> If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work.
No. If you miss a constant percentage of cases, you get the same shape of exponential curve.
That is, a virus with an R0 of 2 and 1 case doubling to 2 and 2 cases doubling to 4.... looks the same when you miss 99% of infections and see 1 of 100 infections doubling to 2 of 200 doubling to 4 of 400.
This fallacy has been common in the response to this data, but it makes no sense. Large numbers of missed cases shift the curve forward and backward in time, and don't change the shape of it.
All of the current findings are still consistent with R0 in the range of 2.0 to 2.5.
We are talking about 1 serological study at a single point in time. We have no idea what percentage of cases we were missing before or after that point.
There's no reason to interpret the serological study to think a higher R0 is implied, which is what you said.
The only way that missing cases affects estimates of R0 from time series is if we're missing a much bigger proportion of cases now than we were in the past. All the evidence I've seen leans the other way-- more testing, better testing, more cases diagnosed by "presumed" criteria.
So, it actually implies the opposite of what you're saying, and argues for lower R0.
I am not interpreting it that way, I believe in the consensus estimates. The parent commenter (articbull) thinks these serological studies (SC, LA, and to a lesser degree NY) prove that the IFR is much lower than we thought because the actual infection numbers are much higher. The NY study, using reasonable assumptions, looks like the IFR might be between 0.5% - 1.0%, but the SC study was claiming 0.1-0.2%. And I am arguing, in the hypothetical case that the SC study is correct (I think it is flawed), then we have under estimated R0.
Considering we have been under lockdown for over a month (which greatly impacts effective transmission), and considering our estimates of latency hasn't changed dramatically, I think my above assumption is fair.
> And I am arguing, in the hypothetical case that the SC study is correct (I think it is flawed), then we have under estimated R0.
I don't necessarily believe the Santa Clara County study, but there are plenty of other alternatives, including a pretty likely one: gross undercounting at the start of the epidemic and/or earlier cryptic spread in communities. This is something that SCC public health officials / Dr. Sara Cody has stated in recent days is her belief-- I am not sure I agree.
At this point we're accumulating a lot of evidence IFR is 0.3-0.5%.
No. If you miss a constant percentage of cases, you get the same shape of exponential curve.
That is, a virus with an R0 of 2 and 1 case doubling to 2 and 2 cases doubling to 4.... looks the same when you miss 99% of infections and see 1 of 100 infections doubling to 2 of 200 doubling to 4 of 400.
This fallacy has been common in the response to this data, but it makes no sense. Large numbers of missed cases shift the curve forward and backward in time, and don't change the shape of it.
All of the current findings are still consistent with R0 in the range of 2.0 to 2.5.