I’ve been working out math problems all day, trying to answer
some questions. I’m not satisfied with the results. I think I’m getting more
questions than answers. Some of this has to do with lack of accurate data—same problem
everyone else has right now.
I’ll share some of the data anyway, and then just ask the
questions.
This chart compares different populations and the number of
deaths attributed to Covid-19. All I’ve done is divide the actual number of
deaths by population in millions to find deaths per million people.
Place
|
Population
in millions
|
Covid-19 Deaths
as of April 30, 2020
|
Covid-19 deaths
Per million
|
Texas
|
30.54 M
|
722
|
23.64
|
Harris County
|
4.6 M
|
109
|
23.69
|
USA
|
328.88 M
|
58,356
|
177.44
|
World
|
7,597.78 M
|
233,014
|
30.67
|
Italy
|
60.46 M
|
27,967
|
462.57
|
New York State
|
19.44 M
|
18,274
|
940.0
|
New
York City
|
8.75 M
|
18,069
|
2,065.0
|
Sweden
|
10.1 M
|
2,586
|
256.0
|
Norway
|
5.4 M
|
210
|
38.89
|
There's a lot of difference between New York and pretty much everywhere else.
This is a snapshot. It doesn’t tell you the death rate of
Covid-19. For that you need to know how many people were exposed to and
contracted the disease. Then you divide that number by how many deaths.
So, in the absence of that data, everybody’s trying to make
guesses.
I took a good look at the video getting a lot of attention
this week of two ER doctors in Kern County, California. They’re making the case for opening up. And their non-numerical arguments for
that are persuasive—like how we lose immunity strength when we avoid all
exposure to germs. And that the costs to society at large—and to overall healthcare
and people’s well-being—because we are focusing on this single health issue are something we need to be talking about.
Dr. Dan Erickson (left) and Dr. Astin Massihi screenshot found here |
Drs. Dan Erickson and Astin Massihi are doing Covid-19 testing on everyone that comes in to their five ER centers, if I’m understanding them
correctly. Not just for possible Covid-19 cases, but everyone who comes in for
anything, which gives them their own raw data. Their numbers show that 6.5% of
those they test (several thousand) test positive. I’m unclear whether that
means people actually have it, people have antibodies because they had it, or a
total including both. Then they point out how many have been tested in California.
Dr. Ericksen says,
We have 33,865 Covid cases, out of a total of 280,900 total
tested. That’s 12% of Californians were positive for Covid.
Then they extrapolate that 12% to the population at large.
But that’s not accurate for a couple of reasons. Most tests, beyond their own
sample, have been done on probable cases, not a random sample. If we were randomly
testing populations, that information could be extrapolated to the
community the sample represents. But most of our testing has been done on people
with symptoms. That leaves out all people who are asymptomatic or have such mild
symptoms they don’t get a test—or don’t qualify to get tested, in many places.
Also, according to some, current tests tend to have a high false positive rate.
They use this extrapolation as “the best we have” right now with the lack of data. But, while their arguments are valid, and a conversation
worth having, we really can’t deduce death rates by this extrapolation. If you assume the sample covers everyone, then it looks like more have been infected than have been, and your death rate calculations will be far lower than reality—just as death rates appear far higher than reality when you only count verified cases. Too
many assumptions.
As they say, the more testing, the better. We just don’t
have the right sample sets yet.
We know the death rates are much lower than what the data
shows. But how much lower? Because, if it’s in the range of a typical flu year,
then we can let people take precautions and then take their chances—the same
way we do for the flu.
What we also don’t know is, what will the contraction and
death numbers be when we get through this season? By that I mean probably the
year. Flu seasons tend to go from fall through the following spring, not the
whole year. By the time it comes around again, it’s a different flu, which is
why there’s an annual—different from the past year—flu shot. So we can count annual
flu deaths and that data is relatively solid. Not all cases get reported, but
deaths caused by flu would be.
We don’t know season length of this virus. We don’t know if
it will mutate enough to make herd immunity a moot point or a proper goal. Either
way, we’re nowhere near the 60-80% required for herd immunity, meaning enough people
have had it that the virus has a hard time finding a new host.
Anyway, we don’t have a stopping point at which we can look
at the different approaches and say one worked better than another.
Among all the questions, I’d also like to know a few more
things:
·
If it’s true that urban areas are more likely to
be hit hard, how do you explain the discrepancy between Houston and New York
City? Houston’s shutdown was mid-March, close to New York’s. New York had a few
more cases by then, but not that explosive a difference.
·
Does it occur to anyone else that public
transportation has a lot to do with the spread of the virus? During the NYC
shutdown, they have kept their subways running. (My germ senses are making me
shudder.)
·
How much lower would the death data be if we
took nursing homes out of the data? Less than half? If so, why have we shut
down society instead of super-protecting nursing homes?
·
Sweden did some social distancing but no
shutdown. Norway, by comparison, did a full shutdown. There are differences,
but are they differences in timing only? How many inevitable cases have hit
Sweden already but will take longer to eventually hit Norway? Do shutdowns stop
cases from happening or simply postpone them?
Mark Ramsey, my SREC Chairman, compared Sweden and the USA graphically this week on Facebook, with this explanation:
Graph of the day. USA and Sweden. As identical as possible,
except for Y-axis, which is different mostly due to extreme difference in size
of the population, and increased testing in the US. Based on the SHAPE of the
two bar-graphs, has turning off a VAST PART of the US economy been
significantly better than Sweden, who simply had recommended practices and a
VERY FEW closures? Has it been "worth it"? The initial models were
very very wrong, and we now have millions of tests to calibrate the risk with.
