[noelsusman]

You're correct to focus on the effect of parameter choices over code quality. It's been a little funny to watch a bunch of software engineers freak out about unit tests while ignoring everything else that has a much larger impact on the output of the model. I would bet large sums of money that this code is producing the correct output according to the model/parameter specifications.

All I can say is welcome to epidemiology. The spread of a disease is highly dependent on a host of factors that we have very little insight into. Even simple things like hospitalization rate or fatality rate can be difficult if not impossible to estimate accurately. Epidemiologists are open about this, but few people ever want to listen. Humans just aren't good at truly conceptualizing uncertainty.

The theory behind disease spread models is relatively sound, but they're highly dependent on accurate estimates of input parameters, and governments have not prioritized devoting resources toward improving those estimates. I sat in on discussions between epidemiologists and government officials about COVID models. The response to nearly every question was "we don't know, but here's our best guess". I listened to them beg officials for random testing of the population to improve their parameter estimates. That testing never happened.

[thu2111]

I would bet large sums of money that this code is producing the correct output according to the model/parameter specifications.

I'll take that money off you then.

The code has various memory safety bugs in it and originally had a typo in a random number generator constant. Amongst other problems.

There's really no reason to believe it produces correct outputs, in fact, we know it didn't and probably still doesn't given how it was written.