change in the overall results.

Reviewer #2:

This study investigates the spread of SARS-CoV-2 in several wards of a hospital in the spring of 2020, aiming to measure transmissibility among patients and healthcare workers (HCWs). The investigators ambitiously incorporate seemingly all relevant data sources into a unified statistical framework in their attempt to reconstruct transmission networks.

The conclusions of the study appear well supported by the data, assuming a few uncertainties in my reading (discussed elsewhere) are resolved. A strength of the analysis is that transmission events are linked by not only the spatiotemporal proximity of individuals' infections but also the genetic similarity of their viruses, and this is a relatively circumscribed population (i.e., patient movement and HCW shift assignments are well known). This is thus an unusually well resolved transmission network over an extended period.

One of the study's claims appears weakly supported by the current analysis. The authors investigate whether Ct values, a measure of viral load, correlate with superspreading. Since Ct values are well known to vary over the course of an infection, some adjustment should probably be made for time of symptom onset, and asymptomatics potentially excluded. Otherwise there is a risk that any correlation between Ct values and increased transmissibility will be masked by variability in relative sampling times. (Transmissibility should probably not be assigned a binary variable either.) Relatedly, correlations between transmissibility and presence of, e.g., fever, would require a larger analysis, and I suggest the authors qualify their language about "identified risk factors."

We have used more cautious language in this paragraph, now saying that the study “may suggest possible risk factors” for the specific cases examined, but that the dataset is too small to draw general conclusions on superspreader characteristics. We have also

emphasised this in the Discussion paragraph on study limitations:

The number of superspreader individuals identified in this study (five) is too small to draw general conclusions on superspreader characteristics. Moreover, it is not possible to disentangle whether superspreading was driven mainly by individual factors (such as infectivity or behaviour) or environmental factors (such as patient placement and ventilation at time of peak infectivity), or a combination of these. PCR cycle threshold (Ct) can vary for many reasons including the timing of sampling during COVID-19 infection, sampling type and technique, viral transport, sample preparation and variability between PCR runs. The finding that Ct values did not vary significantly between superspreader and non-superspreader individuals should therefore be interpreted with caution.

An important public health consideration is whether it is appropriate to describe these as superspreading individuals or superspreading events. My impression is that the two are not clearly identifiable here, but the authors seem well positioned to address this ongoing debate directly. Additionally, it seems possible that the statistical breakdown estimated here (80% of cases are caused by 20% of infections) is distorted by the investigators' decision to analyze the wards with the largest outbreaks; if other wards were included, the estimated contribution of superspreading might be lower.

Our model identifies individuals who appear to transmit the virus to a greater number of individuals than expected, and we identify a statistical pattern of overdispersion in the number of infections caused per individual. However, as above, we have included as a limitation in the Discussion the difficulty in disentangling between superspreader individuals vs superspreader events. With a relatively small set of superspreaders (five) our data cannot produce robust characteristics of superspreaders and we therefore do not reach firm conclusions on this.

This paper presents a real methodological contribution in showing how diverse data sources can be integrated in a statistically coherent way. This will be invaluable in future outbreak investigations and will (I hope) motivate better surveillance, especially collection of date of symptom onset + sequences. In addition to an unusually clear description of the methods, especially for analyses of this complexity, the authors have commendably made their code publicly available, and it appears to be well documented. Future work will likely relax some of the assumptions here, e.g., by allowing unobserved intermediate infections and potentially allowing greater variation in mutation rates and serial intervals in immunocompromised hosts.

We accept that our method is a starting point in integrating different forms of data and hope to push back some of its current limitations in future work.

This work appears technically sound, but the following clarifications/checks would be really useful:

1. Under what conditions were HCWs and patients tested in the wards over time? How confident can we be that unobserved infections did not occur? Was there regular screening for asymptomatics (as suggested in the Discussion, lines 268-270)? When were visitors allowed?

