3 ms·

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

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.

notacoward 7y ago
> 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.
- thu2111 7y ago
  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": https://twitter.com/neil_ferguson/status/1241835454707699713?lang=en 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.

notacoward 7y ago

> 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.

thu2111 7y ago
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": https://twitter.com/neil_ferguson/status/1241835454707699713?lang=en 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.