4 ms·

I met the guy (Nick Hengartner) who did the 6 (really 5.7) estimate and knew his colleagues and I can tell you the paper was a lot more careful than that.

mike_hearn 3y ago
Want to explain how they computed it to us? Because if R0 is a real thing then either the people estimating R0=1.something were wrong, or Nick was wrong, and so somewhere some methodologies need to change. I read a fair few modelling papers and R0 (under varying names) was always being used by the model as a free variable used to achieve fit to a historical dataset. It was never being established via lab work, as you might expect given its definition.
- selimthegrim 3y ago
  https://arxiv.org/abs/2002.03268 https://arxiv.org/abs/2002.03268
  - mike_hearn 3y ago
    So they did exactly what we're saying they do? The paper sets up some ad-hoc models unique to this academic group and then reversed them to find values of R0 that could fit. The problem is underdetermined so there are a huge array of values that could work. Key line: > Overall, we report R0 values are likely be between 4.7 and 6.6 with a CI between 2.8 to 11.3 That's an extremely wide CI by any measure. It's a bit unclear how this is meant to be a contradiction. It looks like a good example of the issue. There is no universal theory or method for computing R0. Every single epidemiologist has their own unique approach which is then often discarded in time for the next paper, making the numbers incomparable.
    - tripletao 3y ago
      There's some minor variation due to different curve-fitting approaches, but the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real, simply because the environment or human behavior varied. As others have repeatedly noted, R0 is a function not just of the pathogen, but also of its environment, including the behavior of its human hosts. Earlier, you wrote: > It [R0] was never being established via lab work, as you might expect given its definition. If you expected that "lab work" could establish R0, then you've grossly misunderstood its meaning and definition. It seems like you're looking for a concept of "R0 but for the pathogen alone, independent of environment and human behavior". That just doesn't exist though, any more than you could define the growth rate of a plant independent of weather and soil fertility.
      - mike_hearn 3y ago
        Briefly, as the substance is discussed in my other reply: > the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real Morpheus: "What is real? How do you define, real?" CIs that wide are just an obfuscated way of saying "we have no idea what's going on or what will happen". Anyone can make predictions that way. For example by the end my life my bank balance will be $4.7 million (CI $5.00-$50M). Those numbers aren't "real" in any meaningful sense. Anyone can express don't-know in sophisticated looking numerical form, and they wouldn't justify me claiming to be a financial expert on the back of them. > any more than you could define the growth rate of a plant independent of weather and soil fertility. You can define growth rate of a plant this way: define a standardized lab environment with regularized soil composition and artificial light/watering schedules. Then plant seeds and measure the dry weight at the end of a fixed time period. This will give you a number that's comparable across species. There are other definitions you could use because "growth rate" is slightly ambiguous in English (does a tree grow faster than a weed because the tree achieves bigger mass?), but that's the general idea. What you shouldn't do is just grab datasets of wildly varying quality off the internet, fit an equation you just invented on the spot to that data, and announce you've discovered something real about the plants in the data. That would indeed yield a growth rate that doesn't tell you anything meaningful.
        tripletao 3y ago
        > You can define growth rate of a plant this way: define a standardized lab environment [...] If the standardized lab environment is dry and sunny, then you'll conclude that a cactus grows faster than a fern, since the ferns will mostly shrivel up and die. If the standardized lab environment is moist and shaded, then you'll conclude that the fern grows faster, since cactuses will mostly die for lack of sun. So which is right? The concept that you're looking for simply doesn't exist--the growth rate of an organism can't be defined except with reference to its environment, which for a virus that infects humans includes human behavior. (What rate of condom use should the standardized lab environment for HIV correspond to? How will you model the increased popularity of fentanyl?) You are looking for CS-level rigor and simplicity in biology, but biology doesn't work like that. You are correct that many biological results were thus oversold to the public during the pandemic; but you're once again criticizing those public-facing oversimplifications, not any science as a practitioner would understand it.

mike_hearn 3y ago

Want to explain how they computed it to us? Because if R0 is a real thing then either the people estimating R0=1.something were wrong, or Nick was wrong, and so somewhere some methodologies need to change. I read a fair few modelling papers and R0 (under varying names) was always being used by the model as a free variable used to achieve fit to a historical dataset. It was never being established via lab work, as you might expect given its definition.

selimthegrim 3y ago
https://arxiv.org/abs/2002.03268 https://arxiv.org/abs/2002.03268
- mike_hearn 3y ago
  So they did exactly what we're saying they do? The paper sets up some ad-hoc models unique to this academic group and then reversed them to find values of R0 that could fit. The problem is underdetermined so there are a huge array of values that could work. Key line: > Overall, we report R0 values are likely be between 4.7 and 6.6 with a CI between 2.8 to 11.3 That's an extremely wide CI by any measure. It's a bit unclear how this is meant to be a contradiction. It looks like a good example of the issue. There is no universal theory or method for computing R0. Every single epidemiologist has their own unique approach which is then often discarded in time for the next paper, making the numbers incomparable.
  - tripletao 3y ago
    There's some minor variation due to different curve-fitting approaches, but the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real, simply because the environment or human behavior varied. As others have repeatedly noted, R0 is a function not just of the pathogen, but also of its environment, including the behavior of its human hosts. Earlier, you wrote: > It [R0] was never being established via lab work, as you might expect given its definition. If you expected that "lab work" could establish R0, then you've grossly misunderstood its meaning and definition. It seems like you're looking for a concept of "R0 but for the pathogen alone, independent of environment and human behavior". That just doesn't exist though, any more than you could define the growth rate of a plant independent of weather and soil fertility.
    - mike_hearn 3y ago
      Briefly, as the substance is discussed in my other reply: > the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real Morpheus: "What is real? How do you define, real?" CIs that wide are just an obfuscated way of saying "we have no idea what's going on or what will happen". Anyone can make predictions that way. For example by the end my life my bank balance will be $4.7 million (CI $5.00-$50M). Those numbers aren't "real" in any meaningful sense. Anyone can express don't-know in sophisticated looking numerical form, and they wouldn't justify me claiming to be a financial expert on the back of them. > any more than you could define the growth rate of a plant independent of weather and soil fertility. You can define growth rate of a plant this way: define a standardized lab environment with regularized soil composition and artificial light/watering schedules. Then plant seeds and measure the dry weight at the end of a fixed time period. This will give you a number that's comparable across species. There are other definitions you could use because "growth rate" is slightly ambiguous in English (does a tree grow faster than a weed because the tree achieves bigger mass?), but that's the general idea. What you shouldn't do is just grab datasets of wildly varying quality off the internet, fit an equation you just invented on the spot to that data, and announce you've discovered something real about the plants in the data. That would indeed yield a growth rate that doesn't tell you anything meaningful.
      - tripletao 3y ago
        > You can define growth rate of a plant this way: define a standardized lab environment [...] If the standardized lab environment is dry and sunny, then you'll conclude that a cactus grows faster than a fern, since the ferns will mostly shrivel up and die. If the standardized lab environment is moist and shaded, then you'll conclude that the fern grows faster, since cactuses will mostly die for lack of sun. So which is right? The concept that you're looking for simply doesn't exist--the growth rate of an organism can't be defined except with reference to its environment, which for a virus that infects humans includes human behavior. (What rate of condom use should the standardized lab environment for HIV correspond to? How will you model the increased popularity of fentanyl?) You are looking for CS-level rigor and simplicity in biology, but biology doesn't work like that. You are correct that many biological results were thus oversold to the public during the pandemic; but you're once again criticizing those public-facing oversimplifications, not any science as a practitioner would understand it.