Patients and HCW had separate testing criteria and sample workflows. HCW were tested in the CUH HCW screening programme, which included both asymptomatic screening and symptomatic testing arms. Asymptomatic screening at the time of this study was focused on staff working on COVID-19 "red" wards (designated for patients with confirmed COVID-19 infection), wards with hospital-acquired infection outbreaks, and wards with high rates of staff positivity. Suggested symptomatology to prompt staff testing are described in Rivett et al., divided into "major" criteria (eg. fever and/or new persistent cough) and "minor" criteria (eg. coryzal symptoms, headache, myalgia) (see Table 1 of Rivett et al., eLife 2020;9:e58728).

For all five of the outbreaks described in this study, all staff working on the outbreak wards were screened by the CUH HCW screening team (i.e. tested regardless of any symptoms or if asymptomatic) during the outbreak periods (prompted by the outbreak investigations and/or high rates of staff positivity on the wards). It is theoretically possible that HCW could have caught COVID-19 early on in the outbreaks and cleared the virus quickly, becoming negative at time of testing, or caught the virus asymptomatically after the screening test, or had levels of virus below the detection limit of the assay (and thus have been false negatives). However, levels of SARS-CoV-2 RNA below the assay detection limit are unlikely to be infectious (and thus not significant for the inferred transmission networks).

There was not systematic asymptomatic screening for inpatients in CUH during the study period, but targeted patient screening on wards with hospital-onset COVID-19 outbreaks was performed. Ward E was a COVID-19 “red” ward; all patients on this ward had tested positive prior to placement there. Wards A-D were all “green” wards (designated for non-COVID-19 patients) at the time the hospital-onset COVID-19 outbreaks started. For Ward A, symptomatic contacts of confirmed cases were tested initially, and as the outbreak grew and more cases were confirmed, ultimately all patients on the ward were screened (including asymptomatics). For Ward C, contacts of confirmed positives and/or patients who developed symptoms were screened, and for Ward D, symptomatic contacts of the index case were tested. Thus, for Wards C and D, systematic asymptomatic screening of all patients on the ward during the outbreaks was not performed, and it is possible some asymptomatic infections (that could have contributed to the transmission networks) were missed. We have added this as a study limitation in the discussion. For the Ward B outbreak, when the index patient (case B0) tested positive, all staff members who had worked on Ward B within the preceding 2 weeks plus all patients on that ward were screened (regardless of any symptoms). Thus, there is high confidence for Wards A, B and E that all infections among both staff and patients were detected (providing the amount of viral RNA was sufficient for detection).

Visitor restrictions were introduced 25th March 2020 and so were present for almost all of this study (first positive swab was for Ward E, collected a few days before this). Visitors were only permitted in exceptional circumstances: for patients at the end of life, visitors with a direct care role for the patient, or the parent of a child (not relevant here, as all of the wards were for adult patients).

The above has been added as a new section to the Methods, section ‘Testing criteria’. The issue of missed infections in the transmission networks has been expanded on in the Discussion, paragraph starting ‘We acknowledge several limitations to our study’.

2. Did the authors confirm that separate models were justified for the wards? Was there evidence of transmission b/w wards, perhaps involving the same HCWs?

Separate models were justified by the data we have, as shown in a new Figure 1. While no HCWs worked on multiple outbreak wards, we cannot exclude unrecorded staff contact outside of these wards (during lunch, outside of work, etc.) We now preface our analysis with a pairwise analysis of the data across all wards. This analysis suggests that

transmission between individuals on different wards was unlikely, but cannot definitively rule out such an event having occurred.

3. In interpreting the transmissibility and susceptibility of HCWs and patients, is there any reason to think that many of the HCWs (especially in the red ward) might already have immunity from prior infection?

A study of SARS-CoV-2 serology conducted at Cambridge University Hospitals NHS

Foundation Trust found that, between 10th June and 7th August 2020, 410/5,698 (7.2%) staff tested positive for SARS-CoV-2 antibodies

(https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3724855). As expected, prevalence was higher for staff working in COVID-19 "red" wards than in “green” wards (9.47% versus 6.16%). Not all people with detectable SARS-CoV-2 antibodies will have clinically protective immunity. Our study of the five “outbreak” wards was based earlier in the pandemic than this serology study (March – June 2020), when staff seroprevalence would have been lower. The proportion of staff with neutralising antibodies during the outbreaks studied here was therefore low, likely <5% in March 2020. Thus, it likely played a minor role in transmission dynamics. This has been added to the discussion, paragraph starting ‘Our inferences of transmission were performed on the basis of a largely complete dataset’.

