The slides that accompanied the Number 10 press conference last Saturday included a slide showing projections from early October charting scenarios if there were no changes in policy or behaviour and under a number of assumptions: R remains constant, contacts increase over winter, no additional mitigations over and above those in early October when the projections were made.
It’s important to read the small print on these slides, as many clues lie therein. Firstly, it shows preliminary, long term scenarios, and secondly, each independent modelling group doesn’t just forecast a trajectory, but a range of possible outcomes, some worse, some better.
All of these modelling groups showed projections where the daily deaths from Covid exceeded those of the first wave peak number of daily deaths. Taken together, the output from the models show daily deaths peaking during December at a number greater than in the first wave. The peak is useful for knowing the pressures on the NHS, but it’s the area under the graph that is the most sobering – this shows how many people could sadly die.
One group, PHE/Cambridge, had a projection that was much larger than the others, but importantly, SPI-M was not asked to prepare a consensus projection for daily deaths. As projections go further into the future, they become less certain – think of the reliability of forecasting next month’s weather as opposed to forecasting tomorrow’s weather. This is especially true when we are dealing with doubling – things can (and have) got out of hand very quickly, and small changes can have large effects.
None of this is new, of course. The Academy of Medical Sciences produced a report in mid-July, Preparing for a Challenging Winter, which set out a reasonable worst case scenario number of deaths (excluding those in care homes) of around 119,000, over double the number in the first wave.
But crucially, it also included priorities for prevention and mitigation, including expanding the test, trace, and isolate system in order that it can respond quickly and accurately; and ‘maintaining a comprehensive, population-wide, near-real-time, granular health surveillance system’. This of course did not happen, with testing capacity exceeded by demand in late August, leading to deterioration in data quality – data that in turn informs the models.