Two charts show that most moves into T3 were really no-brainers:
1) Case rates in these areas are high
And 2) in all-but-one area moved into T3, cases are also rising, in some cases very rapidly.
The exception is Runnymede in Surrey, where cases are elevated but trending downwards.
And here’s all of the above in map form.
Tier 3 maps very neatly onto areas whose case rates are currently elevated.
Rate of change (right) is a less neat fit; some areas in T3 are trending downwards, and most in T2 are trending sharply up.
Finally, a note:
As journalists we claim to be completely impartial, but sometimes we fall short of that standard.
It is with regret that I confess I have shown a bias towards my home town and labelled Doncaster on the top chart despite its minimal significance to the story.
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Genuinely amazed that we aren’t able to get data on the number of people who have been vaccinated.
Such a huge own goal.
Not only is it really important to know this for public health reasons, it would also be a "good news" number to track, and may show the UK in a good light.
I suspect (and certainly hope) we will start getting this data in the near future, but that it wasn’t planned for and published from the outset suggests that once again this stuff is being made up as we go along.
It also sows distrust at a time when this could not be more crucial.
Distrust fuels vaccine hesitancy, and people project their distrust from one domain onto vaccine safety.
Hailing vaccination successes while unable to point to any evidence is a complete shambles.
NEW: the latest UK data is out, and it’s not good.
London is into Tier 3, but that’s only a small part of the wider story of UK’s looming Covid winter.
Case rates rising fast in London, SE & E, but also now rising in Midlands, NW and SW, and decline has halted in NE.
Regrettably, it’s also clear only tough restrictions suppress transmission.
During national lockdown, case rates either went from rising to falling, or from falling slowly to fast, in all English regions.
Since restrictions eased, all declines have either flattened or reversed.
The picture in Wales is even more stark:
During the 17 days of their "fire-break" lockdown, the weekly case rate fell from 295 per 100k to 184. In the first 17 days after restrictions eased, it rebounded to 301.
In the 6 days since then, the rate has increased by 45% to 440.
NEW: it’s expected (but not yet confirmed) London will go into Tier 3 when England’s Covid restrictions are reassessed next week, so what do the data show?
Let’s dig into all 6 metrics the govt is using.
First, new case rates:
• High in much of London, well into old T3 levels
But with cases, *growth rate* is also a key consideration, and this could be the big one for London.
Rates are rising in the vast majority of London boroughs, increasing by 50% per week in many places, and rising faster than almost all current Tier 3 areas.
Sticking to cases, government is also looking at rates specifically among those aged 60 and over.
As with the pattern in the general population, London boroughs send that Tier 2 slope steeply upwards on the left, with case rates among the 60+ well into former Tier 3 territory.
The picture isn’t as clear-cut as one might think, so here’s a short thread on the key details:
At first glance, it seems pretty clear that London should go into T3. Overall, London case rates are currently roughly at the T3 average, with several boroughs showing rates that would be high even in the top tier.
but ...
... the question is not necessarily how case rates compare between the tiers *now*, but how current rates compare to *when tiers were introduced*
On that basis, London as a whole is still at very low end of T3 when it came in (grey circles), and most boroughs exceptionally low
Let’s set aside that ONS series, and instead look at:
• Imperial REACT study
• Covid symptom tracker
• ONS’s model-free weighted estimates (not subject to revisions)
• The dashboard
Each as it stood when lockdown was announced.
All pointed to rapidly increasing infections
tl;dr
The way ONS models incidence can a) give a misleading shape to recent days of the outbreak, and b) cause confusion where estimated prevalence is retrospectively altered.
But there’s no doubt the virus was spreading rapidly in late October when lockdown was announced
Our job is to communicate clearly to people. If they are confused, that's on us much more than on them.
And no, this doesn't mean we should change our definition of things like unemployment and GDP to match what people [mis]understand, but it does mean we should provide explainers if we're using these terms when speaking to a mass audience.
It's the same in #dataviz. If someone doesn't understand my chart, that's mainly on me.
This is why we always included log scale explainers when sharing our trajectory trackers, for example.
We're hear to communicate, not to make pronouncements from on high and then walk away.