A2B-COVID: A method for evaluating potential SARS-CoV-2 transmission events

Illingworth, C. J. R.; Hamilton, W. L.; Jackson, C. H.; Popay, A.; Meredith, L.; Houldcroft, C. J.; Hosmillo, M.; Jahun, A.; Routledge, M.; Warne, B.; Caller, L.; Caddy, S.; Yakovleva, A.; Hall, G.; Khokhar, F. A.; Feltwell, T.; Pinckert, M.; Georgana, I.; Chaudhry, Y.; Curran, M.; Parmar, S.; Sparkes, D.; Rivett, L.; Jones, N. K.; Sridhar, S.; Forest, S.; Dymond, T.; Grainger, K.; Workman, C.; Gkrania-Klotsas, E.; Brown, N. M.; Weekes, M.; Baker, S.; Peacock, S. J.; Gouliouris, T.; Goodfellow, I. G.; de Angelis, D.; Torok, M. E.

Identifying linked cases of infection is a key part of the public health response to viral infectious disease. Viral genome sequence data is of great value in this task, but requires careful analysis, and may need to be complemented by additional types of data. The Covid-19 pandemic has highlighted the urgent need for analytical methods which bring together sources of data to inform epidemiological investigations. We here describe A2B-COVID, an approach for the rapid identification of linked cases of coronavirus infection. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and novel approaches to genome sequence data to assess whether or not cases of infection are consistent or inconsistent with linkage via transmission. We apply our method to analyse and compare data collected from two wards at Cambridge University Hospitals, showing qualitatively different patterns of linkage between cases on designated Covid-19 and non-Covid-19 wards. Our method is suitable for the rapid analysis of data from clinical or other potential outbreak settings.