Lots of talk from politicians and others that, while not exactly triumphalist, is certainly very positive and enthusiastic about the prospect of vaccinating ourselves out of trouble. Here’s one of the earliest “light at the end of the tunnel” articles for example. And with the new Oxford/AZ vaccine there is renewed excitement.

Sorry to pour a little bit of cold water on the mood but a bit of perspective is called for.

Here is what my modelling suggests for the progress of the outbreak though the population so far and into the future. It’s not a pretty sight. The total number that may be infected between now and the start of March (less than 2 months away) is more than the entire number that have been infected so far right from the start of the outbreak last Feb/March.

According to these calculations, roughly 15 million have been infected, and a total of roughly 36 million may be by the time it’s over. That is, we have significantly more infections to come, than we have seen so far. And far more than we got in the first wave last spring, when probably something like 10% (my modelling actually says 8%) of the population was infected.

*If there ever was a time to stay at home and minimise all unnecessary contact, it most surely is now.*

With this rate of spread, vaccinating a few million over the next couple of months has a relatively minor effect. It may reduce the death rate significantly towards the end of this period (and will certainly help the small minority of highly vulnerable people who receive it), but won’t stop the disease spreading widely.

I need to add a few disclaimers about the modelling. This result plotted above is the median of my latest ensemble fit of a simple SEIR model to historical data on deaths and cases. I’ve been modelling the progress of the outbreak for months now and though the model is rather primitive and approximate it has done a pretty decent job of simulating what is actually quite a simple process. If each infected person passes the disease on to more than one other (on average), then the disease grows exponentially, if they pass it on to less than one (on average) then it shrinks exponentially. The more difficult bits (that my model is too simple to attempt) is to predict the effect of specific restrictions such as closing schools or pubs, or determining how many young vs old people get ill. When just looking at total numbers, this simple SEIR model (when carefully used) works better than it probably should.

This simulation, while it fits the historical data well, may not account adequately for the added virulence of the newer strain that has recently emerged. It also assumes that we don’t have an extremely strict lockdown that successfully suppresses the outbreak in the very near future. Reality could end up better than this, or it could end up worse, but I’m pretty confident that the basic message is robust. People are getting infected at a huge rate right now. Stay at home if you can.

Sobering stuff. What (enhanced) rate of vaccine roll-out would you need to make a significant difference?

It’s quite aggressive as it is. My assumption is the death rate halving over 3 months, which seems optimistic, but you still don’t get a huge impact in the first two months. They basically need to be immunised yesterday.

Are you factoring the efficacy of the vaccine in directly, or just applying a guess at reduction in IFR? The figures for efficacy from AstraZeneca so far seem to be all over the place, depending on which sub-group you use, whether there’s an R in the month, and so on.

I’m just applying a reduction of the overall IFR, guesstimating some ballpark numbers based on various analyses I’ve seen.

The IFR starts out at 0.75% (this is the value I’ve been using from the start) but just recently I introduced a gradual trend down to 0.6% though May-Dec last year (representing better treatment, eg dexamethasone), and then a further 50% reduction to 0.3% through Jan-March this year. These values are a little bit lower than most expert groups use in the UK. OTOH the model is probably a little pessimistic in other ways, eg lack of inhomogeneity.

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