5 ms·

> it is recommended not to ... compare values based on different models i.e. R0 numbers are worthless. You can't compare these values between pathogens, you ca

> it is recommended not to ... compare values based on different models i.e. R0 numbers are worthless. You can't compare these values between pathogens, you can't even compare them between different academic groups studying the same pathogen or between outbreaks of the same pathogen as modeled by different versions of the same software. There's no agreed way to measure this value empirically. Instead they write a program to simulate the spread of a virus through population and then (in effect) brute force one or more of the inputs until the results of the simulation match what happened so far. These variables are often given names that sorta reflect an intuition about what they're supposed to do, but they're just names. The actual values are arbitrary and there's no effort to ensure the computed values connect to the names in any plausible way. A funny example of this was a COVID model from University College London that used household size as a free variable. The model used this value to achieve curve fit for each country in the study, so they reported that the average Brit lives with seven other people (the paper has 73 citations). https://miro.medium.com/v2/resize:fit:1400/format:webp/1*sEvI1SgBvOydVhtBGN-NAA.png https://miro.medium.com/v2/resize:fit:1400/format:webp/1*sEv... More seriously, estimates of R0 for SARS-CoV-2 have ranged from 1.5 to nearly 6, maybe even higher. It's not even clear what the definition is because it's not time bounded in any way. So not a value that's really knowable, like the speed of light is.

selimthegrim 3y ago
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.
jasmer 3y ago
[dead]
aredox 3y ago
It's not useless. Do you think all contagious illnesses spread at the same rate? InB4 "clouds don't exist, nobody can tell exactly where they start and end"
- mike_hearn 3y ago
  It would be useful to be able to measure some sort of inherent infectiousness, but R0 as actually defined and computed today is useless. It looks scientific on the surface but everyone who calculates it gets a totally different result, and because there's no actual underlying theory involved (just playing with stats), there's no way to decide which estimate is correct. To get meaningfully constant values for infectiousness you'd probably need a microbiological / genetic theory that let you derive it from RNA sequences.
  - tripletao 3y ago
    > To get meaningfully constant values for infectiousness you'd probably need a theory that let you derive it from RNA sequences. As others have already noted in this thread, the rate at which a disease spreads inherently depends on both the biology of the pathogen and human behavior. For example, if everyone goes to crowded nightclubs, then R0 for an airborne respiratory disease goes up. If everyone stays home, then it goes down. The biology of the pathogen and human behavior interact in complex, multidimensional ways; promoting condoms will reduce R0 for HIV, but not for influenza. The disease spreads only through human behavior, so the concept of infectiousness independent of that simply doesn't exist. You are correct that R0 is determined by curve-fitting to actual case counts. It couldn't be otherwise though, since that's the only way to capture the actual human behavior. We expect different R0 in different situations, so the range you see for SARS-CoV-2 isn't a surprise. This is textbook stuff: > Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R0. Because R0 is a function of the effective contact rate, the value of R0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R0 values (range 5.4–18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R0 values of 3.7–203.3 (23). This wide range highlights the potential variability in the value of R0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances. https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article As with your confused understanding of PCR specificity, you may have been misinformed by indefensibly oversimplified public messaging as to the definition of R0. (I had a comment deleted from /r/coronavirus in 2020, because they considered my statement that R0 varies with environment and human behavior to be misinformation; the citation didn't help.) The literature is once again available for anyone who wishes to read it, though.
PurelyApplied 3y ago
> > it is recommended not to ... compare values based on different models > i.e. R0 numbers are worthless. You can't compare these values between pathogens That conclusion doesn't follow from the predicate. The model isn't a single simulation run. To mathematicians, the model is the entire framework. It's the SIR model, or the SIS model, or the SIER model, or my favorite model of immunity decay against pertussis... _those_ are the models. The virulence of the infection being modeled, resilience of the population, morbidity and mortality rates, rate and timing of population quarantine, vaccine adoption rate, vaccine efficacy, whether any of the aforementioned values are themselves functions of time... those are just parameters into your model. We absolutely compare the values between pathogens _within the same model_ when discussing the utility of the model. But typically, the utility in reproduction ratio is as a point of comparison as you tune other parameters. This is part of why a comparison of reproduction ratio across models isn't recommended. It's not just that it's hard to draw meaning from the comparison, it's that the base assumptions of the models might be too different for the comparison to hold any meaning. The reproductive ratio between a continuous model with uniform population mixing is fundamentally different from the reproductive ratio approximated from discrete simulation. They may both broadly speak to "if everyone they saw was susceptible, how many people do you expect to get sick per sick person?" But what that ratio means is dictated by the context of the model. A frequent utility in comparing ratios is to discuss intervention impact. One might write "Our model found that overall reproduction was reduced to a factor of 0.XYZ (comprehensive infectivity parameters a=nnn b=mmm c=ppp) when the population undertook vaccination schedule foo. Conversely, reproduction was reduced to a factor of 0.JKL for the same infection under vaccination schedule bar. See Figure 14 for complete population state levels over time." That's all that is. It's a number that has some meaning in context, and one that is often removed from that context and unreasonably expected to retain its meaning. Averages don't mean as much if you don't know if your distribution is multimodal. It's the same thing - a summary stat that can give you a glimpse of the whole, but still just a summary stat.

