So, the third wave is officially no more. New modelling by SPI-M, the government’s committee on modelling for pandemics, has, at a stroke, eradicated the predicted surge in new infections, hospital admissions and deaths which it had pencilled in for the autumn or winter as a result of lockdown being eased.
Previous modelling published in April suggested that we could end up with 20,000 in hospital — higher than during the first peak last April. Now the third wave is looking less like the swell off Newquay during an Atlantic storm and a little more like a ripple on the Serpentine. The central predictions for the next peak in hospitalisations, according to the three modelling groups that feed into SPI-M, are as follows: Imperial College London, 4,200; Warwick, 4,640; and the London School of Hygiene and Tropical Medicine, 5,700. All three estimates are based on stages three and four of the lockdown easing roadmap going ahead as planned. By contrast, yesterday’s government figures put the number of people currently in hospital suffering from Covid-19 at 1,152.
But why did SPI-M ever come up with such a pessimistic scenario? As I wrote here on 6 April, the Imperial, Warwick and the London School of Hygiene and Tropical Medicine teams were then using remarkably low assumptions regarding the efficacy of the Pfizer and AstraZeneca vaccines — far lower than that suggested by the data from Phase 3 trials and real world data emerging from the vaccine rollout.
This hasn’t changed. The three modelling groups are still using efficacy assumptions that are way out of line with the trial and real world data. Imperial, for example, uses a central assumption that the AstraZeneca vaccine reduces symptomatic illness by 63 per cent after two doses, and a pessimistic assumption that it reduces symptomatic disease by just 50 per cent.