Superspreaders drive the largest outbreaks of hospital onset covid-19 infections

Christopher Illingworth; William L. Hamilton; Ben Warne; Matthew Routledge; Ashley Popay; Chris Jackson; Tom Fieldman; Luke W. Meredith; Charlotte J. Houldcroft; Myra Hosmillo; Aminu S. Jahun; Laura G. Caller; Sarah L. Caddy; Anna Yakovleva; Grant Hall; Fahad A. Khokhar; Theresa Feltwell; Malte L. Pinckert; Iliana Georgana; Yasmin Chaudhry; Martin D. Curran; Surendra Parmar; Dominic Sparkes; Lucy Rivett; Nick K. Jones; Sushmita Sridhar; Sally Forrest; Tom Dymond; Kayleigh Grainger; Chris Workman; Mark Ferris; Effrossyni Gkrania-Klotsas; Nicholas M. Brown; Michael P. Weekes; Stephen Baker; Sharon J. Peacock; Ian G. Goodfellow; Theodore Gouliouris; Daniela De Angelis; M. Estee Torok

doi:10.7554/eLife.67308

Superspreaders drive the largest outbreaks of hospital onset covid-19 infections

Christopher Illingworth^*, William L. Hamilton, Ben Warne, Matthew Routledge, Ashley Popay, Chris Jackson, Tom Fieldman, Luke W. Meredith, Charlotte J. Houldcroft, Myra Hosmillo, Aminu S. Jahun, Laura G. Caller, Sarah L. Caddy, Anna Yakovleva, Grant Hall, Fahad A. Khokhar, Theresa Feltwell, Malte L. Pinckert, Iliana Georgana, Yasmin ChaudhryMartin D. Curran, Surendra Parmar, Dominic Sparkes, Lucy Rivett, Nick K. Jones, Sushmita Sridhar, Sally Forrest, Tom Dymond, Kayleigh Grainger, Chris Workman, Mark Ferris, Effrossyni Gkrania-Klotsas, Nicholas M. Brown, Michael P. Weekes, Stephen Baker, Sharon J. Peacock, Ian G. Goodfellow, Theodore Gouliouris, Daniela De Angelis, M. Estee Torok

^*Corresponding author for this work

Cambridge Lab Addenbrookes

Research output: Contribution to journal › Article › peer-review

36 Citations (Scopus)

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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.

Original language	English
Article number	67308
Number of pages	24
Journal	eLife
Volume	10
DOIs	https://doi.org/10.7554/eLife.67308
Publication status	Published - 24 Aug 2021

Bibliographical note

Funding Information: 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: 097997/Z/11/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 For- schungsgemeinschaft (DFG) Grant SFB 1310.

Open Access: 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.

Publisher Copyright: © Illingworth et al.

Citation: Illingworth et al, Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections, eLife 2021;10:e67308

DOI: 10.7554/eLife.67308

Keywords

TRANSMISSION TREES
DISEASES

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Illingworth2021SuperspreaderDriveTheLargestOutbreaksOfHospitalOnsetCOVID-19InfectionsELifeFinal published version, 644 KBLicence: CC BY

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Illingworth, C., Hamilton, W. L., Warne, B., Routledge, M., Popay, A., Jackson, C., Fieldman, T., Meredith, L. W., Houldcroft, C. J., Hosmillo, M., Jahun, A. S., Caller, L. G., Caddy, S. L., Yakovleva, A., Hall, G., Khokhar, F. A., Feltwell, T., Pinckert, M. L., Georgana, I., ... Estee Torok, M. (2021). Superspreaders drive the largest outbreaks of hospital onset covid-19 infections. eLife, 10, Article 67308. https://doi.org/10.7554/eLife.67308

@article{1ef2930cc2814dd3bb1e906288fea911,

title = "Superspreaders drive the largest outbreaks of hospital onset covid-19 infections",

