A few comments on how to interpret this preprint on the front page (!) of the @nytimes. During a period of exponential growth, early intervention can have a big impact. This is well known. But it is extraordinarily difficult to quantify the magnitude of this counterfactual... 1/5
The group from Columbia's model works by estimating the impact of movement restrictions on R, and then shifting this either 1 or 2 weeks earlier. R dramatically drops around the time of restrictions, so this yields a big change to the dynamics of the subsequent outbreak. 2/5
But the drop in R has been observed to occur earlier than policies are put in place. People watch the news and change their behavior voluntarily. Hence my quote in @nytimes: “Do people need to hear the sirens for them to stay home?” Is it valid to shift R back a week or two? 3/5
Furthermore, modeling requires location-specific estimates of R and reporting rates, but these things are hard to separate. Consider the situation in early March in NYC. The sharp increase in cases reflects both growth in the epidemic and growth in testing. 4/5
In my opinion, these models are best equipped to help us draw qualitative conclusions, not quantitative estimates of how many deaths would have been prevented by earlier intervention. The answer is many, but there is otherwise too much uncertainty. 5/5
Adding my excerpted quote from this story: 
