6 ms·

Flatten the Curve of Armchair Epidemiology

31 points by scott_s 6y ago

augustt 6y ago
I don't think it's too surprising people are not interested in listening to authorities given 1) the bungled response from the WHO, CDC, and of course Trump administration. 2) the lack of clear messaging about the steps forward. The curve was flattened, now what?
darepublic 6y ago
> the recent outbreak indicates that severe DKE-19 primarily affects men ages 24–36 working in tech, for reasons unknown to scientists who are unaccountably also men. Aren't flippant comments of this type also a virulent form of DKE?
- MattGaiser 6y ago
  It is just a lazy way to rally supporters. Notice that it is the highlighted part they want you to tweet out.
  - notacoward 6y ago
    That dismissal seems a bit lazy and flippant too. Plus attribution of motive. Like it or not, there is a grain of truth to the idea that techies (young and old) are particularly prone to this kind of thing. We're rather notorious for it. It's a natural and predictable consequence of having often been the smartest person in the room, able to leap ahead of others who have studied a subject (only slightly) longer. Equating income with intelligence doesn't help either. It takes a while to learn that "opine first, study later" doesn't work so well when dealing with fields equal or superior to our own in technical depth and rigor requiring years of effort to gain expertise, or that it's actively dangerous to indulge such impulses when the stakes are far higher than all but a handful of computer bugs.
    - MattGaiser 6y ago
      I attribute motivation because when it came time to choose one sentence in their article to tweet, that was what they chose. I don't deny there is some truth to it, but it seems perfectly suited for gaining traction through the Twitter mobs...
  - cjhopman 6y ago
    > Notice that it is the highlighted part they want you to tweet out. You mean the highlight that medium applies automatically based on user behavior and the author has no part in controlling?
- notacoward 6y ago
  They might be rude and hurt some people's feelings, but they're not pretending to represent expertise one does not have so they're nothing to do with DKE.
  - thu2111 6y ago
    They implicitly are, as they're arguing that these men aren't experts. Judging this would require one of two things: 1. The author can tell what is expertise based on the words spoken, the quality of the material produced. But then they'd need to be an expert themselves. 2. The authors defines expertise purely in terms of credentialism. Someone works at university X and not university Y, therefore they are experts and anyone else isn't. As they mock people who engage in "armchair epidemiology" it's not (1), so it must be (2). But it's that absence of critical thinking that deserves to be flattened. It's an attitude of "don't think just obey" - the exact thing that a university education supposedly trains out of people. Supposedly. It's how Google ends up deleting videos of doctors because they aren't "experts" in health or disease, whilst leaving the pronouncements of people who can write R up.
    - notacoward 6y ago
      If it's not (1) then it's not Dunning-Kruger. Credentialism is a different fallacy (argumentum ad verecundiam). Saying we should flatten the curve for all deficits of critical thinking is reading something into the article that's not actually there, no matter how worthy you or I might believe such an effort is.
      - thu2111 6y ago
        Alright, fair enough. You could argue these writers are implicitly claiming to be meta-experts, that is, they have superior understanding of the nature and location of expertise than other people do, although they don't. But I guess this sort of subjective argument is why I don't like Dunning-Kruger as a concept. Especially given that it's an output of social psychology, a field that started to collapse once outsiders from other fields i.e. non-experts started critically thinking about the content of psychology papers. Seems kind of ironic.
- II2II 6y ago
  Given that the article is also a parody of itself, I suspect the answer is yes.
SketchySeaBeast 6y ago
Reminds me of the Olympics, when for a brief few weeks every 4 years, a ton of people suddenly become experts in the skeleton bobsled.
- dariusj18 6y ago
  Gotta lean in more, what a rookie!
NelsonMinar 6y ago
A particularly urgent need to identify and quarantine VC investors who think that because they financed a virally marketed app once, they are now experts in Covid-19.
chkaloon 6y ago
BS, flim-flam, conspiracies, and Cliff Clavens will always flourish at times like this. I remember before the Internet, mimeographed and Xerox'd flyers were the primary transmission method. The difference now is the Internet, and the fact that lack of leadership has created a huge vacuum that this stuff happily fills.
quaffapint 6y ago
My daughter is studying to be an epidemiologist. I used to have to explain to people what that was.
ralfn 6y ago
