#345470
Postby neversay » October 5th, 2020, 8:50 pm
@johnhemming has answered more succinctly, but here was my verbose and poorly worded work in progress reply:
There are many times more actual cases (include asymptomatic, mild, or untested) than tested positive cases which are leading indicators of hospitalisations and then deaths. The current demographic for the positive cases are in younger and healthier people, but as the number of actual cases grows it will give more vectors for the infection of those who are clinically vulnerable (hence more likely to die). The issue is these leading and lagging indicators, along with interventions and their consequences, are all heavily time-shifted.
Healthcare capacity is the other side of the equation: "In 2011/12 there were around 5,400 critical care beds, by 2019/20 this had risen to 5,900 (NHS England 2019b) (Figure 5). Of these, around 70 per cent are for use by adults and the remainder for children and infants." [https://www.kingsfund.org.uk/publications/nhs-hospital-bed-numbers]. Of course we now have other wards/hospitals cleared and the Nightingale facilities, but that's a finite amount of beds. However, the issue is that the number of critical care staff is far less scalable and fungible.
This time around any peak of infections, of the same un-mutated virus, would also coincide with conventional winter loads*. So the actual capacity for cases is very limited.
So it's all still about flattening the curve. The Government is damned if they do, damned if they don't. If they let the virus rip then we'll see the death toll rise rapidly just as before, with the hindsight hysteria of the press, public and political opponents deeming it 'unacceptable'. If they are successful in their measures to reduce the vectors for infection then people will complain that the low death toll means it was a waste of time because of the economic impact, social impact and other life-years lost in collateral damage (cancer, suicides, etc).
The assessments on the cost-benefits (life-years saved v economic/societal cost) of different interventions gives some apparently nonsensical trade-offs. If closing the pubs at 10pm can shave 0.5% off total infections, then that might be the 0.5% of curve flattening sufficient to keep the schools open for longer. The public won't understand the 'arbitrary' time of 10pm but the portfolio of measures have to balance the risks versus the benefits. Likewise many of the interventions will not be understood as they have behavioural underpinning. Tell people to allow 2m and they will still pass at 1m. If they'd told people 1m then the public would pretty much forget distancing as they were so close anyway.
Many will argue about the 'science' behind the epidemiological model and each of the underlying parameter sensitivies (e.g. 2m v 1m) but, speaking as someone who builds models for a living, I believe they will be getting the balance about right. The epidemiological model of infections is relatively straightforward, but the parameter sensitivies are a probabilistic curve rather than a fix value. So the assessments are based on an ensemble of probabilities that don't require an individual parameter to be exact. (Plus a margin of risk/error is added as part of the precautionary principle). So it doesn't all fall down on Prof Ferguson's magical model.
(*- we should expect that other seasonal infections like flu will be lighter due to the same control measures, but other illnesses will not, e.g. breathing difficulties, heart attacks, etc.)
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