Help the Fight Against COVID-19

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Perhaps some small reasons for optimism:

https://www.dailywire.com/news/oxford-epidemiologist-heres-why-that-doomsday-model-is-likely-way-off

https://www.dailywire.com/news/epid...admits-he-was-wrong-drastically-revises-model

Punch line: it's possible this had spread more widely earlier than was known and that we might therefore be at a later stage of the pandemic than we presume. Changes nothing about present circumstances of course.
The data doesn't support that. The growth rate has not changed. If that were true we'd see the growth rate slowing, but it's not.
 
Perhaps some small reasons for optimism:

https://www.dailywire.com/news/oxford-epidemiologist-heres-why-that-doomsday-model-is-likely-way-off

https://www.dailywire.com/news/epid...admits-he-was-wrong-drastically-revises-model

Punch line: it's possible this had spread more widely earlier than was known and that we might therefore be at a later stage of the pandemic than we presume. Changes nothing about present circumstances of course.
I kinda buy that.

We weren't severe, and thus were never tested. But what my family had back in late February had all the classic COVID-19 symptoms and ran its course just like a classic case of COVID-19.

There is only one reason to seriously doubt that that's what we had; namely, that the first case of it in my family began 3 days earlier than the first confirmed case in my state. Prior to that, presumably, nobody had it in my state. If this presumption were true, there was no-one around to infect us; and therefore, what we had wasn't SARS-CoV-2 after all. It was just a really weird flu that perfectly matched the coronavirus pattern.

On the other hand, if the presumption was false, and COVID-19 was already community-spreading in my state long before anyone was aware, then the timeline makes more sense.

But in that case, it's very probable that the "heavy flu season" the U.S. just went through was actually a "flu season plus some COVID-19 not previously identified as COVID-19" season.

Here's another somewhat optimistic piece, arguing that we've been radically overstating the lethality of the disease in the U.S.:
https://mises.org/wire/uss-covid-19-death-rate-far-below-rates-italy-and-spain

(But, caveat emptor: Once again we're comparing numbers between different countries whose processes for measuring those numbers are so wildly different as to make comparisons nearly nonsensical.)
 
People have also died from taking them.
People you’ve read about died from eating aquarium cleaner by the spoonful. You’ve been misled by the media. They’ve since edited the headlines and the articles. Go see for yourself.

Meanwhile, the dude from the Imperial College of London whose team was predicting “500k” deaths in the U.K. alone is walking back his predictions and now saying about 20k people will die (possibly a lot fewer), half of whom would die anyway of other diseases: . Twitter thread links to the article.
 
The data doesn't support that. The growth rate has not changed. If that were true we'd see the growth rate slowing, but it's not.
@FractalAudio, has anyone yet devised a way to tell the difference between...
(a.) increase in the number of known cases because more people are infected; and,
(b.) increase in the number of known cases because more people are being tested
...?

I ask because I'm having trouble figuring out how reliable these reported increases in "the number of known cases" actually are, when we try to use them as a proxy for actual spread of the virus.

The scenario I keep running through my head goes like this:

It's Monday morning in Whoville, and medical professionals would like to test sick Whos for coronavirus. But there's limited testing available, so all the testing is rationed: Only the very ill get tested. (Occasionally, once the very-ill person tests positive, other people in the same house will also be tested, but not always. If they other folks are already symptomatic, they're merely presumed to have it, to save resources.)

As a result of testing performed prior to end-of-business on Monday, the numbers released on Tuesday morning show 250 active cases of COVID-19 in Whoville.

At noon on Tuesday, a lot of testing gear and extra personnel show up, so that there is suddenly an excess capacity for testing. Different groups of health professionals respond in different ways:
Group 1, "the randomizers": One group goes out and starts working to conduct entirely-random testing of the Whoville population. They test 100 of the Whos by 5pm, and confirm that 22 of them have COVID-19. Some of these 22 people are asymptomatic, some have a sniffle, some are very sick and feverish, and one is about to be hospitalized
Group 2, "the symptomizers": Another group goes out and randomly tests only persons who have symptoms. They find 50 persons with symptoms by 5pm, and test them, resulting in 35 test-positives.
Group 3, "the hospitalizers": Finally, there's the group that tests people who show up at the E.R. requiring hospitalization. 14 of them show up on Tuesday with symptoms that might be COVID-19. They're tested, and 9 of them have COVID-19; the other 5 have other problems.

All groups are careful not to double-count someone who's already been tested by another group.

On Tuesday night, they tally up the figures (the test-positives provided by all 3 groups), and on Wednesday morning they report that the number of known cases of COVID-19 in Whoville has grown by 66 (22 + 35 + 9) cases, from 250 on Tuesday to 316 on Wednesday.

Whoville's most popular morning-show, Good Morning Whoville, has a Pretty Blonde Who With Blinding Lip-Gloss, who reports this as a 26.4% daily increase in COVID-19 infections.

But that reporting's complete nonsense, isn't it?

If they hadn't stopped rationing testing, it would have been only reported as an increase of 9 cases, which is only a 3.6% increase! Big difference from 26.4!

But then, on Wednesday, Group 1 ("the randomizers") goes out and finds a new sample-set of 100 people to test. Unlike the set on Tuesday, the 100 people tested on Wednesday produce 24 positive tests.

Group 1 also goes out on Thursday, and finds yet another 100 people to test. This time, they get 26 positive tests. Looking at their numbers across 3 different days (22, 24, 26) Group 1 announces that, in their opinion, the actual COVID-19 daily growth rate is between 8% and 9%.

Which group is right?

I think Group 1 is right, because they're using a statistically valid methodology. But is anyone in the U.S. doing it that way?