It's completely logical that engineers have professional deformation. Society is in crisis and there are metrics. How can you realistically expect engineers not to start debugging and interpreting the metrics? Yes. Engineers aren't medical domain experts, but the experts and/or political leadership failed. The models are not being shared. The process isn't transparent, nor optimized for quick interventions based on incomplete information. And claims and policies differ around the world. There is no consensus among the experts. The truth is, engineers might have more experience in how to operate during a crisis like this. How to quickly asses risks in a world of incomplete and sometimes contradictory information. I wish the real experts were more convincing in their abilities. But in the west this is the first time they deal with this kind of situation while it not being a hypothetical. They is their first real experience with it as well.
- notacoward 6y ago
  > engineers might have more experience in how to operate during a crisis like this. That's probably more true for real engineers - the kind requiring certification and insurance - than software engineers. Yet they hesitate to speak over the epidemiologists. Why? Because they know how to be diligent and not say things they can't back up. > But in the west this is the first time they deal with this kind of situation while it not being a hypothetical. I've personally had to work with epidemiologists in the context of a (minor) measles outbreak. TB is an ongoing issue in many western countries. Norovirus. Legionnaire's. Western epidemiologists have also been involved in outbreaks elsewhere - e.g. SARS or Ebola. Their experience isn't any less because those outbreaks weren't on their home turf. Sure, they don't have direct experience with a pandemic, but neither do the amateurs. How can you think that people who have spent years studying the theory and getting real in-the-field experience are less qualified than some random software developer or VC when neither have faced this before? The fact that mistakes have been made doesn't mean there's nothing to their field and anyone can do it. Are there no software bugs? Can just anyone come in and fix those?
  - thu2111 6y ago
    But you're overlooking the most likely outcome - that nobody is qualified in this field. However software developers can admit this and epidemiologists can't. Bearing in mind for a moment that epidemiologists are amateur programmers, their work output is literally papers+code, they have a major handicap that they can't stop and say "maybe this doesn't work". What else would they do? I've found the comments by Caswell Bligh very insightful on this topic. Bligh is one of the awful "men in tech" programmers the article laments, who has been writing his own models based on epidemiological papers. Perhaps even from an armchair. Everything he's concluded is worth reading but I'll quote some extracts: https://lockdownsceptics.org/2020/04/26/latest-news-12/#comment-935 https://lockdownsceptics.org/2020/04/26/latest-news-12/#comm... Do ‘R0’ or the other ‘R’ values even mean anything when ‘infection’ versus ‘exposure’ can’t even be defined? If social distancing reduces the level of exposures but not the number, does this mean the ‘R’ number is the same, or reducing? Maybe the lockdown means that the population is being quietly, in the background, inoculated to a certain level of resistance (which is what I think is probably happening) but that it doesn’t show up in antibody tests. Many of these things will never be known. They are probably not even predictable with viruses that are not ‘novel’. Virologists and immunologists may know some of it, and they also know what they don’t know. The modellers don’t know any of it. As such, models will never be able to give you absolute values for the number of infections, deaths and so on. We may as well not bother with the units on the axes and simply use the model to illustrate the different shapes that are possible In this interview there's a useful quote: https://fivethirtyeight.com/videos/how-one-modeler-is-trying-to-forecast-the-toll-of-covid-19/ https://fivethirtyeight.com/videos/how-one-modeler-is-trying... At about 8.00: “The nature of SEIR models is they do tend to always show this huge increase up to the point of saturation where most people get infected and very often that has not historically happened”. https://lockdownsceptics.org/how-reliable-is-imperial-colleges-modelling/#comment-1070 https://lockdownsceptics.org/how-reliable-is-imperial-colleg... @Hugh Osmond. What you’ve articulated – and it’s something that’s been bothering me – is that ‘R0’ and the other ‘R’ variants are not a real characteristic of the virus. Snapshots during the epidemic may give you observed values for them, but that doesn’t mean that they represent something fundamental. Attempting to both derive, and then use values for ‘R’ in models, has led to this disaster. It needs to be shouted from the rooftops. ‘R0’ is not a characteristic of the virus. And saying “‘R0’ depends on the virus but also age/population density/pollution etc. etc.” is just another way of saying the same thing. If you know ‘R0’ in advance, you’ve already anticipated what the simple model is going to tell you – there’s almost no point running the model. In the more sophisticated, more realistic model we shouldn’t even try to accommodate an ‘R0’ input at all, and nor should we try to modify an ‘R’ as time goes on. That’s the wrong way round. Really, I think you’ve articulated something extremely important here. It’s been driving me mad for weeks: the illness is being defined in terms of a standard, simplistic, mathematical model that was easy to compute before the invention of computers. But even now, if you want to make a more sophisticated model, you are still forced to talk about its results in terms of the simple model. When I told a biologist friend of mine I was creating a model, the first thing she asked me was “What value of R0 are you using?”. This is how we have arrived at this disaster.