4. Are the results affected substantially if we assume that the location function for the HCWs is +/- 0.5 d instead of 1 d? The 1-d assumption could implicate HCWs unnecessarily. Fomite transmission seems hard to justify.

This does not make a large difference to the results obtained. In the framework of this

method we consider time in discrete intervals of one day. Under the default setting our

method assigns HCWs as being present on a ward with probability one half during an

interval of +/- 1 days from any days on which they are recorded as being present. This was designed to account for fomite transmission, but also for multiple other factors, including the possibility of night shifts spanning more than one day, but being recorded as presence on a single day. We have now generalised our method to allow this probability to be input as an arbitrary value.

We reran calculations in which this probability was set to zero. This leads to some changes to the details of the inference, as shown in a new Figure 5S1, but no change in the headline result of 21% of infections causing 80% of transmission events.

Further clarifications suggested for the text:

5. Please include symptom definitions and whether they varied by case type (HAI, HCW) and also describe the wards a bit more fully, if possible (square footage? patients per room?).

Re: Symptom definitions – these were collected separately for HCW and patients. HCW

testing was part of the CUH HCW screening programme, and testing criteria are described in Rivett et al., (Table 1). Staff who tested positive were then contacted by members of the HCW screening team and asked retrospectively about their symptoms, and onset dates were recorded by the HCW screening team and used in this analysis. For patients, symptom onset dates were collected by retrospective review of patients’ electronic hospital records (EPIC Systems), usually noted at presentation by the clerking doctor but all records were examined for any suggestion of symptoms. Symptom definitions followed the standard national recommendations at the time: initially fever, breathlessness and new continuous cough; muscle aches was added in early May and loss of taste or smell was included from mid-May.

This has been added to Methods, ‘Data collection’ section.

Re: Ward descriptions – We are unable to provide precise information about the wards in order to preserve confidentiality. The wards are typically organised into bays, with 4 to 6 patients per bay, and a limited number of side rooms (which are critical for infection control purposes). In summary, Ward A had 30 beds with 3 side rooms; Ward C had 27 beds with 3 side rooms; Ward D had 30 beds with 4 side rooms; Ward E had 26 beds with 3 side rooms. The Ward B outbreak was focused around a ward with 32 beds of which 12 were side rooms, although several adjacent wards were screened as part of the outbreak investigation (as staff were shared between these wards).

This has been added to Methods, ‘Sample selection’ section.

6. It would be really nice to have a time series of infections by ward relative to some arbitrary day 0. I think there should be some way to do this that would not violate privacy.

We have added a new Figure 5 containing symptom onset dates and dates at which

sequence data were collected, noting that not all samples led to high quality sequences. This information has been normalised to day 0 for each ward; this is the maximum we are able to make public given the other information we share.

7. In the Discussion (lines 257-278), the authors recommend masks and hand hygiene. Are there any citations for the latter? If not, I might say something more vague (or mention only the former, with references).

This is in accordance with PHE guidance. The specific question of the role of hand hygiene has been addressed by the UK Government Scientific Advisory Group for Emergencies (SAGE) here:

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/897598/S0574_NERVTAG-EMG_paper_-_hand_hygiene_010720_Redacted.pdf

However we have made the sentence more generally refer to infection control practices:

First, scrupulous adherence to infection control practices including appropriate

personal protective equipment (PPE) at all times, even on green wards and in nonclinical hospital areas, is required to limit transmission between asymptomatic patients and staff in which COVID-19 is not suspected.

8. I found the description of the network analysis of patient bed movements hard to follow (paragraph starting l. 373). What "network analysis" does FoodChain Lab do exactly? The listed criteria seemed straightforward to apply.