selimthegrim 3y ago

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.

jasmer 3y ago

[dead]

aredox 3y ago

It's not useless. Do you think all contagious illnesses spread at the same rate? InB4 "clouds don't exist, nobody can tell exactly where they start and end"

mike_hearn 3y ago
It would be useful to be able to measure some sort of inherent infectiousness, but R0 as actually defined and computed today is useless. It looks scientific on the surface but everyone who calculates it gets a totally different result, and because there's no actual underlying theory involved (just playing with stats), there's no way to decide which estimate is correct. To get meaningfully constant values for infectiousness you'd probably need a microbiological / genetic theory that let you derive it from RNA sequences.
- tripletao 3y ago
  > To get meaningfully constant values for infectiousness you'd probably need a theory that let you derive it from RNA sequences. As others have already noted in this thread, the rate at which a disease spreads inherently depends on both the biology of the pathogen and human behavior. For example, if everyone goes to crowded nightclubs, then R0 for an airborne respiratory disease goes up. If everyone stays home, then it goes down. The biology of the pathogen and human behavior interact in complex, multidimensional ways; promoting condoms will reduce R0 for HIV, but not for influenza. The disease spreads only through human behavior, so the concept of infectiousness independent of that simply doesn't exist. You are correct that R0 is determined by curve-fitting to actual case counts. It couldn't be otherwise though, since that's the only way to capture the actual human behavior. We expect different R0 in different situations, so the range you see for SARS-CoV-2 isn't a surprise. This is textbook stuff: > Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R0. Because R0 is a function of the effective contact rate, the value of R0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R0 values (range 5.4–18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R0 values of 3.7–203.3 (23). This wide range highlights the potential variability in the value of R0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances. https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article As with your confused understanding of PCR specificity, you may have been misinformed by indefensibly oversimplified public messaging as to the definition of R0. (I had a comment deleted from /r/coronavirus in 2020, because they considered my statement that R0 varies with environment and human behavior to be misinformation; the citation didn't help.) The literature is once again available for anyone who wishes to read it, though.

PurelyApplied 3y ago

> > it is recommended not to ... compare values based on different models > i.e. R0 numbers are worthless. You can't compare these values between pathogens That conclusion doesn't follow from the predicate. The model isn't a single simulation run. To mathematicians, the model is the entire framework. It's the SIR model, or the SIS model, or the SIER model, or my favorite model of immunity decay against pertussis... _those_ are the models. The virulence of the infection being modeled, resilience of the population, morbidity and mortality rates, rate and timing of population quarantine, vaccine adoption rate, vaccine efficacy, whether any of the aforementioned values are themselves functions of time... those are just parameters into your model. We absolutely compare the values between pathogens _within the same model_ when discussing the utility of the model. But typically, the utility in reproduction ratio is as a point of comparison as you tune other parameters. This is part of why a comparison of reproduction ratio across models isn't recommended. It's not just that it's hard to draw meaning from the comparison, it's that the base assumptions of the models might be too different for the comparison to hold any meaning. The reproductive ratio between a continuous model with uniform population mixing is fundamentally different from the reproductive ratio approximated from discrete simulation. They may both broadly speak to "if everyone they saw was susceptible, how many people do you expect to get sick per sick person?" But what that ratio means is dictated by the context of the model. A frequent utility in comparing ratios is to discuss intervention impact. One might write "Our model found that overall reproduction was reduced to a factor of 0.XYZ (comprehensive infectivity parameters a=nnn b=mmm c=ppp) when the population undertook vaccination schedule foo. Conversely, reproduction was reduced to a factor of 0.JKL for the same infection under vaccination schedule bar. See Figure 14 for complete population state levels over time." That's all that is. It's a number that has some meaning in context, and one that is often removed from that context and unreasonably expected to retain its meaning. Averages don't mean as much if you don't know if your distribution is multimodal. It's the same thing - a summary stat that can give you a glimpse of the whole, but still just a summary stat.