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

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.