The answer needs to change accordingly. (data from WorldoMeters.info at 13:00
CDT, 4-28-2020)
Comparison graphs of Sweden and USA from Mark Ramsey on Facebook |
Then there are some political questions. I hope this virus
hasn’t actually become political. Maybe it’s more of an urban/suburban/rural
difference—which, again, doesn’t explain Houston vs. New York. Is it the
prevalence of cars instead of mass transit in Houston? By the way, California’s
deaths per million is about 31. I didn’t have today’s data, so I didn’t include
California on the chart, above. But I’m guessing driving instead of mass transit
is a reasonable explanation there too. Also sunshine.
Facebook friend Leslie May has been looking at the politics and the data the
last couple of days. She said this yesterday on Facebook:
CHANCES OF CONTRACTING OR DYING OF COVID-19.... overall,
Democratic-governed states are more urban with an average population density of
123 people per square mile....
average population density of Republican-governed state: 71
people per square mile
for all Democratic-governed states, the average rate of
COVID-19 cases reported per 100,000 residents is 414
for R-governed states, the average is only 180 (less than
half)
the average mortality per 100,000 in D-governed states is 23
the average mortality per 100,000 in R-governed states is 7,
less than 1/3
by the way, Harris County, Texas, rates are 130 reported
cases per 100,000 residents, and 2 deaths per 100,000 residents, lower than the
average STATE....Texas as a state is 93 reported cases per 100,000 residents
with 3 deaths per 100,000 residents.
Today she adds a map and more questions and data:
Wondering why certain states have no stay home orders
(labeled 1 on the map below), others just "recommendations" (2 on the
map), and some STILL have them in place (3 on the map)? Here's what our country
looks like as with the "order" status / party of the governor
superimposed on the actual cases per 100,000 people in that state. Also looked
at where cases are increasing, decreasing or about the same.
7 states, all with R governors, are under NO stay home
orders: SD, ND, OK, WY, NE, IA, AR. SD, WY, OK are in the lowest category on
the map for number of cases/100,000. The others are, pardon the pun, "all
over the map."
4 states are under stay home recommendations, including TX.
The others are KY, UT and CT (kinda another outlier in the northeast, although
cases decreasing may explain it). 2 with D governors, 2 with R.
39 states are still under stay at home, 22 D governors, 17 R.
Oregon, Montana, Minnesota, NC, WV and ME are low in cases/100,000. Only WV's
governor is R, although if memory serves, I think that is a relatively recent
development.
In terms of increasing, decreasing or about the same number
of cases, I could see little rhyme or reason to how that influenced these
decisions -- for those in the middle with recommendations, TX and KY about the
same, UT increasing, CT decreasing.
Finally, Harris County, TX, has 6161 cases, or 134 cases
/100,000 residents, with 109 deaths or 2/100,000 -- about the same size geographically
as Rhode Island with 8247 cases, 778/100,000 people, 251 deaths, or 24/100,000
residents. RI has twice the death rate of Harris County per capita.
Graphic from Leslie Joan May, from Facebook |
Texas starts opening up tomorrow. There’s a mixture of “hurray”s
and “no no no”s. I lean toward the hurrays. But I’m not rushing to a theater
any time soon. And I’m still wearing a mask at the grocery store, and carrying
hand santizer, wipes, and gloves for use as needed.
If we can be sensible while getting people back to work, I
think that’s a win.
I don’t think it was ever our intention to hold everyone
housebound until there was no more risk. We were told 2-3 weeks, and maybe a
little longer (it’s been 4 weeks tacked on to the original 3 already) to “flatten
the curve.” That never meant fewer people would contract the illness; it meant
fewer would get it at the same time. The number that would eventually get it
was expected to remain the same. See my question about Sweden and Norway above. So my question is, what's the rationale for making it take longer but not limiting the actual number who get it? Is that goal, whatever it is, worth economic collapse, with associated famine, hunger, poverty, and hopelessness?
One advantage of time has been the possibility of finding treatments
that work. The new drug remdesivir was announced this week as a successful treatment. Because it’s new, it will be
more expensive than hydroxycholoquine plus zinc plus Z-pac (one meme I saw
showed $1,000 instead of $20). Still, it’s great news. Enough for a stock market bump upward.
Others treatments are coming. Even a vaccine may be coming
soon, which would be in record time. So this could mean that the pause was of
some value beyond guaranteeing our hospitals wouldn’t be overwhelmed, which it
turned out was not the case even in NYC.
This morning Ben Shapiro talked about a new data piece on
his show (haven’t tracked it down elsewhere yet) that there’s not a single case
in which a child has spread the illness to an adult. I don’t know how they know
that, but that’s great news. If true, then schools and play dates will start
looking safe again. And maybe we’re in time for summer sports leagues.
At the very least, I think we need an absolute rational
explanation for every infringement on our freedom from this point forward. “For
your safety” won’t do. Neither will “The public can’t be trusted.” We need
something like, “Here's the data that shows this (X requirement) limits spread of the
disease by (X)%, and it is imperative that you not spread this disease in (X location)
before (X time).”
Failure to provide that information might just lead us to
believe that fear and control are a goal when what we really want are freedom
and innovation.