> 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?
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.
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.
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.
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.
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.
Also, I've heard that one of Dunning or Kruger has objected to the way the term/concept is commonly used. How ironic is that? AIUI they originally proposed that people at many levels underestimate how much they still don't know. It wasn't limited to people who believe they've jumped all the way from the novice end to the expert end, which is the more common usage.
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.
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.
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.
> 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?
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:
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
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”.
@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 ...
>The fact that mistakes have been made doesn't mean there's nothing to their field and anyone can do it
I was not making that claim. The premise is: should every non epidemiologist keep their mouth shout and trust the domain experts blindly? Don't they have the right in a democracy to question the science and therefor the legitimicy of the suggested policies?
That's a hard sell, when the field of experts themselves have little consensus.
I might be seeing this from a different bubble, over here in the Netherlands. We have had our equivalent of the CDC show and explain their models and policy choices in detail. We got powerpoints and everything.
Yet, it turned into a shitshow. It was already going wrong in Italy, when they claimed their models suggested we have it fully under control if only we wash our hands, and they then let one of the biggest social events happen (think mardi gras). Even though we already had our first cases.
Italy's expert initially said they had it under control. They suggested cultural differences in health and healthcare made it such a bigger problem for China than for them. Then it went to shit in Italy. Then the dutch experts said, after a few cases, they had it completely under control and the reason it went wrong in Italy was cultural differences in health and healthcare. Yeah, racist ignorance.
And all their preparations didn't include supply chain management or the human factor. For example: the relationship (worldwide) between the lockdowns and the available of PPE -vs- the availability of PPE and the spread of the virus. Because none of those things are part of the known models. So they just exclude it entirely.
So, given all of that, yeah, I would like to see their homework in more detail, and we should all put in the intellectual effort, to sort this stuff out.
>Are there no software bugs?
More than any other engineering discipline. So software engineers do actually have more experience debugging collapsing systems build on the wrong assumptions.
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:
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 ...
> 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.
A lot of science relies on observation without experiment. That's still enough to support a cycle of hypothesis, test, etc. For example, where are the experiments in astrophysics? Seen anyone build a quasar recently? Epidemiology is no different. From the very first days of the "pump handle" cholera situation, epidemiologists have acted based on empirical observation. Then they have built models based on empirical findings, and those models are constantly checked against new findings. How is that not science? It seems considerably more scientific than most of what we in tech do.
Astrophysics does have pretty considerable on-going problems as a result of its inability to do real experiments. Even regular physics is heading the same way. Dark matter is basically a giant admission that our models of the universe don't match reality and we have no idea why or how to fix them. String theory does trigger big debates about whether it's actually science or not.
Astrophysics is saved as a science largely by the universe's close relationship with more testable on-earth physics - the motion of planets is Newtonian motion which can be experimented on locally, the physics inside a star is something that can be replicated in a Tokamak, and so on. Sometimes observations are all we have to go on, like with the light bending observation that cemented Einstein as right, but that's rare. Mostly physics yields to experiments. When it doesn't you do see decades without progress.
those models are constantly checked against new findings
I think I'll have to dispute that. My research into epidemiology and its history suggests that this part never seems to happen except by outsiders. Models produce totally failed predictions, in fact often reality falls outside even the uncertainty bounds that are already very generous.
Scientists would at this point stop and say, hmm, there seems to be a problem with germ theory. Our predictions are wrong so we need a refined theory. They'd then design experiments to figure out what that problem is.
Epidemiologists never do this, perhaps because they often don't have a medical background. They can't simply switch contexts and start doing lab experiments. So they simply declare themselves to be highly successful despite all the evidence they weren't, and wait for the next outbreak. Then they jump up and present often the same or very similar models to policymakers as "science", and inform them if they don't immediately implement the epidemiologists recommendations (which are always extreme) millions will die.
COVID is a case in point. Imperial presented a model that was in the words of its author based on "thousands of lines of undocumented C written over 13 years ago to model flu pandemics":
It's rather ironic that we've seen endless attacks on people who "claim it's just like the flu" when one of the world's leading epidemiologist literally used a flu model to predict COVID.
But this statement implies epidemiological theories and models didn't really change in 13 years. And when you look at these models in depth, that isn't a surprise. They're astonishingly simple. There hasn't been any breakthrough in germ theory from these people, and their models always predict massive epidemics that don't actually happen. To explain this they'd need to do medicine, or at least honestly admit that current theory is inadequate to explain observed reality. They never do this, which is why in my eyes they aren't scientists.
