4 ms·

It's worth bearing in mind that initially (and until only a few months ago iirc) tests were only available to certified essential workers - first NHS staff, the

It's worth bearing in mind that initially (and until only a few months ago iirc) tests were only available to certified essential workers - first NHS staff, then civil servants, etc. I actually paid £100 for a private test in May. I've had an NHS drive-thru one more recently. I'm not sure of the absolute veracity of your 'regardless of symptoms' claim. As I understand it, the R number is calculated based on all the NHS tests done (and private, with some lag iirc). This even explicitly says "You cannot get a free NHS test unless you have symptoms, have been asked to by your local council, live in England and have been told to by your hospital, or are taking part in a government pilot project." https://www.gov.uk/get-coronavirus-test https://www.gov.uk/get-coronavirus-test

thomc 6y ago
I believe they also run a programme of random tests regardless of symptoms. A friend of mine was selected for this and they periodically come to his house and take a sample.
- DerDangDerDang 6y ago
  Ah ok. Any idea how extensive the programme is and what weight it's given in relation to overall testing? Obviously if this is the only / main input to the R number then my previous comment is crap and I retract it. Otherwise, I think I gave reasonable examples of how the sampling has been skewed, in good faith.
tigershark 6y ago
Absolutely false. I got a test in May as part of a statistical study in UK. I was randomly selected as you would expect in this kind of studies.
- secondcoming 6y ago
  What part is absolutely false? The NHS randomly picked you, you didn't ask the NHS
  - jlokier 6y ago
    The false part is "tests were only available to certified essential workers". That's true for people seeking tests, and workplaces etc. But the statistical sampling was conducted separately by the Office of National Statistics (not the NHS) across the whole country in order to understand the pandemic, and it did have access to tests for this purpose.
    - DerDangDerDang 6y ago
      While you’re right in absolute terms, I think you’re obscuring a valid point. The ONS having tests doesn’t change the fact that for the vast majority of the population, being an essential worker was the only way to get a test. The ONS tests are not the only input to the announced R number are they? Maybe I’m misunderstanding horribly, but it seems like you’re saying there’s absolutely no correlation whatsoever between availability of tests to the general public and the accuracy of the announced R number for the general public. If I’m misunderstanding I do genuinely want to understand!
      - jlokier 6y ago
        > it seems like you’re saying there’s absolutely no correlation whatsoever between availability of tests to the general public and the accuracy of the announced R number for the general public. Depends what you mean by accuracy, whether that's bias or uncertainty. If you're talking about bias, then I agree with the above statement. My estimate of the mean bias in published R estimates is zero. (Possibly on a logarithmic scale :-) But if you're talking about uncertainty and not bias, then in general more data is better provided its biases are known, but it's hard to say that focusing tests on a subset of the population reduces certainty. In a mathematical sense, to minimise uncertainty from sampling estimates if you have a fixed number of samples but a choice about which situations to assign them to, you want to focus more testing on the situations which provide the highest information content. That is not necessarily the same as spreading them evenly through the population in an unbiased manner. I think what you may be misunderstanding, and therefore misrepresenting, is the idea that a combination of statistically sampled tests (ONS) plus biased targeted tests (NHS, key workers, Test & Trace etc) results in "skewed" or more misleading R estimates than just the statistically sampled tests (ONS) by themselves. I think that's unlikely. I'm assuming the data is combined by competent professional statisticians. Assuming they are competent, the likelihood of any biases due to NHS sampling that you or I might think of not having already been evaluated by the statisticians is negligible. So, provided the bias can be estimated, combining data from multiple sources tends to reduce uncertainty rather than introducing bias in a particular direction. That's why I say my personal "estimate of mean bias" is zero. Published