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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 sa

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 ;)

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 ;)