What has struck me most about Epidemiology, are the qualifications needed to be someone pursuing a career in it.
It seems like an Epidemiologist is most likely to be a doctor with some knowledge of statistics. On the contrary, I would have thought that a statistician/operations research person with domain knowledge of medicine would be a lot better suited to study epidemiology.
A lot of the existing models also seem really naïve for something an entire field depends on. Tradeoffs between human life lost due to the disease and effective human lives lost due to economic downturns seem entirely unstudied.
Czechia's actions of forcing people to using masks seems to have worked amazingly well for flattening out the curve. The "science" not seems to support it, and official recommendations are now moving back to recommending masks for all. The popular hypothesis for this, is that it stops nonsymptomatic carriers from being spreaders.
Now this sounds like a fairly obvious thing to realize, and to an extent, existing models should have caught this a lot earlier.
I grew up as a kid with an almost religious belief in Science. Over time, I have come to realize that a lot of our sciences are at best empirical and at worst entirely unverifiable. I am a 100% certain, that in a 100 years, we will look at medicine and nutrition of the 2000s, the way we look at humors and blood letting.
Speaking as an infectious disease epidemiologist - epidemiology is a broad field and includes people from many different backgrounds. The type of epidemiology I do (dynamic modeling of epidemics) leans more towards people with backgrounds in statistics, physics, math, and engineering when compared to the more "traditional" types epidemiology (if there even is such a thing anymore).
There is benefit to a team with diverse backgrounds. Mathematical and statistical knowledge is vital, but so is understanding how hospitals and humans react to disease and interventions.
As for looking back at medicine in 200 years as blood letting and humours, I think that is to misunderstand medicine in its entirety. Modern medicine is evidence based where it can be, mixed with a high level of uncertainty and best application of basic science. This has predictive power, but is obviously limited due to the complexity of the system. A better analogy would be industrial era engineering vs modern day engineering: the same aim with similar attempts but much better understanding of the system at a higher level of resolution achieved by improvements in technology and base understanding.
If you look at the stuff coming out of Imperial and Oxford in recent weeks, there are a huge number of basic problems. Their papers can't be replicated for multiple reasons. They're using known-bad input data. They're not providing uncertainty bounds. There's no peer review. The Imperial paper assumed constant hospital capacity. Their papers reach diametrically opposite conclusions. They have a track record of model failure.
It's all practically a poster child for the replication crisis.
The /r/physics post criticises physicists who write some code to do basic curve fitting. Has he looked at the Oxford epidemiology paper? It is by all accounts literally curve fitting to the first 15 days of outbreak and reaches diametrically opposite conclusions to the Imperial paper.
It criticises people who write papers and then upload them to arXiv because reporters will find it and create panic. Would this person prefer people to use the Imperial/Oxford technique of sending papers directly to journalists, and entirely skipping the whole upload to arXiv step?
It blames physicists for creating "denial": "You become a punchline to a denier that says, they can't decide if there's going to be hundred thousand cases or a hundred million cases! Scientists don't know anything!" - guess what, they're saying that already and are going to say it a lot more because epidemiologists themselves keep contradicting and criticising each other for doing bad work, in public. Additionally, there are enough people with scientific literacy to understand the limits of statistics and modelling out there. They aren't as easily confused as this person rather seems to assume (does he have any data showing that people conflate epidemiology with physics?).
Right now I absolutely want to see papers written by physicists studying COVID-19. Why - because physics is a significantly more rigorous field than epidemiology. I trust the average physicist to have at least slightly higher standards, for instance I trust them to at least pretend to care about statistical uncertainty, and I suspect many of them will upload their source code. I don't expect any of them to email their paper straight to known-friendly newspapers.
Tbh I don’t disagree with quite a few of your points. A lot of papers out there atm are pretty poor quality. But having more similar papers won’t help matters. Wait for the high quality papers that will come from more valid high quality data and models that haven’t been rushed out. As for physicist have higher rigour, I’m sorry but that’s just arrogance. There are good scientists and bad scientists, and whether they studied physics or epidiemoology isn’t the point. Stop assuming that just because people studied a subject you like and are familiar with that they are better than the other group.
I agree with you on one level - the difference in rigour I perceive is a function of surrounding culture in a field, not the specific people who are in it. On the other hand if you look at the confidence levels required to publish something as a discovery, they're much higher in physics, partly that's fundamental to the field and partly it's that in physics scientists are OK with statements like "to make the next discovery we must spend 10 years building a billion dollar machine that will require international cooperation on a scale never seen before". Whereas in most other fields their ambition stops with collecting a bunch of grad students, or downloading data from sources that wasn't meant for the purpose to which it's put.
It seems like an Epidemiologist is most likely to be a doctor with some knowledge of statistics. On the contrary, I would have thought that a statistician/operations research person with domain knowledge of medicine would be a lot better suited to study epidemiology.
A lot of the existing models also seem really naïve for something an entire field depends on. Tradeoffs between human life lost due to the disease and effective human lives lost due to economic downturns seem entirely unstudied.
Czechia's actions of forcing people to using masks seems to have worked amazingly well for flattening out the curve. The "science" not seems to support it, and official recommendations are now moving back to recommending masks for all. The popular hypothesis for this, is that it stops nonsymptomatic carriers from being spreaders.
Now this sounds like a fairly obvious thing to realize, and to an extent, existing models should have caught this a lot earlier.
I grew up as a kid with an almost religious belief in Science. Over time, I have come to realize that a lot of our sciences are at best empirical and at worst entirely unverifiable. I am a 100% certain, that in a 100 years, we will look at medicine and nutrition of the 2000s, the way we look at humors and blood letting.