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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
Verified account
@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. Health Nerd‏Verified account @GidMK 16 May 2020

      There are numerous other errors in the study, but I think I've made my point If I were the author or the journal, I'd retract the study immediately But that's just me

      3 replies 0 retweets 27 likes
      Show this thread
    2. J. Heald‏ @heald_j 17 May 2020
      Replying to @GidMK

      Yes there are a fair number of problems with the paper, but your critique is off. The study is essentially trying to fit R(t) = R0 (1 - p) where p is proportion resistant using cumulative number of confirmed infections x as a surrogate for p, positing p = a x

      1 reply 0 retweets 0 likes
    3. J. Heald‏ @heald_j 17 May 2020
      Replying to @heald_j @GidMK

      In both cases linearity is not an unreasonable assumption (your criticism 1), although one should be cautious that * 'a' may not be stable wrt time, as testing regimes change (to allow different dates to be compared, cumulative hospitalisations might be better)

      1 reply 0 retweets 0 likes
    4. J. Heald‏ @heald_j 17 May 2020
      Replying to @heald_j @GidMK

      * 'p' might reflect proportion of an 'effective' actively mixing population, rather than the whole population * there could be several other confounding factors systematically relating the two variables But it's not an unreasonable relation to investigate.

      2 replies 0 retweets 0 likes
    5. Health Nerd‏Verified account @GidMK 17 May 2020
      Replying to @heald_j

      But that's not true. They do not calculate either R or R0, they calculate a completely different metric from case numbers and called it R which tbh is another serious problem with the paper

      1 reply 0 retweets 0 likes
    6. J. Heald‏ @heald_j 17 May 2020
      Replying to @GidMK

      Look more closely, and you'll find that they have constructed R_ADIR as an estimate of R(t)

      1 reply 0 retweets 0 likes
    7. Health Nerd‏Verified account @GidMK 17 May 2020
      Replying to @heald_j

      That's certainly what they argue, but it is at best an extremely vague estimate and definitely not a realistic calculation

      1 reply 0 retweets 0 likes
    8. J. Heald‏ @heald_j 17 May 2020
      Replying to @GidMK

      See their definition of R_ADIR It's not a very sophisticated estimator; & its associated date should be shifted to account for date of test usually being sometime after the date of end of incubation. But I can't see why it should be grossly off, even if Fig 2 doesn't look righ

      2 replies 0 retweets 0 likes
    9. Health Nerd‏Verified account @GidMK 17 May 2020
      Replying to @heald_j

      At best, it's a remarkably crude estimate that disregards any uncertainty and uses the input of another model as an assumption. And to your earlier point, the prediction is not of proportion resistant - at best, it is proportion INFECTED which is obviously not the same

      1 reply 0 retweets 0 likes
    10. Health Nerd‏Verified account @GidMK 17 May 2020
      Replying to @GidMK @heald_j

      Ideally, you'd want a more sophisticated estimate of R and some attempt at an SIR model, which any infectious disease epi could've done for them

      1 reply 0 retweets 0 likes
      Health Nerd‏Verified account @GidMK 17 May 2020
      Replying to @GidMK @heald_j

      Just come back to this and realized that it's actually wrong - the R(ADIR) in the paper isn't in any way an estimate of R(eff). They've confused infectious period with serial interval, which I suspect has driven some of the bad calculations

      4:20 PM - 17 May 2020
      1 reply 0 retweets 0 likes
        1. New conversation
        2. J. Heald‏ @heald_j 18 May 2020
          Replying to @GidMK

          Agree to some extent - I don't think their derivation is good - but I think the actual calculation may not be that far off. Better might be 7-day rolling avg of new cases divided by previous 7-day rolling avg, all raised to the power of (6.5/7) if 6.5 days is the serial interval.

          2 replies 0 retweets 0 likes
        3. Health Nerd‏Verified account @GidMK 18 May 2020
          Replying to @heald_j

          Let me put it this way - if they've arrived at the right number using an incorrect calculation, then it's pretty unimpressive

          0 replies 0 retweets 0 likes
        4. End of conversation

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