Speaking to your point, check out NY state hospitalization by individual region. The trend is clear: If you had a big event in spring 2020, your winter 2020 was muted. If you had a small event in spring, you got hit hard in the winter.
Also, speaking to Washington state: bloodwork shows 2.1% of donations had antibodies -- in Mid-December 2019, long before we did anything at all to slow the spread (and before testing). It's extremely likely the virus came through in flu season and we didn't even notice because old and/or sick people die all the time during flu season.
I started down a path of looking at how a county's deaths ranked within a state from 2020-April 15 2021 vs. how that county ranked within the state from April 15 2021- present (date arbitrary to try to seperate "waves"). In NY, I do see that the counties that had the most deaths in wave 1 subsequently rank pretty low (fewest deaths) in wave 2. When I looked across the country, however, I could not find a strong signal. I created scatter plots with the axes being rank within a state for waves 1 & 2. General negative correlation was not obvious... instead I mostly saw what appeared to be completely random scatter.
I'm in Marin County, CA, and it does look like cases track temperature, which should be related to humidity (and I haven't found an absolute humidity history).
Fun. California is a bit tricky - if you removed LA County (27473/76122 "Covid deaths" and 10.04/39.51 population vs CA) it might resemble WA and OR; but it also depends on how fast people were already dying in terms of the math being used here. It's just funny that all of the West Coast seemed to have a bit of pre-existing resistance to the virus except LA (and inland CA).
Presumably what this measures, besides rate of spread due to urban centers, is overlap or mismatch between comorbidities. Most comorbidities for SC2 should also apply for "general dying," so sick and healthy regions can be adjusted and compared safely. But some regions might have a high "general dying" comorbidity that doesn't map to SC2. Obvious example is the developing world - lots of general dying risk factors that aren't SC2.
The biggest mismatch in the other direction might be that urban pollution is significantly more deadly for SC2 than for general dying (a good explanation for LA and inland CA as well).
I like boiling down the stats to the size of 250 to approximate Dunbars number. I did that for Israel and Florida. The difference at that size was Israel had saved .5 more life than Florida. Now consider Florida's economy...and its like...sheesh...all the fuss...all the debate. Maybe I'm over simplifying and of course every life lost is a tragedy but has it all been worth it?
There are several alleged "co-morbidities," conditions that seem related to covid mortality. I wonder if there are reliable stats on prevalence of those conditions by state. One might assume states with higher obesity rates, for example, would show higher deaths. But they might have higher death rates without covid. So it might need some normalizing.
Speaking to your point, check out NY state hospitalization by individual region. The trend is clear: If you had a big event in spring 2020, your winter 2020 was muted. If you had a small event in spring, you got hit hard in the winter.
https://coronavirus.health.ny.gov/daily-hospitalization-summary
Also, speaking to Washington state: bloodwork shows 2.1% of donations had antibodies -- in Mid-December 2019, long before we did anything at all to slow the spread (and before testing). It's extremely likely the virus came through in flu season and we didn't even notice because old and/or sick people die all the time during flu season.
I started down a path of looking at how a county's deaths ranked within a state from 2020-April 15 2021 vs. how that county ranked within the state from April 15 2021- present (date arbitrary to try to seperate "waves"). In NY, I do see that the counties that had the most deaths in wave 1 subsequently rank pretty low (fewest deaths) in wave 2. When I looked across the country, however, I could not find a strong signal. I created scatter plots with the axes being rank within a state for waves 1 & 2. General negative correlation was not obvious... instead I mostly saw what appeared to be completely random scatter.
There's evidence that severity track absolute humidity... https://www.zerohedge.com/covid-19/theory-why-covid-cases-started-skyrocketing-central-europe-last-month
I'm in Marin County, CA, and it does look like cases track temperature, which should be related to humidity (and I haven't found an absolute humidity history).
If you check cases at https://coronavirus.marinhhs.org/surveillance versus temperature in 2020 at https://weatherspark.com/h/y/529/2020/Historical-Weather-during-2020-in-Marin-City-California-United-States#Figures-Summary it seems to track peak temps.
We've had a very mild winter so far, just started getting colder recently, and now cases are climbing vs weather at https://weatherspark.com/h/y/529/2021/Historical-Weather-during-2021-in-Marin-City-California-United-States#Figures-Temperature
Of course Omicron could also be a factor, but cases do seem to follow the temps with just a visual check.
Fun. California is a bit tricky - if you removed LA County (27473/76122 "Covid deaths" and 10.04/39.51 population vs CA) it might resemble WA and OR; but it also depends on how fast people were already dying in terms of the math being used here. It's just funny that all of the West Coast seemed to have a bit of pre-existing resistance to the virus except LA (and inland CA).
Presumably what this measures, besides rate of spread due to urban centers, is overlap or mismatch between comorbidities. Most comorbidities for SC2 should also apply for "general dying," so sick and healthy regions can be adjusted and compared safely. But some regions might have a high "general dying" comorbidity that doesn't map to SC2. Obvious example is the developing world - lots of general dying risk factors that aren't SC2.
The biggest mismatch in the other direction might be that urban pollution is significantly more deadly for SC2 than for general dying (a good explanation for LA and inland CA as well).
I like boiling down the stats to the size of 250 to approximate Dunbars number. I did that for Israel and Florida. The difference at that size was Israel had saved .5 more life than Florida. Now consider Florida's economy...and its like...sheesh...all the fuss...all the debate. Maybe I'm over simplifying and of course every life lost is a tragedy but has it all been worth it?
There are several alleged "co-morbidities," conditions that seem related to covid mortality. I wonder if there are reliable stats on prevalence of those conditions by state. One might assume states with higher obesity rates, for example, would show higher deaths. But they might have higher death rates without covid. So it might need some normalizing.