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Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong.

Dillon C AdamPeng WuJessica Y WongEric Ho Yin LauTim K TsangSimon CauchemezGabriel M LeungBenjamin John Cowling
Published in: Nature medicine (2020)
Superspreading events (SSEs) have characterized previous epidemics of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) infections1-6. For SARS-CoV-2, the degree to which SSEs are involved in transmission remains unclear, but there is growing evidence that SSEs might be a typical feature of COVID-197,8. Using contact tracing data from 1,038 SARS-CoV-2 cases confirmed between 23 January and 28 April 2020 in Hong Kong, we identified and characterized all local clusters of infection. We identified 4-7 SSEs across 51 clusters (n = 309 cases) and estimated that 19% (95% confidence interval, 15-24%) of cases seeded 80% of all local transmission. Transmission in social settings was associated with more secondary cases than households when controlling for age (P = 0.002). Decreasing the delay between symptom onset and case confirmation did not result in fewer secondary cases (P = 0.98), although the odds that an individual being quarantined as a contact interrupted transmission was 14.4 (95% CI, 1.9-107.2). Public health authorities should focus on rapidly tracing and quarantining contacts, along with implementing restrictions targeting social settings to reduce the risk of SSEs and suppress SARS-CoV-2 transmission.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • coronavirus disease
  • public health
  • healthcare
  • machine learning
  • electronic health record
  • big data
  • cancer therapy
  • artificial intelligence