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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 17 Mar 2020
      Replying to @sinanaral

      I don't think that's true - it teaches us a lot. What it doesn't give us is perfect information, but I think the reality is that we simply aren't going to have perfect information for some time yet

      1 reply 0 retweets 0 likes
    2. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
      Replying to @GidMK

      What we care most about is prevalence and risk (morbidity/mortality). Testing people as they drive up to a tent indeed teaches us little about prevalence or risk, not to mention asymptomatic shedding and transmission. The errors are so large, we can't plan.

      1 reply 0 retweets 2 likes
    3. Health Nerd‏Verified account @GidMK 17 Mar 2020
      Replying to @sinanaral

      I disagree wholeheartedly. With an infinite supply of testing and a perfectly happy population, perhaps, but in the real world we care most about identifying as many cases as possible in as short a space of time with limited testing facilities

      2 replies 0 retweets 0 likes
    4. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
      Replying to @GidMK

      Wow, yes, we completely disagree. Learning about the prevalence of cases in regional & demographic subpopulations will help us find more true positives. Assuming testing ramps up over time, the tests later are better deployed with knowledge of where to deploy them.

      2 replies 0 retweets 0 likes
    5. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
      Replying to @sinanaral @GidMK

      Testing convenience samples helps those convenience samples who get treatment, but not the millions who could learn from a statistically valid sample. I'm not saying test randomly. I'm saying test smartly so we learn something.

      1 reply 0 retweets 0 likes
    6. Health Nerd‏Verified account @GidMK 17 Mar 2020
      Replying to @sinanaral

      But we learn a lot! As I said, we know enormous amounts about the disease, we just don't have a ~perfect~ sample and the certainty that comes from it. The issue with Ioannidis is that he's essentially arguing that without perfection, we know nothing

      1 reply 0 retweets 0 likes
    7. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
      Replying to @GidMK

      Have you seen the confidence intervals?? Our estimates of mobility and mortality, transmission and prevalence are so wide we know very little. I don't think we need a perfect sample, but a little more precision is warranted.

      1 reply 0 retweets 1 like
    8. Health Nerd‏Verified account @GidMK 17 Mar 2020
      Replying to @sinanaral

      CIs are mostly a function of sample size, not sampling accuracy. I do agree we don't know a lot, but I would strongly disagree that we know very little. We have a very good idea of the R0, confident estimates of hospitalization rate, and pretty decent predictions of morbidity

      1 reply 0 retweets 0 likes
    9. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
      Replying to @GidMK

      I pretty much disagree with everything in this tweet.

      3 replies 0 retweets 6 likes
    10. Dean Eckles‏ @deaneckles 17 Mar 2020
      Replying to @sinanaral @GidMK

      Computing standard confidence intervals from big, biased data does not yield valid inference for quantities of interest. CIs that shrink with n no matter the sampling method lead to folly. @XiaoLiMeng1 is great on this: https://statistics.fas.harvard.edu/files/statistics-2/files/statistical_paradises_and_paradoxes.pdf …pic.twitter.com/gVQ2UmhLM2

      3 replies 10 retweets 41 likes
      Health Nerd‏Verified account @GidMK 17 Mar 2020
      Replying to @deaneckles @sinanaral @XiaoLiMeng1

      I mean, yes that's precisely my point. Narrow CIs wouldn't give you any confidence in an estimate!

      6:23 PM - 17 Mar 2020
      1 reply 0 retweets 0 likes
        1. New conversation
        2. Jason Kerwin‏ @jt_kerwin 17 Mar 2020
          Replying to @GidMK @deaneckles and

          I think you’re missing the point of that paper. Even tons of data from a convenience sample might tell is extremely little. In contrast, we can quickly achieve tight & accurate CIs by testing random samples of people.

          1 reply 1 retweet 5 likes
        3. Sinan Aral‏Verified account @sinanaral 17 Mar 2020
          Replying to @jt_kerwin @GidMK and

          Right.

          0 replies 0 retweets 0 likes
        4. End of conversation

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