2) Biological data is often a nightmare to work with. Estimates about behavior too. Getting something within an order of magnitude is often not too shabby.

3) Errors ('up to') are sensitive.

Here's a toy example. Suppose you think two numbers are each around 5, but the data are consistent with anywhere between 0-10. The sum of these numbers must be between 0-20 (low case: 0 + 0 = 0, high: 10 + 10 = 20), and their product between 0-100 (0 x 0 = 0; 10 x 10 = 100).

More data comes in and you can estimate each value more precisely: now you know they're somewhere between 4-6. You know the sum is actually between 8-12, and the product between 16-36. That's a massive decrease in the upper bound (64 percent for the product!) but literally nothing has changed except for the increased precision.

The COVID models have exactly this problem--none of the parameters are known exactly--and the outcome is some function of combining them. Moreoever, we're learning more about what factors matter AND how to fight the virus.