thu2111 6y ago
It's pretty apparent the writers don't understand epidemiology themselves. What is this field, really? It's not medicine. Its most famous practitioners often don't have any medical training or background, e.g. Prof Ferguson who did his PhD in theoretical physics. The assumptions it uses are of the level of medical complexity that a small child can understand - essentially that people who spend time close to each other infect each other. The models don't take as input the molecular biology or DNA of a virus that's being simulated. Arguably it's not science. A basic characteristic of science is that it tells us something new about the world, something that can be proven true. Experiments are fundamental to science for that reason. Epidemiology is about the development of models. Models aren't science because they cannot ever tell us something truly new or unexpected: a model is ultimately just a rendering of its author's assumptions. When someone puts some formulas into R and copies the results into a paper, the model is acting as a kind of fancy PowerPoint. It's useful to illustrate the conclusions that follow from the assumptions, but it no more lets us discover new things than PowerPoint does. (n.b. Under this definition, a lot of what gets published as computer science also isn't science but rather engineering. "Data science" on the other hand is a tautology. That's a fine conclusion and one I'll happily defend.) It often gets described as mathematics. It's not mathematics any more than economics or coding a search engine is mathematics. Mathematicians publish new theorems and proofs. The maths they develop may be of theoretical interest or it may find applicability in other fields, but it ultimately stands alone. Epidemiologists use maths as a part of their work but so do many other people. As far as I can tell the field that resembles epidemiology most closely is actually video game programming. The models epidemiologists vary in their approach, but the most advanced ones attempt to simulate society by simulating the interactions between people. That's why they can take into account things like school closures or novel social policies. 3Blue1Brown has done a good video where he implements an epidemiological model: https://www.youtube.com/watch?v=gxAaO2rsdIs https://www.youtube.com/watch?v=gxAaO2rsdIs One of the top comments is "I'm a professional epidemiologist (generally focusing on modelling livestock diseases) and I'm angry at how amazingly good your graphics are compared to anything I've come up with!" - so what's being done in that video isn't much different to what they do. If you play Cities: Skylines then you actually end up running an epidemiological model, because the inhabitants of your city are all simulated individually. They can get sick and die, they can overwhelm hospitals or even the road networks between them. For instance put a water intake pipe too near to a sewage outlet and you'll have mass disease pretty quickly. After watching epidemiologists flame each other in public for the last month, disagree on basically everything, constantly make unfalsifiable claims, have their few falsifiable predictions be indeed proven false and repeatedly publish papers through news outlets, I've come to the conclusion that this field structurally has no way to resolve disagreements or improve itself. The epidemiological models being presented by places like Imperial or University of Washington are no more reliable now than they were 20 years ago for the foot-and-mouth epidemic in the UK. How can a field not get even slightly more accurate over a period of 20 years? Well, because there's no incentive to. Like academic economists they're rewarded for publishing papers, not being correct. The "men working in tech" this comment so snottily dresses down differ from epidemiologists in some crucial ways. Many of them will work with data in a context where being wrong will ultimately inflict financial damage on themselves and their coworkers, or even crater their startup. Their ability to work with data correctly matters to them in ways it doesn't really matter to academics, whose jobs are perfectly safe even in this time of mass unemployment and furloughs. Given the total failure of academics to correctly predict real epidemics, I hope we see way more such men bring outside views to what is otherwise a small and stagnant field.

