Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Adam J Kucharski; Timothy W Russell; Charlie Diamond; Yang Liu; John Edmunds; Sebastian Funk; Rosalind M Eggo

There are several challenges to such analysis, however, particularly in real-time. There can be a delay to symptom appearance resulting from the incubation period and delay to confirmation of cases resulting from detection and testing capacity (9) . Modelling approaches can account for such delays and uncertainty, by explicitly incorporating delays resulting from the natural history of infection and reporting processes (10) . In addition, individual data sources may be biased, incomplete, or only capture certain aspects of the outbreak dynamics. Evidence synthesis approaches, which fit to multiple data sources rather than a single dataset (or data point) can enable more robust estimation of the underlying dynamics of transmission from noisy data (11, 12) . Combining a mathematical model of SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December and February 2020. We then used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases are introduced.