Challenges in control of Covid-19: short doubling time and long delay to effect of interventions

Lorenzo Pellis; Francesca Scarabel; Helena B Stage; Christopher E Overton; Lauren H K Chappell; Katrina A Lythgoe; Elizabeth Fearon; Emma Bennett; Jacob Curran-Sebastian; Rajenki Das; Martyn Fyles; Hugo Lewkowicz; Xiaoxi Pang; Bindu Vekaria; Luke Webb; Thomas A House; Ian Hall

Each page of Figure S1 shows the GAM compared to a simple GLM with θ = 1 and T taken to be all of the data range, in contrast to the results in the main paper and Figure S2 , where we fit θ and let T correspond to the first nine days of the local epidemic after the cumulative number of cases has reached 25 (the only exceptions are the UK and Romania, where fitting started an additional 9 days later to reflect the local situation). While the simple GLM method is clearly inadequate if the fit is not restricted to a window where exponential growth appears reasonable, it is shown for comparison in these plots. The left panel shows the output of the model fit and the data, the middle panel the instantaneous growth rate from GAM (black) and the growth rate from GLM (red) with 95% CI, and the right most panel shows these growth rates converted to doubling times. Of the fifteen countries, two (Belgium and Romania) have equivalent fits from GAM as from GLM and show constant growth rates over the time period. The Czech Republic, Greece, Ireland and Poland have the central GLM result within the 95% CI of the GAM suggesting a constant growth rate is a plausible explanation of the data reported. Austria, France, Italy, Portugal, Spain, Switzerland show a fairly smooth transition from short to longer doubling times. Germany, Netherlands and UK show more oscillatory behaviour in doubling times.