31 points by scott_s 6y ago

I don't think it's too surprising people are not interested in listening to authorities given 1) the bungled response from the WHO, CDC, and of course Trump administration. 2) the lack of clear messaging about the steps forward. The curve was flattened, now what?

darepublic 6y ago

> the recent outbreak indicates that severe DKE-19 primarily affects men ages 24–36 working in tech, for reasons unknown to scientists who are unaccountably also men. Aren't flippant comments of this type also a virulent form of DKE?

MattGaiser 6y ago
It is just a lazy way to rally supporters. Notice that it is the highlighted part they want you to tweet out.
- notacoward 6y ago
  That dismissal seems a bit lazy and flippant too. Plus attribution of motive. Like it or not, there is a grain of truth to the idea that techies (young and old) are particularly prone to this kind of thing. We're rather notorious for it. It's a natural and predictable consequence of having often been the smartest person in the room, able to leap ahead of others who have studied a subject (only slightly) longer. Equating income with intelligence doesn't help either. It takes a while to learn that "opine first, study later" doesn't work so well when dealing with fields equal or superior to our own in technical depth and rigor requiring years of effort to gain expertise, or that it's actively dangerous to indulge such impulses when the stakes are far higher than all but a handful of computer bugs.
  - MattGaiser 6y ago
    I attribute motivation because when it came time to choose one sentence in their article to tweet, that was what they chose. I don't deny there is some truth to it, but it seems perfectly suited for gaining traction through the Twitter mobs...
- cjhopman 6y ago
  > Notice that it is the highlighted part they want you to tweet out. You mean the highlight that medium applies automatically based on user behavior and the author has no part in controlling?
notacoward 6y ago
They might be rude and hurt some people's feelings, but they're not pretending to represent expertise one does not have so they're nothing to do with DKE.
- thu2111 6y ago
  They implicitly are, as they're arguing that these men aren't experts. Judging this would require one of two things: 1. The author can tell what is expertise based on the words spoken, the quality of the material produced. But then they'd need to be an expert themselves. 2. The authors defines expertise purely in terms of credentialism. Someone works at university X and not university Y, therefore they are experts and anyone else isn't. As they mock people who engage in "armchair epidemiology" it's not (1), so it must be (2). But it's that absence of critical thinking that deserves to be flattened. It's an attitude of "don't think just obey" - the exact thing that a university education supposedly trains out of people. Supposedly. It's how Google ends up deleting videos of doctors because they aren't "experts" in health or disease, whilst leaving the pronouncements of people who can write R up.
  - notacoward 6y ago
    If it's not (1) then it's not Dunning-Kruger. Credentialism is a different fallacy (argumentum ad verecundiam). Saying we should flatten the curve for all deficits of critical thinking is reading something into the article that's not actually there, no matter how worthy you or I might believe such an effort is.
    - thu2111 6y ago
      Alright, fair enough. You could argue these writers are implicitly claiming to be meta-experts, that is, they have superior understanding of the nature and location of expertise than other people do, although they don't. But I guess this sort of subjective argument is why I don't like Dunning-Kruger as a concept. Especially given that it's an output of social psychology, a field that started to collapse once outsiders from other fields i.e. non-experts started critically thinking about the content of psychology papers. Seems kind of ironic.
II2II 6y ago
Given that the article is also a parody of itself, I suspect the answer is yes.

SketchySeaBeast 6y ago

Reminds me of the Olympics, when for a brief few weeks every 4 years, a ton of people suddenly become experts in the skeleton bobsled.

dariusj18 6y ago
Gotta lean in more, what a rookie!

