Mechanistic model calibration and the dynamics of the COVID-19 epidemic in the UK (the past, the present and the future)

Summary ▪ We augment the well-known susceptible – infected – recovered – deceased ( SIRD ) epidemiological model to include vaccination dynamics, implemented as a piecewise continuous simulation. We calibrate this model to reported case data in the UK at a national level, ▪ Our modelling approach decouples the inherent characteristics of the infection from the degree of human interaction (as defined by the effective reproduction number, R e ). This allows us to detect and infer a change in the characteristic of the infection, for example the emergence of the Kent variant, ▪ We find that that the infection rate constant ( k ) increases by around 89% as a result of the B.1.1.7 (Kent) COVID-19 variant in England, ▪ Through retrospective analysis and modelling of early epidemic case data (between March 2020 and May 2020) we estimate that ∼1.2M COVID-19 infections were unreported in the early phase of the epidemic in the UK. We also obtain an estimate of the basic reproduction number as, R 0 = 3.23, ▪ We use our model to assess the UK Government’s roadmap for easing the third national lockdown as a result of the current vaccination programme. To do this we use our estimated model parameters and a future forecast of the daily vaccination rates of the next few months, ▪ Our modelling predicts an increased number of daily cases as NPIs are lifted in May and June 2021, ▪ We quantify this increase in terms of the vaccine rollout rate and in particular the percentage vaccine uptake rate of eligible individuals, and show that a reduced take up of vaccination by eligible adults may lead to a significant increase in new infections.