Note: This thread is related to #Coronavirus #COVID19

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John Burn-Murdoch
+ Your AuthorsArchive @jburnmurdoch Stories, stats & scatterplots for @FinancialTimes | Daily updates of the coronavirus trajectory tracker | [email protected] | #dataviz Sep. 28, 2020 1 min read

I’ve noticed a lot of people slipping up on how they interpret UK Covid-19 prevalence & testing data, so here’s a very brief thread on how to interpret figures from different sources, and what caveats each source does and does not come with:

• Pillar 2 community testing: these are the bulk of cases picked up at the moment. Case and positivity rates here *could* be influenced by where and who is being tested, so e.g patterns in this data with age, deprivation etc could be skewed by who is getting tested

@ONS infection survey: these tests are random, and designed to be representative of the overall population.

Therefore trends and patterns in this data *are not* due to e.g certain locations or groups of people being more likely to get tested.

Samples taken for @ONS tests are re-tested multiple times to make false positives extremely unlikely (one in tens of thousands), so any false positive chatter is completely absurd for the ONS survey (and also hugely exaggerated re the Pillar 2 tests)

So when we see things like today’s fascinating @ONS breakdowns of Covid-19 prevalence & growth by deprivation, age, levels of socialising etc, these trends *are not* being driven by differential rates of testing.

They’re being driven by actual behaviour


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