@article{Illingworth_C-2021_39949,
title = {Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections},
author = {Illingworth, C. and Hamilton, W. and Warne, B. and Routledge, M. and Popay, A. and Jackson, C. and Fieldman, T. and Meredith, L. and Houldcroft, C. and Hosmillo, M. and Jahun, A. and Caller, L. and Caddy, S. and Yakovleva, A. and Hall, G. and Khokhar, F. and Feltwell, T. and Pinckert, M. and Georgana, I. and Chaudhry, Y. and Curran, M. and Parmar, S. and Sparkes, D. and Rivett, L. and Jones, N. and Sridhar, S. and Forrest, S. and Dymond, T. and Grainger, K. and Workman, C. and Ferris, M. and Gkrania-Klotsas, E. and Brown, N. and Weekes, M. and Baker, S. and Peacock, S. and Goodfellow, I. and Gouliouris, T. and De Angelis, D. and Török, M.},
month = {aug},
year = {2021},
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.},
journal = {Evolutionary Biology Microbiology and Infectious Disease},
publisher = {eLife Sciences Publications},
url = {https://doi.org/10.7554/eLife.67308},
}