R may by higher or lower than true R, and we can take it for granted it will be off by some amount and constantly (and retroactively) revised with new data, which is fair enough for estimations, but I have no basis on which to assume corrected results are more likely to have bias in one direction or the other. A nice feature of having two or more kinds of sampling is that intentionally-randomly-sourced data (ONS) acts as an "anchor" on the interpretation of non-ONS data, allowing raw biases from various sources to be estimated and adjusted for, uncertainties to be estimated too, while at the same time trends (such as over time) remain trackable with the higher statistical power that comes from larger numbers of samples. In other words, a bit of the best of both worlds. And since r and R are trend parameters, that's quite helpful.
chrisseaton 6y ago
> It's worth bearing in mind that initially (and until only a few months ago iirc) tests were only available to certified essential workers - first NHS staff, then civil servants, etc. You’re not saying true things. Why are you spreading this disinformation despite everyone correcting you? They’ve been randomly sampling. R values are randomly sampled.
- DerDangDerDang 6y ago
  Ok, I really am arguing in good faith here. Not intending to spread disinformation. Would you mind spending the time to conclusively correct me? So. Are you saying that the R number given by the government is derived from data only from random sampling, and that other test statistics do not feed into the estimate at all? I’m not a statistician or epidemiologist and I find that very hard to believe. But I will honestly be happy to have learned something if you can teach me otherwise. My understanding was that longitudinal studies using random sampling were used to check and correct the numbers derived from the actual test numbers for the real population. That meant the longitudinal studies would have been even more important early on when access to testing was poor, because the sample distribution was obviously skewed wrt the whole population. Again, I really would appreciate a substantial rebuttal, link or otherwise. Thanks
  - chrisseaton 6y ago
    This paper describes how they've been sampling. https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf https://royalsociety.org/-/media/policy/projects/set-c/set-c... It specifically says that they have been conducting ongoing random sampling of volunteers, outside of people being tested because they're possibly presenting symptoms. > as of latest release of survey data on the 25 June 2020, 27,494 individuals out of 17139 households enrolled have agreed to continue to be tested
    - DerDangDerDang 6y ago
      Thanks, I appreciate it. It does say they've been doing random sampling. They also say that randomly sampled longitudinal surveys are the best predictors. They also list (p32) all the other data sources that are used to estimate R in different studies - including case numbers from tests taken by symptomatic members of the general public. Section 7.1 Case Numbers notes that Pillar 1 tests aren't a good predictor, Pillar 2 and 3 can be useful but (heavily paraphrasing here if you'll excuse me) the results need to be adjusted and hedged. This page says they take many studies as input to the official R, including studies that look at test figures in the general population - https://www.gov.uk/guidance/the-r-number-in-the-uk https://www.gov.uk/guidance/the-r-number-in-the-uk Section 8 of the rs paper specifically says that longitudinal studies should be used on a much wider scale than they are being, because of asymptomatic transmission. So in August, there was not enough random longitudinal testing, and an over-reliance on studies based on case numbers! My controversial comment said that early on, access to testing would have skewed things, making R harder to confidently estimate (better wording this time?). I.E. Pillar 2 and 3 tests were not available to the non-essential members of the general public early on. Is it fair to say that makomk's comment "one of the main things used to estimate the value of R is randomly sampling ... regardless of symptoms" is broadly true (now) - but that my comments about poor early access to testing skewing early R estimates is also broadly true? They're both supported by the paper you linked, imo. Edit: To be clear, I was wrong about there being no access to testing without symptoms, as at least some random longitudinal testing was happening early on - just not enough, according to the paper you linked. Thanks for reading ;)

