Wednesday, August 05, 2020

Could R still be less than 1?

It's been suggested that things might all be fine, maybe the increase in case numbers is just due to more/better testing. There certainly could be a grain of truth in the idea, as the number of tests undertaken has risen a little and the proportion of tests that have been positive has actually kept fairly steady over recent weeks at around 1%. On the other hand, you might reasonably expect the proportion of positives to drop with rising test numbers even if the number of ill people was constant, let alone falling as SAGE claim - consider at the extreme, 65 million tests couldn't find 650,000 positives if only a few tens of thousands are actually ill at any one time. Also, the ONS pilot survey is solid independent evidence for a slight increase in cases, albeit not entirely conclusive. But let's ignore that inconvenient result (as the BBC journalist did), and consider the plausibility of R not having increased in recent weeks. 

This is fairly easy to test with my data assimilation system. I can just stop R from varying at some point in time (by setting the prior variance on the daily step to a negligible size). For the first experiment, I replaced the large jump I had allowed on 4th July, with fixing the value of R from that point on. Note however that the estimation is still using data subsequent to that date, ie it is finding the (probabilistic) best fit for the full time series, under the constraint that R cannot change past 4 July. I've also got a time-varying case ascertainment factor which I'll call C, which can continue to vary throughout the full interval.

Here are the results, which are not quite what I expected. Sure, R doesn't vary past the 4th of July, but in order to fit the data, it shoots up to 1 in the few days preceding that date (red plume on 2nd plot). The fit to the death data in the bottom plot looks pretty decent (the scatter of the data is very large, due to artefacts in the counting methodology) and also the case numbers in the top plot are reasonable. See what has happened to the C factor though (red plume on top graph). After being fairly stable through May and most of June, it takes a brief nose-dive to compensate for R rising at the end of June, and then has to bounce back up in July to explain the rise in case numbers.





While this isn't impossible, it looks a bit contrived, and also note that even so, we still have R=1, firmly outside the SAGE range of 0.8-0.9. Which isn't exactly great news with school opening widely expected to raise this value by 0.2-0.5 (link1link2).

So, how about fixing R to a more optimistic level, somewhere below 1? My code isn't actually set up very well for that specific experiment, so instead of holding R down directly, I just put the date back at which R stops varying. In the simulations below it can't change past the 1st June. It still climbs up just prior to that date, but only to 0.9 this time, right at the edge of the range of SAGE values. The fit to the death data is similar, but tis time the swoop down for C on the upper plot is a bit more pronounced (because R is higher through June) and then it has to really ramp up suddenly in July to match the rise in case numbers. You can see that it starts to underestimate the case numbers towards the present day too, C would have to keep on ramping up even more to match that properly.




So R being in the SAGE range isn't completely impossible, but requires some rather contrived behaviour from the rest of the model which doesn't look reasonable to me. I don't believe it and think that unfortunately there is a much simpler explanation for (some of) the rise in case numbers.

2 comments:

crandles said...

I agree that model behaviour looks contrived.

But just wondering what we should expect if there are increasing numbers of local lockdowns with mobile testing units brought in. Do we find in a fairly short period several cases that have been building up for a while rather than just in that short period? One local lockdown might look like a lump, but several with increasing frequency might mean that a few lumps look like a trend?

What then for your "you might reasonably expect the proportion of positives to drop with rising test numbers"?

FWIW I live in Flintshire. Wrexham and Flintshire represent approx half of all cases in Wales in last couple of weeks and this looks like a lump on Wales graph of cases. Though Wales only opened pubs indoors on Monday.

Probably a mixture of all of:

Increase in R
Increase in testing
Increased targeting of testing in local lockdowns areas
other effects?

Attribution is never easy and it doesn't look easy to attribute increase in cases to these different causes.







crandles said...

7 day average July 8 545
July 23 656
Aug 5 818

Increase of 111 / 20% in 15 days
Then 162 / 25% in 13 days

more than linear and exponential growth.

A increasing R could explain more than exponential growth, but there could also be other explanations and this doesn't really help attribution?