I'm sorry, but you get an absolutely huge [citation needed] for that. You're making a lot of highly prejudicial claims, with just about zero data to back them up.
> thousands of lines of undocumented C written over 13 years ago to model flu pandemics
Is the fact that it's undocumented even relevant? That seems like severe bikeshedding, in the sense of judging what you know instead of what matters. Are there qualitative differences between how flu and coronaviruses spread? I don't mean do the diseases have different symptoms or prognosis, though I see you're conflating the two. Does the spread of the virus differ other than in the sense of tweaking variables like R0? It's not immediately clear why a model originally developed for flu would not be readily adaptable to COVID-19, just like a CFD model can be adapted to different problems. It doesn't require detailed medical or virological knowledge either - just empirical observation, of which epidemiologists are quite capable.
BTW, let me ask you: do you consider economics a science? Because it seems like your opposition to epidemiologists' conclusions seems to be based in economic projections. How ironic.
Absolutely. Ferguson asserted he couldn't reveal his code because nobody except him knows how to run it. That means nothing based on it could have ever been properly peer reviewed in 13 years. Over a month later he still hasn't uploaded his code anywhere, despite its outputs driving extreme social policies around the world. That's pathetic and deserves condemnation from anyone who cares about the scientific method. Being able to peer review and reproduce published results is critical - look at what happened in psychology when people tried to replicate famous papers - but this is one more way epidemiology doesn't even try to be scientific.
Does the spread of the virus differ other than in the sense of tweaking variables like R0? ... It's not immediately clear why a model originally developed for flu would not be readily adaptable to COVID-19
Nobody knows, do they. If anyone understood these viruses their models wouldn't always be so wrong....
26 comments
[ 2.7 ms ] story [ 70.1 ms ] threadAren't flippant comments of this type also a virulent form of DKE?
I don't deny there is some truth to it, but it seems perfectly suited for gaining traction through the Twitter mobs...
You mean the highlight that medium applies automatically based on user behavior and the author has no part in controlling?
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.
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.
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?
The difference now is the Internet, and the fact that lack of leadership has created a huge vacuum that this stuff happily fills.
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.
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?
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/#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...
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-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 ...
>The fact that mistakes have been made doesn't mean there's nothing to their field and anyone can do it
I was not making that claim. The premise is: should every non epidemiologist keep their mouth shout and trust the domain experts blindly? Don't they have the right in a democracy to question the science and therefor the legitimicy of the suggested policies?
That's a hard sell, when the field of experts themselves have little consensus.
I might be seeing this from a different bubble, over here in the Netherlands. We have had our equivalent of the CDC show and explain their models and policy choices in detail. We got powerpoints and everything.
Now before you make assumptions: https://www.weforum.org/agenda/2019/11/countries-preparednes... .. the Netherlands ranked 3rd in preparedness.
Yet, it turned into a shitshow. It was already going wrong in Italy, when they claimed their models suggested we have it fully under control if only we wash our hands, and they then let one of the biggest social events happen (think mardi gras). Even though we already had our first cases.
Italy's expert initially said they had it under control. They suggested cultural differences in health and healthcare made it such a bigger problem for China than for them. Then it went to shit in Italy. Then the dutch experts said, after a few cases, they had it completely under control and the reason it went wrong in Italy was cultural differences in health and healthcare. Yeah, racist ignorance.
And all their preparations didn't include supply chain management or the human factor. For example: the relationship (worldwide) between the lockdowns and the available of PPE -vs- the availability of PPE and the spread of the virus. Because none of those things are part of the known models. So they just exclude it entirely.
So, given all of that, yeah, I would like to see their homework in more detail, and we should all put in the intellectual effort, to sort this stuff out.
>Are there no software bugs?
More than any other engineering discipline. So software engineers do actually have more experience debugging collapsing systems build on the wrong assumptions.
>Can just anyone come in and fix those?
Yes, mostly. Its called a pull request.
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
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 ...
A lot of science relies on observation without experiment. That's still enough to support a cycle of hypothesis, test, etc. For example, where are the experiments in astrophysics? Seen anyone build a quasar recently? Epidemiology is no different. From the very first days of the "pump handle" cholera situation, epidemiologists have acted based on empirical observation. Then they have built models based on empirical findings, and those models are constantly checked against new findings. How is that not science? It seems considerably more scientific than most of what we in tech do.
