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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.<\/jats:p>","DOI":"10.7554\/elife.67308","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T20:00:25Z","timestamp":1629748825000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.67308","source":"Crossref","is-referenced-by-count":48,"title":["Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-2784","authenticated-orcid":true,"given":"Christopher JR","family":"Illingworth","sequence":"first","affiliation":[{"name":"MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge, United Kingdom"},{"name":"Institut f\u00fcr Biologische Physik, Universit\u00e4t zu K\u00f6ln, K\u00f6ln, Germany"},{"name":"Department 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