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

Transmission was modelled as a geometric random walk process, and we used sequential Monte Carlo to infer the transmission rate over time, as well as the resulting number of cases and the time-varying basic reproduction number, R t , defined here as the average number of secondary cases generated by a typical infectious individual on each day in a full susceptible population. The model had three unknown parameters, which we estimated: magnitude of temporal variability in transmission, proportion of cases that would eventually be detectable, and relative probability of reporting a confirmed case within Wuhan compared to an internationally exported case originated in Wuhan. We assumed the outbreak started with a single infectious case on 22 nd November 2019 and the entire population was initially susceptible. Once we had estimated R t , we used a branching process with a negative binomial offspring distribution to calculate the probability an introduced case would cause a large outbreak. We also conducted sensitivity analysis on . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.