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They're useful tools to compare potential scenarios, but since we're dealing with exponentials, tiny changes can have huge effects on outcomes. Here's one example where I start with 1k cases and project 2 weeks out based on different doubling times - e.g. diff 1.5 vs 2.0! 2/npic.twitter.com/WDZM35pD3d
So small differences in (assumed) doubling times will have massive effects on projected outcome numbers. This is very important for #COVID19 since most (undersampled) places start out at ~1.5-2.0, then 2.5, 3.5, etc. before averaging at 7+ days prior to plateau and decline.
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... and here I'm just doing a straight up regression with a single variable (doubling time) - real models are much more complex and have many more variables, most of which are uncertain at this time. YET, e.g., @MRC_Outbreak have managed to provide many very useful models!
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Note - I'm in no way a modeler, so take all of what I just said with that in mind - including if you double-check my calculations above... 
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