SQL was used to identify patient co-locations that met our set parameters and these were then imported into FoodChain Lab to draw the network diagram.

We have expanded on this paragraph to make the methods clearer, new text in red font (in Methods, section ‘Sample selection’):

An initial network analysis of patient bed movements was conducted to add patients to each ward cluster that may have been in contact with the HAI cases, either with community onset infections on the same ward or while they were co-located on other wards outside of the “outbreak wards” themselves. This analysis was undertaken in two steps using SQL v18.5.1 and FoodChain-Lab (an extension of the Knime Analytics Platform v3.6.1). The first step involved utilising SQL to process case and ward movements data from CUH patients, creating a list of ward-based case co-locations that were within the set parameters of the network analysis model. These parameters were (1) an infectious period that included the 4 days prior to symptom onset up until 7 days after symptom onset, and (2) a susceptibility period of 14 days prior to symptom onset. Where symptom onset date was not available, the case positive specimen date was used instead. The second step was to import the case colocations data into FoodChain-Lab, which was used to draw a social network diagram of cases and their ward co-locations with one another that met the set parameters. This network diagram was used to identify any clustering of cases by ward that had not met the original criteria of HAI and HCW cases but could have been involved in shared transmission with those individuals based on their co-location within the infection period of the virus. This yielded the final case set for each ward cluster taken forward for analysis using the transmission reconstruction model.

9. Line 431: Separate references and CIs here would help.

We have separated the references in order to clarify the origin of these parameters. We

could not find confidence intervals for these parameters in the literature, albeit they could be obtained by repeating the work of the papers cited. For the purpose of our study we utilised the estimated parameters from the literature cited.

10. Line 445: Typo in the lognormal component → "((log(x) -mu)^2)"

Fixed.

11. Lines 658-661: Subscript typo in this criterion?

Fixed.

12. Lines 693, 696: There seems to be a major contradiction between "all possible networks" and all networks meeting the condition where individual i infects n others.

We don’t think there is a contradiction and have rewritten these lines to clarify the matter. Given likelihoods for all possible networks, the probability of a specific network was calculated as its likelihood divided by the sum of all likelihoods. The probability that individual i infects n others is then the sum of the probabilities of all of the networks in which i infects n others.

13. Is there any way to add schematics for the later steps of the analysis? I greatly appreciated what the authors already provided.

We have added another figure (5S4) showing how sequence variants constrain which

transmission events are possible, and the order in which they occur; we think this provides clarification of two points in the methods.

https://doi.org/10.7554/eLife.67308.sa2

Article and author information

Author details

  1. Christopher JR Illingworth

    1. MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge, United Kingdom
    2. Institut für Biologische Physik, Universität zu Köln, Köln, Germany
    3. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Cambridge, United States
    Contribution
    Conceptualization, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    William L Hamilton
    For correspondence
    cjri2@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0030-2784
  2. William L Hamilton

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    Christopher JR Illingworth
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3330-353X
  3. Ben Warne

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Matthew Routledge

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Ashley Popay

    Public Health England Field Epidemiology Unit, Cambridge Institute of Public Health, Forvie Site, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Chris Jackson

    MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge, United Kingdom
    Contribution
    Formal analysis, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Tom Fieldman

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Luke W Meredith

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Charlotte J Houldcroft

    University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1833-5285
  10. Myra Hosmillo

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3514-7681
  11. Aminu S Jahun

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Data curation, Investigation, Writing - review and editing
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4585-1701
  12. Laura G Caller

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Sarah L Caddy

    Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9790-7420
  14. Anna Yakovleva

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Grant Hall

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3928-3979
  16. Fahad A Khokhar

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  17. Theresa Feltwell

    University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  18. Malte L Pinckert

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  19. Iliana Georgana

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8976-1177
  20. Yasmin Chaudhry

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  21. Martin D Curran

    Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    No competing interests declared
  22. Surendra Parmar

    Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    No competing interests declared
  23. Dominic Sparkes

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  24. Lucy Rivett

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2781-9345
  25. Nick K Jones

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Data curation, Investigation, Writing - review and editing
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4475-7761
  26. Sushmita Sridhar

