I have been running the Imperial College Covid-19 model for different scenarios. The published parameter files (on GITHUB) use an R0 value of 3.0 with an IFR of ~1%, higher than that published in February (2.4). This predicts a total number of 620,000 deaths in the UK if the disease were allowed to run its course through to “herd immunity” without any mitigation. These are the figures that SAGE must have been considering in early March, when the UK policy then was to only test arrivals from China and flatten infections towards an eventual herd immunity. On the 13th March however the advice was suddenly changed:
The new advice issued by the Chief Medical Officer is as follows:
Stay at home for 7 days if you have either:
- a high temperature
- a new continuous cough
Do not go to a GP surgery, pharmacy or hospital.
Something dramatic changed very quickly and by March 23rd a full lockdown was imposed – Places (schools, shops, restaurants, pubs, businesses ) were closed, travel restricted and social distancing measures applied. The new slogan was “stay at home, protect the NHS, save lives”.
I have used Neil Ferguson’s (ICL) model to investigate exactly why this sudden policy change occurred. It seems that SAGE had by then concluded that the current R value was ~3.0 and infections exploding mainly in London. ICL’s model was now predicting 620,000 deaths without government intervention. This was way too much to “take it on the chin” by “slowing the curve”. But what would have happened if the decision for lockdown had instead been taken a week earlier or a week later ? This is what the ICL model says.
Figure 1. How the timing of lockdown measures affected final UK death rates. Note also the strange effect that the models and data all coincide on day 100 with 10,000 deaths (10th April). This appears to be because Ferguson forces the model to agree with the actual data on this day.
It would have been far worse to delay the decision a week than to advance it a week! Hindsight is a wonderful luxury for all armchair critics. Fergusson’s model predicts that if the lockdown had been imposed a week earlier, then it might have saved up to 15000 lives in the short term. However if it had instead been applied a week afterwards it may instead have cost an extra 40,000 deaths !
The IC model is driven by various parameter files which are very obtuse and difficult to fathom out without any proper documentation. So instead I simply delayed all intervention start dates by ~ 1 week to get these results. However this results in the day 100 effect as described above. ATTP has an ad hoc fix for this but I am not sure if this doesn’t perhaps also bias the result.