I’m not disagreeing with you that their are confounding factors in both directions, this is clearly true. I’m disagreeing that at this moment - and with statis

I’m not disagreeing with you that their are confounding factors in both directions, this is clearly true. I’m disagreeing that at this moment - and with statistics like excess mortality in NYC, there is very good reason to believe that upward and downward pressure are not equally likely. As to the first assumption I disagree . We have pretty good ideas of all causes rates in somewhere like NYC, we have pretty good numbers on things like accidents etc. And then sort of factors you point out like the effect of a recession aren’t on effect yet for the most part. So you have a bunch of excess morbidity, and you have pretty good local statistics in the gaps, at least locally. You are right that this doesn’t go apples to apples in other places but we aren’t doing that so it does matter. There is one kind of interesting wrinkle, you may have stress related impacts that show up even on a short timeline. So there is a question if you can/should attribute those and you can pick reasonable ways, either choice . Flu isn’t likely going to be confounded too badly because people are going to be focussing in the counfounding impacts (e.g. of isolation). It just seems strange to me to assume this will be a problem , some people involved are good at this sort of analysis . Much more likely that a decent job will be done and the estimation errors will have wider bounds. I guess I just don’t see any data to support your pessimism , on that , but I could be wrong of course. I mean this sort of modelling isn’t easy but it is doable.