I’m a member of the U of FL software team that is building a low-cost (i.e., under $200) ventilator for Third World countries. It took six of us working around the clock for over a month to get the software working for the ventilator. Only this week is it ready for FDA testing and approval. Anyone who thinks they can design, code, test, and debug a significant piece of software in less than a weekend is kidding themselves.
That is a very important point. Too many college students and other inexperienced developers do not appreciate the complexity of software which does anything significant. They learn to throw together demo level programs largely from existing code and think that they are done since it worked once, or at least it looked like it did.
In the case of the epidemic models many of the modelers fell for the trap of writing a bunch of equations for which you really don't know any of the correct values for the constant terms. You get an output, and if you pick the right values for the parameters it would in fact model what is actually happening in the real world. But you have no idea what the parameters really are, so your model is just a guess.
You can see that in the confidence intervals of the models that have been published. They are so wide that nearly any result falls in their error band.
They are no better than a weather prediction model that says tomorrow's temperature has a greater than 90% chance of being between -30 and + 110 degrees. It is right nearly always, but doesn't tell you much.
Anyone who thinks they can design, code, test, and debug a significant piece of software in less than a weekend is kidding themselves.
If from scratch, I agree with you. It depends, though, on what you have on which to build the model code. For example, if they had an accurate model of SARS, then it's a matter of fitting the coefficients to the actual numbers you have. Emphasis on the word "accurate."
One step that's not mentioned is how the kids TESTED the accuracy of their model to data to date. Also I'd call into question the accuracy of said data (inaccurate based on reporters says COVID-19 when it may not be, or may not have been the principal cause of death) both as the model was being written, and during subsequent tracking of prediction over reported data.
Questions the reporter did not ask. Must have flunked Statistics 201.