When should the last lockdown end?
We can estimate when it will be safe to end all lockdown restrictions in the UK using the Imperial SIR model (report 9). Life can return to normal once the natural (unmitigated) value of R falls below 1
As of today (22nd February) we know that
- About 13.5 million people have contracted COVID and recovered. They are therefore immune and extremely unlikely to be re-infected
- 17.5 million people have been vaccinated so by now roughly 15 million of these people are already immune.
- The latest ONS infection survey 19th Feb found that 550,000 people were currently infected with COVID
The UK population is 66.6 million so as of today we have the following split in numbers
Susceptible = ~37.5 million Recovered = ~ 28.5 million Infected = 0.55 million
Now taking the original Imperial College model (Report 9) we can see where we currently sit on their SIR curve.
Vaccinating 17.5 million people moves them from susceptible to recovered without infection thereby reducing the unmitigated value of R<1
The good news is that we have basically already beaten the epidemic. Even if we stopped all lockdown restrictions next week the disease would eventually peter out because it already can’t find enough susceptible people to infect in an unmitigated state (shown above). Of course infections would initially rise because of easing restrictions but this would soon stabilise and then fall naturally. The downside is that for a short period hospital admissions would rise before collapsing. We can avoid that initial panic by waiting until the number of vaccinated doubles to ~35 million by mid April then it is probably safe to end all restrictions.
The epidemic would then essentially all be over my mid May and normal life can resume.
The only question is whether Boris will have the guts to do it. Let’s hope so !
Postscript: Boris is taking a very cautious step by step approach with the goal of ending lockdown in July. This looks over cautious but he wants to minimise any risk of extra deaths until all adults have been vaccinated. He apparently got spooked again by the latest models of Fergusson et al. !
Quote: “Due to eligibility and vaccine hesitancy, vaccination alone will not be sufficient to keep the epidemic under control. NPIs must be lifted slowly and cautiously to minimise the number of deaths and prevent high hospital occupancy, with some baseline NPIs remaining in place (and adhered to) throughout 2021 and beyond.”
I wonder what they mean by “under control” ?
The HadCRUT5 data show about a 0.1C increase in annual global temperatures compared to HadCRUT4.6. There are two reasons for this.
- The change in sea surface temperatures moving from HadSST3 to HadSST4
- The interpolation of nearby station data into previously empty grid cells.
Here I look into how large each effect is. Shown below is a comparison of HadCRUt4.6 with HadCRUT5.
Comparison of standard HadCRUT4 and HadCRUT5 showing a ~0.1C increase in recent years,
Next we estimate the effect of both HadSST4 and interpolation. The previous post showed that my 3D triangulation method agrees almost exactly with HadCRUT5, so I now apply it to HadCRUT4.6. Any differences identifies the HadSST4 component.
Comparison of HadCRUT4, Interpolated HadCRUT4 and HadCRUT5
Conclusion: There is an increase of recent global temperatures of about 0.1C when moving from HadCRUT4.6 to HadCRUT5. Half of this increase is due to interpolation and half is due to using HadSST4 instead of HadSST3.
The latest version of the CRU station data is called CRUTEM5 which when combined with the new Met Office sea surface temperature data HadSST4 becomes the new official global temperature dataset HadCRUT5. This was released about 2 weeks ago and is now operational. We have already seen that HadSST4 increases recent temperatures mainly by updating the historic corrections of bucket and engine room intake temperatures. In addition CRU have used a method of infilling sparse 5 degree bins similar to that of Cowtan & Way, which now becomes obsolete. I decided to have a look at the new data.
The main difference for programmers is that all the data are now stored in Net CDF files, which means you have to rewrite all the data handling software. The particular format they use is not documented as far as I can tell. So I used the NCDUMP utility to work out the structure of their NetCDF files. All the station files are now also in netCDF files instead of simple text files.
My method of calculating global temperatures uses a spherical triangulation of measurement points across the surface of the earth. This has the advantage of naturally covering the full surface area. The size of individual triangles just changes with measurement density. So the grid “cells” are 3D triangles rather than 2D Lat,Lon cells. The temperature of each triangle is the average of the 3 vertices. After a couple of days effort I now have it working.
HadCRUT5 temperature distribution for December 2020 determined by spherical triangulation
So how do my results compare with theirs? Here is a direct comparison for the annual temperature anomalies.
Comparison of the official HadCRUT5 annual temperature anomalies and the Spherical triangulation method.
My results are remarkably similar to theirs whereas this was not the case with HadCRUT4. The reason why they are now in good agreement is because HadCRUT5 extrapolates a fit into empty grid cells using a method similar to that of Cowtan & way. As a result HadCRUT5 has now become one of the “warmer” datasets whereas HadCRUT4 was one of the coolest. Despite this CRUTEM5 has not really expanded much in station data. I find 10631 stations but of these only 7734 have data within the normalisation period 1961-1990, which is not much more than in CRUTEM4 (10295 & 7680).
Finally here are the monthly results.
HadCRUT5 Monthly temperature anomalies. calculated using spherical triangulation.
My monthly and annual temperature anomalies can both be viewed as simple text files.
The software written in IDL is also available on request.