Why it’s so hard to make a good Covid-19 model

The article is a long listing of ways to say GIGO. It's hard to get good clean accurate data.

What about our models for global warming?

Correct. Non-linear dynamic multivariate systems are very difficult to model and uncertainty is a function of time. That's why we can't predict the weather more than a few days out with any degree of accuracy.

Those two things are unrelated.

Unlike a virus, the laws of physics don’t appear to mutate.

Also, you can’t measure the quality of your model of virus progression with earth observation satellites or hundreds of thousands of years worth of ice core data.

Also, rather unfortunately in this case, the lack of meaningful action by world governments means that none of the climate models have to account for feedback caused by the world actually acting on the results of those the models.

What this article misses is that simple models of complex systems in science are most useful for understanding the dynamics of phenomena, not for making accurate quantitative predictions. This is not a model of a mass accelerating in a vacuum where Newton's laws are sufficient to a high degree of accuracy, or even a numerical model of the aerodynamics of an airplane, where the physics are well understood but there are far too many particles to solve analytically. This is a parameterized model for the behavior of millions of people, each one's reaction to exposure to viral particles, legal and social norms, personal and economic situations, and so on. Of course we are not going to be able to predict the future of an unprecedented event like this quantitatively. It's not just that data are hard to obtain accurately; the models themselves are so simplified that they can't capture much of the important dynamics going on.