NelsonMinar 6y ago

A particularly urgent need to identify and quarantine VC investors who think that because they financed a virally marketed app once, they are now experts in Covid-19.

chkaloon 6y ago

BS, flim-flam, conspiracies, and Cliff Clavens will always flourish at times like this. I remember before the Internet, mimeographed and Xerox'd flyers were the primary transmission method. The difference now is the Internet, and the fact that lack of leadership has created a huge vacuum that this stuff happily fills.

quaffapint 6y ago

My daughter is studying to be an epidemiologist. I used to have to explain to people what that was.

ralfn 6y ago

It's completely logical that engineers have professional deformation. Society is in crisis and there are metrics. How can you realistically expect engineers not to start debugging and interpreting the metrics? Yes. Engineers aren't medical domain experts, but the experts and/or political leadership failed. The models are not being shared. The process isn't transparent, nor optimized for quick interventions based on incomplete information. And claims and policies differ around the world. There is no consensus among the experts. The truth is, engineers might have more experience in how to operate during a crisis like this. How to quickly asses risks in a world of incomplete and sometimes contradictory information. I wish the real experts were more convincing in their abilities. But in the west this is the first time they deal with this kind of situation while it not being a hypothetical. They is their first real experience with it as well.

notacoward 6y ago
> engineers might have more experience in how to operate during a crisis like this. That's probably more true for real engineers - the kind requiring certification and insurance - than software engineers. Yet they hesitate to speak over the epidemiologists. Why? Because they know how to be diligent and not say things they can't back up. > But in the west this is the first time they deal with this kind of situation while it not being a hypothetical. I've personally had to work with epidemiologists in the context of a (minor) measles outbreak. TB is an ongoing issue in many western countries. Norovirus. Legionnaire's. Western epidemiologists have also been involved in outbreaks elsewhere - e.g. SARS or Ebola. Their experience isn't any less because those outbreaks weren't on their home turf. Sure, they don't have direct experience with a pandemic, but neither do the amateurs. How can you think that people who have spent years studying the theory and getting real in-the-field experience are less qualified than some random software developer or VC when neither have faced this before? The fact that mistakes have been made doesn't mean there's nothing to their field and anyone can do it. Are there no software bugs? Can just anyone come in and fix those?
- thu2111 6y ago
  But you're overlooking the most likely outcome - that nobody is qualified in this field. However software developers can admit this and epidemiologists can't. Bearing in mind for a moment that epidemiologists are amateur programmers, their work output is literally papers+code, they have a major handicap that they can't stop and say "maybe this doesn't work". What else would they do? I've found the comments by Caswell Bligh very insightful on this topic. Bligh is one of the awful "men in tech" programmers the article laments, who has been writing his own models based on epidemiological papers. Perhaps even from an armchair. Everything he's concluded is worth reading but I'll quote some extracts: https://lockdownsceptics.org/2020/04/26/latest-news-12/#comment-935 https://lockdownsceptics.org/2020/04/26/latest-news-12/#comm... Do ‘R0’ or the other ‘R’ values even mean anything when ‘infection’ versus ‘exposure’ can’t even be defined? If social distancing reduces the level of exposures but not the number, does this mean the ‘R’ number is the same, or reducing? Maybe the lockdown means that the population is being quietly, in the background, inoculated to a certain level of resistance (which is what I think is probably happening) but that it doesn’t show up in antibody tests. Many of these things will never be known. They are probably not even predictable with viruses that are not ‘novel’. Virologists and immunologists may know some of it, and they also know what they don’t know. The modellers don’t know any of it. As such, models will never be able to give you absolute values for the number of infections, deaths and so on. We may as well not bother with the units on the axes and simply use the model to illustrate the different shapes that are possible In this interview there's a useful quote: https://fivethirtyeight.com/videos/how-one-modeler-is-trying-to-forecast-the-toll-of-covid-19/ https://fivethirtyeight.com/videos/how-one-modeler-is-trying... At about 8.00: “The nature of SEIR models is they do tend to always show this huge increase up to the point of saturation where most people get infected and very often that has not historically happened”. https://lockdownsceptics.org/how-reliable-is-imperial-colleges-modelling/#comment-1070 https://lockdownsceptics.org/how-reliable-is-imperial-colleg... @Hugh Osmond. What you’ve articulated – and it’s something that’s been bothering me – is that ‘R0’ and the other ‘R’ variants are not a real characteristic of the virus. Snapshots during the epidemic may give you observed values for them, but that doesn’t mean that they represent something fundamental. Attempting to both derive, and then use values for ‘R’ in models, has led to this disaster. It needs to be shouted from the rooftops. ‘R0’ is not a characteristic of the virus. And saying “‘R0’ depends on the virus but also age/population density/pollution etc. etc.” is just another way of saying the same thing. If you know ‘R0’ in advance, you’ve already anticipated what the simple model is going to tell you – there’s almost no point running the model. In the more sophisticated, more realistic model we shouldn’t even try to accommodate an ‘R0’ input at all, and nor should we try to modify an ‘R’ as time goes on. That’s the wrong way round. Really, I think you’ve articulated something extremely important here. It’s been driving me mad for weeks: the illness is being defined in terms of a standard, simplistic, mathematical model that was easy to compute before the invention of computers. But even now, if you want to make a more sophisticated model, you are still forced to talk about its results in terms of the simple model. When I told a biologist friend of mine I was creating a model, the first thing she asked me was “What value of R0 are you using?”. This is how we have arrived at this disaster.

