Superspreaders Drive the Largest Outbreaks of Hospital Onset COVID-19 Infections

Overview

Journal Elife

Specialty Biology

Date 2021 Aug 24

PMID 34425938

Citations 27

Authors

Christopher Jr 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

Affiliations

Soon will be listed here.

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.

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