[BostonCard]

I have a more fundamental question that pertains to this and other topics:  why are some people so vested in the belief that (a) the virus did not reach the US until late January/February versus earlier, and/or (b) the virus must have started in late 2019?  Who cares at this point?


Is it because if it's been around longer than originally thought, then the spread modeling being used now is inaccurate?  I guess I "get' that, but again, as a layman my big concern is ability of hospitals to be able to treat those that need it.  Can't you just say "we are at capacity, thus we shouldn't pull back on restrictions," or "we have excess beds and equipment and can handle more cases," which militates in favor of loosening to some extent?



Anyway, I find it interesting that people tweet their absolute certainty that this didn't arrive in US till February; or alternatively, those insisting that the virus was here earlier than initially believed.  I don't "get" why it is such a big deal either way.

The challenge we have in fighting this virus is that any measure you take is about one to two weeks behind the virus.  So, if you wait until hospitals are nearing capacity to intervene, then by the time your intervention starts having an effect, hospitals will be overwhelmed.  The opposite is also true; if you wait for infections to reach a certain level before loosening restrictions, you will probably have waited a bit too long and unnecessarily taken an economic hit (this is partly why I'm interested in the data from Spain, who loosened restrictions a couple days ago, despite still being pretty hard hitth).  So this is why the epidemiological models are really useful.  But the models are only as good as the data that goes into them.

The bottom line is that it makes a very big difference if the proportion of Californians exposed to COVID-19 is .06% based on confirmed diagnoses, 0.2% (based on the number of deaths and an assumed CFR of 1%), 2% based on generous assumptions about untested asymptomatic and lightly symptomatic patients, or 10% (if you assume the virus has been around a while).  The likelihood of a spike is very much dependent on the proportion of unexposed individuals; the higher that number, the lower the likelihood of a second wave.

BC

[BostonCard]

I have a more fundamental question that pertains to this and other topics:  why are some people so vested in the belief that (a) the virus did not reach the US until late January/February versus earlier, and/or (b) the virus must have started in late 2019?  Who cares at this point?


Is it because if it's been around longer than originally thought, then the spread modeling being used now is inaccurate?  I guess I "get' that, but again, as a layman my big concern is ability of hospitals to be able to treat those that need it.  Can't you just say "we are at capacity, thus we shouldn't pull back on restrictions," or "we have excess beds and equipment and can handle more cases," which militates in favor of loosening to some extent?



Anyway, I find it interesting that people tweet their absolute certainty that this didn't arrive in US till February; or alternatively, those insisting that the virus was here earlier than initially believed.  I don't "get" why it is such a big deal either way.

The challenge we have in fighting this virus is that any measure you take is about one to two weeks behind the virus.  So, if you wait until hospitals are nearing capacity to intervene, then by the time your intervention starts having an effect, hospitals will be overwhelmed.  The opposite is also true; if you wait for infections to reach a certain level before loosening restrictions, you will probably have waited a bit too long and unnecessarily taken an economic hit (this is partly why I'm interested in the data from Spain, who loosened restrictions a couple days ago, despite still being pretty hard hitth).  So this is why the epidemiological models are really useful.  But the models are only as good as the data that goes into them.

The bottom line is that it makes a very big difference if the proportion of Californians exposed to COVID-19 is .06% based on confirmed diagnoses, 0.2% (based on the number of deaths and an assumed CFR of 1%), 2% based on generous assumptions about untested asymptomatic and lightly symptomatic patients, or 10% (if you assume the virus has been around a while).  The likelihood of a spike is very much dependent on the proportion of unexposed individuals; the higher that number, the lower the likelihood of a second wave.

BC