The # of cases reported that I believe they are comparing against is the number of confirmed positive tests, not the number of assumed cases in the county based on some other model that is trying to project # infected. I don't think anyone thought that we had 100% coverage where all cases were tested. So we're not "wrong" if such a study as this sees a gap between reported confirmed cases and expected infected, it just means we have a better understanding of the testing gap, which is self evident in existence but the magnitude of which we don't know.

Given all the other factors, an order of magnitude or more gap between tests and sick people doesn't seem completely out of the question. I would be curious if there are other models using a different methodology which could help us get a handle on the conditional probability chain leading to tests being done or not on an individual, to see if there is a similar set of conclusions.

It's impossible to believe. Currently, Santa Clara's crude CFR is 3.7%.

NYC has similar demographics (and as bad nursing home hits?) -- 0.14% of the population has died from covid. That puts an upper bound of 26x (and that's if the entire population was infected)

It’s not hard to believe when you consider testing criteria.