thomc 6y ago

I believe they also run a programme of random tests regardless of symptoms. A friend of mine was selected for this and they periodically come to his house and take a sample.

DerDangDerDang 6y ago
Ah ok. Any idea how extensive the programme is and what weight it's given in relation to overall testing? Obviously if this is the only / main input to the R number then my previous comment is crap and I retract it. Otherwise, I think I gave reasonable examples of how the sampling has been skewed, in good faith.

tigershark 6y ago

Absolutely false. I got a test in May as part of a statistical study in UK. I was randomly selected as you would expect in this kind of studies.

secondcoming 6y ago
What part is absolutely false? The NHS randomly picked you, you didn't ask the NHS
- jlokier 6y ago
  The false part is "tests were only available to certified essential workers". That's true for people seeking tests, and workplaces etc. But the statistical sampling was conducted separately by the Office of National Statistics (not the NHS) across the whole country in order to understand the pandemic, and it did have access to tests for this purpose.
  - DerDangDerDang 6y ago
    While you’re right in absolute terms, I think you’re obscuring a valid point. The ONS having tests doesn’t change the fact that for the vast majority of the population, being an essential worker was the only way to get a test. The ONS tests are not the only input to the announced R number are they? Maybe I’m misunderstanding horribly, but it seems like you’re saying there’s absolutely no correlation whatsoever between availability of tests to the general public and the accuracy of the announced R number for the general public. If I’m misunderstanding I do genuinely want to understand!
    - jlokier 6y ago
      > it seems like you’re saying there’s absolutely no correlation whatsoever between availability of tests to the general public and the accuracy of the announced R number for the general public. Depends what you mean by accuracy, whether that's bias or uncertainty. If you're talking about bias, then I agree with the above statement. My estimate of the mean bias in published R estimates is zero. (Possibly on a logarithmic scale :-) But if you're talking about uncertainty and not bias, then in general more data is better provided its biases are known, but it's hard to say that focusing tests on a subset of the population reduces certainty. In a mathematical sense, to minimise uncertainty from sampling estimates if you have a fixed number of samples but a choice about which situations to assign them to, you want to focus more testing on the situations which provide the highest information content. That is not necessarily the same as spreading them evenly through the population in an unbiased manner. I think what you may be misunderstanding, and therefore misrepresenting, is the idea that a combination of statistically sampled tests (ONS) plus biased targeted tests (NHS, key workers, Test & Trace etc) results in "skewed" or more misleading R estimates than just the statistically sampled tests (ONS) by themselves. I think that's unlikely. I'm assuming the data is combined by competent professional statisticians. Assuming they are competent, the likelihood of any biases due to NHS sampling that you or I might think of not having already been evaluated by the statisticians is negligible. So, provided the bias can be estimated, combining data from multiple sources tends to reduce uncertainty rather than introducing bias in a particular direction. That's why I say my personal "estimate of mean bias" is zero. Published R may by higher or lower than true R, and we can take it for granted it will be off by some amount and constantly (and retroactively) revised with new data, which is fair enough for estimations, but I have no basis on which to assume corrected results are more likely to have bias in one direction or the other. A nice feature of having two or more kinds of sampling is that intentionally-randomly-sourced data (ONS) acts as an "anchor" on the interpretation of non-ONS data, allowing raw biases from various sources to be estimated and adjusted for, uncertainties to be estimated too, while at the same time trends (such as over time) remain trackable with the higher statistical power that comes from larger numbers of samples. In other words, a bit of the best of both worlds. And since r and R are trend parameters, that's quite helpful.

chrisseaton 6y ago

> It's worth bearing in mind that initially (and until only a few months ago iirc) tests were only available to certified essential workers - first NHS staff, then civil servants, etc. You’re not saying true things. Why are you spreading this disinformation despite everyone correcting you? They’ve been randomly sampling. R values are randomly sampled.

DerDangDerDang 6y ago
Ok, I really am arguing in good faith here. Not intending to spread disinformation. Would you mind spending the time to conclusively correct me? So. Are you saying that the R number given by the government is derived from data only from random sampling, and that other test statistics do not feed into the estimate at all? I’m not a statistician or epidemiologist and I find that very hard to believe. But I will honestly be happy to have learned something if you can teach me otherwise. My understanding was that longitudinal studies using random sampling were used to check and correct the numbers derived from the actual test numbers for the real population. That meant the longitudinal studies would have been even more important early on when access to testing was poor, because the sample distribution was obviously skewed wrt the whole population. Again, I really would appreciate a substantial rebuttal, link or otherwise. Thanks
- chrisseaton 6y ago
  This paper describes how they've been sampling. https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf https://royalsociety.org/-/media/policy/projects/set-c/set-c... It specifically says that they have been conducting ongoing random sampling of volunteers, outside of people being tested because they're possibly presenting symptoms. > as of latest release of survey data on the 25 June 2020, 27,494 individuals out of 17139 households enrolled have agreed to continue to be tested
  - DerDangDerDang 6y ago
    Thanks, I appreciate it. It does say they've been doing random sampling. They also say that randomly sampled longitudinal surveys are the best predictors. They also list (p32) all the other data sources that are used to estimate R in different studies - including case numbers from tests taken by symptomatic members of the general public. Section 7.1 Case Numbers notes that Pillar 1 tests aren't a good predictor, Pillar 2 and 3 can be useful but (heavily paraphrasing here if you'll excuse me) the results need to be adjusted and hedged. This page says they take many studies as input to the official R, including studies that look at test figures in the general population - https://www.gov.uk/guidance/the-r-number-in-the-uk https://www.gov.uk/guidance/the-r-number-in-the-uk Section 8 of the rs paper specifically says that longitudinal studies should be used on a much wider scale than they are being, because of asymptomatic transmission. So in August, there was not enough random longitudinal testing, and an over-reliance on studies based on case numbers! My controversial comment said that early on, access to testing would have skewed things, making R harder to confidently estimate (better wording this time?). I.E. Pillar 2 and 3 tests were not available to the non-essential members of the general public early on. Is it fair to say that makomk's comment "one of the main things used to estimate the value of R is randomly sampling ... regardless of symptoms" is broadly true (now) - but that my comments about poor early access to testing skewing early R estimates is also broadly true? They're both supported by the paper you linked, imo. Edit: To be clear, I was wrong about there being no access to testing without symptoms, as at least some random longitudinal testing was happening early on - just not enough, according to the paper you linked. Thanks for reading ;)