thu2111 6y ago

It's pretty apparent the writers don't understand epidemiology themselves. What is this field, really? It's not medicine. Its most famous practitioners often don't have any medical training or background, e.g. Prof Ferguson who did his PhD in theoretical physics. The assumptions it uses are of the level of medical complexity that a small child can understand - essentially that people who spend time close to each other infect each other. The models don't take as input the molecular biology or DNA of a virus that's being simulated. Arguably it's not science. A basic characteristic of science is that it tells us something new about the world, something that can be proven true. Experiments are fundamental to science for that reason. Epidemiology is about the development of models. Models aren't science because they cannot ever tell us something truly new or unexpected: a model is ultimately just a rendering of its author's assumptions. When someone puts some formulas into R and copies the results into a paper, the model is acting as a kind of fancy PowerPoint. It's useful to illustrate the conclusions that follow from the assumptions, but it no more lets us discover new things than PowerPoint does. (n.b. Under this definition, a lot of what gets published as computer science also isn't science but rather engineering. "Data science" on the other hand is a tautology. That's a fine conclusion and one I'll happily defend.) It often gets described as mathematics. It's not mathematics any more than economics or coding a search engine is mathematics. Mathematicians publish new theorems and proofs. The maths they develop may be of theoretical interest or it may find applicability in other fields, but it ultimately stands alone. Epidemiologists use maths as a part of their work but so do many other people. As far as I can tell the field that resembles epidemiology most closely is actually video game programming. The models epidemiologists vary in their approach, but the most advanced ones attempt to simulate society by simulating the interactions between people. That's why they can take into account things like school closures or novel social policies. 3Blue1Brown has done a good video where he implements an epidemiological model: https://www.youtube.com/watch?v=gxAaO2rsdIs https://www.youtube.com/watch?v=gxAaO2rsdIs One of the top comments is "I'm a professional epidemiologist (generally focusing on modelling livestock diseases) and I'm angry at how amazingly good your graphics are compared to anything I've come up with!" - so what's being done in that video isn't much different to what they do. If you play Cities: Skylines then you actually end up running an epidemiological model, because the inhabitants of your city are all simulated individually. They can get sick and die, they can overwhelm hospitals or even the road networks between them. For instance put a water intake pipe too near to a sewage outlet and you'll have mass disease pretty quickly. After watching epidemiologists flame each other in public for the last month, disagree on basically everything, constantly make unfalsifiable claims, have their few falsifiable predictions be indeed proven false and repeatedly publish papers through news outlets, I've come to the conclusion that this field structurally has no way to resolve disagreements or improve itself. The epidemiological models being presented by places like Imperial or University of Washington are no more reliable now than they were 20 years ago for the foot-and-mouth epidemic in the UK. How can a field not get even slightly more accurate over a period of 20 years? Well, because there's no incentive to. Like academic economists they're rewarded for publishing papers, not being correct. The "men working in tech" this comment so snottily dresses down differ from epidemiologists in some crucial ways. Many of them will work with data in a context where being wrong will ultimately inflict financial damage on themselves and their coworkers, or even crater their startup. Their ability to work with data correctly matters to them in ways it doesn't really matter to academics, whose jobs are perfectly safe even in this time of mass unemployment and furloughs. Given the total failure of academics to correctly predict real epidemics, I hope we see way more such men bring outside views to what is otherwise a small and stagnant field.