Astrophysics is saved as a science largely by the universe's close relationship with more testable on-earth physics - the motion of planets is Newtonian motion which can be experimented on locally, the physics inside a star is something that can be replicated in a Tokamak, and so on. Sometimes observations are all we have to go on, like with the light bending observation that cemented Einstein as right, but that's rare. Mostly physics yields to experiments. When it doesn't you do see decades without progress.
those models are constantly checked against new findings
I think I'll have to dispute that. My research into epidemiology and its history suggests that this part never seems to happen except by outsiders. Models produce totally failed predictions, in fact often reality falls outside even the uncertainty bounds that are already very generous.
Scientists would at this point stop and say, hmm, there seems to be a problem with germ theory. Our predictions are wrong so we need a refined theory. They'd then design experiments to figure out what that problem is.
Epidemiologists never do this, perhaps because they often don't have a medical background. They can't simply switch contexts and start doing lab experiments. So they simply declare themselves to be highly successful despite all the evidence they weren't, and wait for the next outbreak. Then they jump up and present often the same or very similar models to policymakers as "science", and inform them if they don't immediately implement the epidemiologists recommendations (which are always extreme) millions will die.
COVID is a case in point. Imperial presented a model that was in the words of its author based on "thousands of lines of undocumented C written over 13 years ago to model flu pandemics":
https://twitter.com/neil_ferguson/status/1241835454707699713...
It's rather ironic that we've seen endless attacks on people who "claim it's just like the flu" when one of the world's leading epidemiologist literally used a flu model to predict COVID.
But this statement implies epidemiological theories and models didn't really change in 13 years. And when you look at these models in depth, that isn't a surprise. They're astonishingly simple. There hasn't been any breakthrough in germ theory from these people, and their models always predict massive epidemics that don't actually happen. To explain this they'd need to do medicine, or at least honestly admit that current theory is inadequate to explain observed reality. They never do this, which is why in my eyes they aren't scientists.
I'm sorry, but you get an absolutely huge [citation needed] for that. You're making a lot of highly prejudicial claims, with just about zero data to back them up.
> thousands of lines of undocumented C written over 13 years ago to model flu pandemics
Is the fact that it's undocumented even relevant? That seems like severe bikeshedding, in the sense of judging what you know instead of what matters. Are there qualitative differences between how flu and coronaviruses spread? I don't mean do the diseases have different symptoms or prognosis, though I see you're conflating the two. Does the spread of the virus differ other than in the sense of tweaking variables like R0? It's not immediately clear why a model originally developed for flu would not be readily adaptable to COVID-19, just like a CFD model can be adapted to different problems. It doesn't require detailed medical or virological knowledge either - just empirical observation, of which epidemiologists are quite capable.
BTW, let me ask you: do you consider economics a science? Because it seems like your opposition to epidemiologists' conclusions seems to be based in economic projections. How ironic.
On current events:
https://www.spectator.co.uk/article/six-questions-that-neil-...
https://judithcurry.com/2020/04/01/imperial-college-uk-covid...
https://twitter.com/AlexBerenson/status/1245748387359711234
https://medium.com/@wpegden/a-call-to-honesty-in-pandemic-mo...
On a major prior disaster caused by listening to Imperial modellers:
https://www.researchgate.net/publication/51683518_Destructiv...
https://journals.plos.org/plosone/article/file?id=10.1371/jo...
http://pdfs.semanticscholar.org/5ab5/c0d4699e499e9c99a20c8d7...
https://www.telegraph.co.uk/news/2020/03/28/neil-ferguson-sc...
Here they are getting it all wrong on Zika:
https://science.sciencemag.org/content/sci/353/6297/353.full...
https://www.sciencemag.org/news/2017/08/zika-has-all-disappe...
And finally, a page with many useful links under "further reading":
https://lockdownsceptics.org/how-reliable-is-imperial-colleg...
I hope that provides some interesting reading!
Is the fact that it's undocumented even relevant?
Absolutely. Ferguson asserted he couldn't reveal his code because nobody except him knows how to run it. That means nothing based on it could have ever been properly peer reviewed in 13 years. Over a month later he still hasn't uploaded his code anywhere, despite its outputs driving extreme social policies around the world. That's pathetic and deserves condemnation from anyone who cares about the scientific method. Being able to peer review and reproduce published results is critical - look at what happened in psychology when people tried to replicate famous papers - but this is one more way epidemiology doesn't even try to be scientific.
Does the spread of the virus differ other than in the sense of tweaking variables like R0? ... It's not immediately clear why a model originally developed for flu would not be readily adaptable to COVID-19
Nobody knows, do they. If anyone understood these viruses their models wouldn't always be so wrong....