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.",

keywords = "TRANSMISSION TREES, DISEASES",

author = "Christopher Illingworth and Hamilton, {William L.} and Ben Warne and Matthew Routledge and Ashley Popay and Chris Jackson and Tom Fieldman and Meredith, {Luke W.} and Houldcroft, {Charlotte J.} and Myra Hosmillo and Jahun, {Aminu S.} and Caller, {Laura G.} and Caddy, {Sarah L.} and Anna Yakovleva and Grant Hall and Khokhar, {Fahad A.} and Theresa Feltwell and Pinckert, {Malte L.} and Iliana Georgana and Yasmin Chaudhry and Curran, {Martin D.} and Surendra Parmar and Dominic Sparkes and Lucy Rivett and Jones, {Nick K.} and Sushmita Sridhar and Sally Forrest and Tom Dymond and Kayleigh Grainger and Chris Workman and Mark Ferris and Effrossyni Gkrania-Klotsas and Brown, {Nicholas M.} and Weekes, {Michael P.} and Stephen Baker and Peacock, {Sharon J.} and Goodfellow, {Ian G.} and Theodore Gouliouris and {De Angelis}, Daniela and {Estee Torok}, M.",

note = "Funding Information: 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: 097997/Z/11/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 For- schungsgemeinschaft (DFG) Grant SFB 1310. Open Access: 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. Publisher Copyright: {\textcopyright} Illingworth et al. Citation: Illingworth et al, Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections, eLife 2021;10:e67308 DOI: 10.7554/eLife.67308",

year = "2021",

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language = "English",

volume = "10",

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Illingworth, C, Hamilton, WL, Warne, B, Routledge, M, Popay, A, Jackson, C, Fieldman, T, Meredith, LW, Houldcroft, CJ, Hosmillo, M, Jahun, AS, Caller, LG, Caddy, SL, Yakovleva, A, Hall, G, Khokhar, FA, Feltwell, T, Pinckert, ML, Georgana, I, Chaudhry, Y, Curran, MD, Parmar, S, Sparkes, D, Rivett, L, Jones, NK, Sridhar, S, Forrest, S, Dymond, T, Grainger, K, Workman, C, Ferris, M, Gkrania-Klotsas, E, Brown, NM, Weekes, MP, Baker, S, Peacock, SJ, Goodfellow, IG, Gouliouris, T, De Angelis, D & Estee Torok, M 2021, 'Superspreaders drive the largest outbreaks of hospital onset covid-19 infections', eLife, vol. 10, 67308. https://doi.org/10.7554/eLife.67308

TY - JOUR

T1 - Superspreaders drive the largest outbreaks of hospital onset covid-19 infections

AU - Illingworth, Christopher

AU - Hamilton, William L.

AU - Warne, Ben

AU - Routledge, Matthew

AU - Popay, Ashley

AU - Jackson, Chris

AU - Fieldman, Tom

AU - Meredith, Luke W.

AU - Houldcroft, Charlotte J.

AU - Hosmillo, Myra

AU - Jahun, Aminu S.

AU - Caller, Laura G.

AU - Caddy, Sarah L.

AU - Yakovleva, Anna

AU - Hall, Grant

AU - Khokhar, Fahad A.

AU - Feltwell, Theresa

AU - Pinckert, Malte L.

AU - Georgana, Iliana

AU - Chaudhry, Yasmin

AU - Curran, Martin D.

AU - Parmar, Surendra

AU - Sparkes, Dominic

AU - Rivett, Lucy

AU - Jones, Nick K.

AU - Sridhar, Sushmita

AU - Forrest, Sally

AU - Dymond, Tom

AU - Grainger, Kayleigh

AU - Workman, Chris

AU - Ferris, Mark

AU - Gkrania-Klotsas, Effrossyni

AU - Brown, Nicholas M.

AU - Weekes, Michael P.

AU - Baker, Stephen

AU - Peacock, Sharon J.

AU - Goodfellow, Ian G.

AU - Gouliouris, Theodore

AU - De Angelis, Daniela

AU - Estee Torok, M.

N1 - Funding Information: 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: 097997/Z/11/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 For- schungsgemeinschaft (DFG) Grant SFB 1310. Open Access: 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. Publisher Copyright: © Illingworth et al. Citation: Illingworth et al, Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections, eLife 2021;10:e67308 DOI: 10.7554/eLife.67308

PY - 2021/8/24

Y1 - 2021/8/24

N2 - 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.

AB - 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.

KW - TRANSMISSION TREES

KW - DISEASES

UR - http://www.scopus.com/inward/record.url?scp=85114407016&partnerID=8YFLogxK

U2 - 10.7554/eLife.67308

DO - 10.7554/eLife.67308

M3 - Article

AN - SCOPUS:85114407016

SN - 2050-084X

VL - 10

JO - eLife

JF - eLife

M1 - 67308

ER -

Superspreaders drive the largest outbreaks of hospital onset covid-19 infections

Abstract

Bibliographical note

Keywords

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