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
    3. Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7453-7482
  27. Sally Forrest

    Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
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    Data curation, Investigation, Writing - review and editing
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    No competing interests declared
  28. Tom Dymond

    Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Data curation, Investigation, Writing - review and editing
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    No competing interests declared
  29. Kayleigh Grainger

    Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Data curation, Investigation, Writing - review and editing
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    No competing interests declared
  30. Chris Workman

    Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Data curation, Investigation, Writing - review and editing
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    No competing interests declared
  31. Mark Ferris

    Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5040-4263
  32. Effrossyni Gkrania-Klotsas

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. MRC Epidemiology Unit, University of Cambridge, Level 3 Institute of Metabolic Science, Cambridge, United Kingdom
    3. University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    Data curation, Funding acquisition, Investigation, Writing - review and editing
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0930-8330
  33. Nicholas M Brown

    Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6657-300X
  34. Michael P Weekes

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
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    Resources, Data curation, Funding acquisition, Investigation
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3196-5545
  35. Stephen Baker

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
    Contribution
    Resources, Data curation, Funding acquisition, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1308-5755
  36. Sharon J Peacock

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
    3. Public Health England, National Infection Service, London, United Kingdom
    Contribution
    Supervision, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1718-2782
  37. Ian G Goodfellow

    University of Cambridge, Department of Pathology, Division of Virology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9483-510X
  38. Theodore Gouliouris

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    3. Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Supervision, Validation, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  39. Daniela de Angelis

    1. Institut für Biologische Physik, Universität zu Köln, Köln, Germany
    2. Public Health England, National Infection Service, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  40. M Estée Török

    1. University of Cambridge, Department of Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9098-8590

Funding

Wellcome Trust (108070/Z/15/Z)

  • Michael P Weekes

Wellcome Trust (215515/Z/19/Z)

  • Stephen Baker

Wellcome Trust (207498/Z/17/Z)

  • Ian G Goodfellow

Wellcome Trust (204870/Z/16/Z)

  • Charlotte J Houldcroft

Academy of Medical Sciences

  • M Estée Török

NIHR

  • Ben Warne
  • M Estée Török

National Institute for Health Research

  • Effrossyni Gkrania-Klotsas

Medical Research Council (MC_UU_00002/11)

  • Christopher J R Illingworth
  • Chris Jackson
  • Daniela de Angelis

Deutsche Forschungsgemeinschaft (SFB 1310)

  • Christopher J R Illingworth

The Health Foundation

  • M Estée Török

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by COG-UK, which is supported by funding from the Medical Research Council (MRC) part of UK Research and Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute; We also acknowledge the support from the Wellcome (Senior Clinical Fellowship to MPW (ref: 108070/Z/15/Z), Senior Research Fellowship to SB (ref: 215515/Z/19/Z), Senior Fellowship to IG (ref: 207498/Z/17/Z); Collaborative Grant to CJH (ref: 204870/Z/16/Z)); the Academy of Medical Sciences and the Health Foundation (Clinician Scientist Fellowship to MET), the National Institute for Health Research Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust (to BW, MET); the NIHR Clinical Research Network Greenshoots award (to EGK); and MRC core funding (MC_UU_00002/11, for CJRI, DDA). CJRI acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) Grant SFB 1310.

Ethics

Human subjects: This study was conducted as part of surveillance for COVID-19 infections under the auspices of Section 251 of the NHS Act 2006. It therefore did not require individual patient consent or ethical approval. The COG-UK study protocol was approved by the Public Health England Research Ethics Governance Group (reference: R&D NR0195).

Senior Editor

  1. Aleksandra M Walczak, École Normale Supérieure, France

Reviewing Editor

  1. C Brandon Ogbunugafor, Yale University, United States

Reviewer

  1. Sarah E Cobey, University of Chicago, United States

Publication history

  1. Received: February 7, 2021
  2. Accepted: July 15, 2021
  3. Version of Record published: August 24, 2021 (version 1)
  4. Version of Record updated: September 2, 2021 (version 2)

Copyright

© 2021, Illingworth et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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