[FRIAM] off-label technologies, exaptatiion and exponential technological growth.

[Marcus Daniels]

Consider deaths in Louisiana (20) vs. Texas (90).   Both states with lots of obesity and similar weather.  

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From: Friam <friam-bounces at redfish.com> On Behalf Of u?l? ?>$
Sent: Wednesday, August 11, 2021 10:52 AM
To: friam at redfish.com
Subject: Re: [FRIAM] off-label technologies, exaptatiion and exponential technological growth.

It would be interesting to plot some geographical data about comorbidities, particularly obesity.

On 8/11/21 9:09 AM, Marcus Daniels wrote:
> It is weird there are orders of magnitude of variability.   I wonder if it is differences in spatial distribution of the different vaccines?   Ethnicity?   Prevalence?
> -----Original Message-----
> From: Friam <friam-bounces at redfish.com> On Behalf Of u?l? ?>$
> Sent: Wednesday, August 11, 2021 8:06 AM
> To: friam at redfish.com
> Subject: Re: [FRIAM] off-label technologies, exaptatiion and exponential technological growth.
> 
> Attached.
> 
> Missing Arkansas, Connecticut, Florida, Hawaii, Iowa, Kansas, Maryland, Missouri, New York, Pennsylvania, West Virginia, Wyoming.
> 
> On 8/10/21 4:43 PM, David Eric Smith wrote:
>> I am sure it is just dieseling at this point, but I was pleased to see the following article:
>> https://www.nytimes.com/interactive/2021/08/10/us/covid-breakthrough-
>> i
>> nfections-vaccines.html
>> <https://www.nytimes.com/interactive/2021/08/10/us/covid-breakthrough
>> - infections-vaccines.html> (I usually get to these things late; 
>> y’all probably have read it already)
>>
>> In reading the first table, on hospitalization and death fractions by 
>> vax/unvax, I was thinking “okay, now since we have vaccinated 
>> fractions by date, we could do a covariance plot, and of course could 
>> then do more involved multiple regressions on dummy variables as we 
>> could find them.”  (No pun meant on “dummy variable”, though I am 
>> unable to miss it myself.  Things like measures of hospital 
>> performance, coverage of masking rules or other public health 
>> measures, population density and gathering density, etc.  Some of 
>> these to be proxies for fraction exposed, which is hard to get at.)
>>
>> But then that is just where the article goes.  It’s funny how a pair made of a careful writer and a lazy reader can be an unhelpful combination.  The text leading to the second table says "people who were not fully vaccinated were hospitalized with Covid-19 at least five times more often than fully vaccinated people, according to the analysis, and they died at least eight times more often.”  I remember the nice passage in John Paulos’s book “Innumeracy”, where (to make some point, which I now forget), he comments on why a sign over the highway “Entering New York, Population at least 6” is not particularly informative, though quite true.
>>
>> Look then at the distribution of multipliers in the table.  For the “at least five times” column, the first six entries, alphabetically, are 75x, 17x, 47x, 68x, 22, 148x, 161x, and likewise for the “eight times” column. Ahh, if the American Public would only tolerate being shown a histogram giving the whole distribution at a glance….  Of course, if I were not lazy, I could find and download the data and make my own histogram.
>>
>> But, credit to those authors.  Within the bounds of what is permitted to them, this is a useful data digest.
>>
>> Eric
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