Group 2 is using a methodology that helps us identify people who need to self-quarantine. That's good practical information, but it's unhelpful for guessing the spread of infection.

And the folks in the hospitals (Group 3) ...what useful, actionable information does their testing give us? As far as I can tell, its sole usefulness is to put all the COVID-19 people in a separate hospital wing, and to tell the nurses to garb up, so that they don't infect others. Or, if there's a known treatment that's good for COVID-19 but useless for other diseases, it'll indicate for which patients that treatment should be used. But it's only distantly, indirectly related to the question of how fast the infection is spreading.

So I'm not saying all these groups shouldn't be doing their tests. Each group's approach has its uses.

But what I fear is that the publicly announced numbers are produced by mixing numbers from all 3 groups!

The result is a big bowl of hen's teeth, snake-legs, and unicorn flatulence, and that's what's being reported.
 
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I think Group 1 is right, because they're using a statistically valid methodology. But is anyone in the U.S. doing it that way?

Yes, apparently the rich folk in Telluride want to test everyone in San Miguel County, CO as they have no hospital. It is a great experiment but still likely still early on.

"San Miguel County is still figuring out exactly how to get the tests to 8,000 people across 1,300 square miles. Ideally, residents would get voluntarily tested twice, two weeks apart, which doubles the logistical challenge. "

https://www.theatlantic.com/science/archive/2020/03/coronavirus-tests-everyone-tiny-colorado-county/608590:
 
https://www.nature.com/articles/s41591-020-0820-9

Perhaps. It's worthwhile to note that Nature (Inc.) is basically owned by the CCP.

Nature is one of the most well respected science publications in the world. I'm not sure who owns them, but they're certainly not some propaganda machine (unless you don't believe in science) Their editorial staff is top notch, and any scientist/researcher who publishes in Science is opening themselves up to severe scrutiny and peer review. Experts don't want to publish data/analysis with holes, as it can embarrass them or ruin their careers if its later proven to be lousy research. I'm expecting there will be many follow-ups on this research, and yes, it's likely that in future hindsight we'll have more accurate analysis. But for the time being, their findings seems to provide the best hypothesis for the viruses origins.

Often, we can't know things with 100% certainty / black-and-white terms. It's our job to analyze each possible side of a given issue and determine how much uncertainty comes with each interpretation. In this case, I would say, I'm 95% convinced now that this was not a man-made virus. Before reading this article, I was probably only 80% convinced. On the flip side of the coin, the conspiracy theories out there are sure entertaining (I actually love conspiracy stories in books, tv and film... one of my favorite sub genres) But, in the real world, conspiracy theories are usually only based on "something that sounds fishy", combined with a lot of speculation and faith... I am all for critical thinking and questioning authority, but I also believe we should listen to the consensus of experts in given fields, and require strong factual foundations for claims that may cause harm to people.
 
Maybe of interest: I grabbed the Hopkins raw data from Github and plotted on a logarithmic scale.
I'm no expert on epidemics but the rate of growth at the beginning (slope) seems much higher than it is now, which I find reassuring.
Eyeballed: One order of magnitude every 5 days in the beginning, now maybe every 20 days.
covid.png
x axis: days (1 = Jan 22)
y axis: confirmed global cases.
Source: (note, this is the "frozen" git SHA-tag I used. There will be newer versions)

Thanks to everybody who is staying at home...
 
Maybe of interest: I grabbed the Hopkins raw data from Github and plotted on a logarithmic scale.
I'm no expert on epidemics but the rate of growth at the beginning (slope) seems much higher than it is now, which I find reassuring.
Eyeballed: One order of magnitude every 5 days in the beginning, now maybe every 20 days.
View attachment 65475
x axis: days (1 = Jan 22)
y axis: confirmed global cases.
Source: (note, this is the "frozen" git SHA-tag I used. There will be newer versions)

Thanks to everybody who is staying at home...

That might point to that lockdowns and distancing are making a difference in countries now having rapid surges. But any world aggregate is not ideal to do analysis or extrapolations on.

From a data science perspective, and probably epidemiologically, it is much better to analyze locally but country or even better by metropolitan area. Case in point: Germany vs Italy vs USA; NY vs CA vs WA. Also analyses per capita (e.g. per 1000) can give different results and insights than raw counts.
 
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Here's another somewhat optimistic piece, arguing that we've been radically overstating the lethality of the disease in the U.S.:
https://mises.org/wire/uss-covid-19-death-rate-far-below-rates-italy-and-spain

This one I don't buy. They're spreading number of deaths across total population which might be valid in hindsight but only if the total number of deaths attributable to the virus were accurate across different nations. In real time they're comparing countries with higher rates of testing and earlier spread of the virus to the US despite the fact that we're farther back on the curve. I'm disappointed,, Mises is usually a lot better than that.
 
Maybe of interest: I grabbed the Hopkins raw data from Github and plotted on a logarithmic scale.
I'm no expert on epidemics but the rate of growth at the beginning (slope) seems much higher than it is now, which I find reassuring.
Eyeballed: One order of magnitude every 5 days in the beginning, now maybe every 20 days.
View attachment 65475
x axis: days (1 = Jan 22)
y axis: confirmed global cases.
Source: (note, this is the "frozen" git SHA-tag I used. There will be newer versions)

Thanks to everybody who is staying at home...

I may be sounding like a broken record, as I've mentioned this a few times, but the analysis of the "total cases" and the associated curve does not provide very high quality information for analyzing Covid-19 growth. It is too strongly correlated with testing capacity.... (ie: much of the total cases curve / slope change / numbers will be related just to how many tests are administered and what threshold of severity of cases are tested, which has changed over the time axis of the chart) .. another way of visualizing this is that the further back in time you look on the curve, the higher the ratio of "unreported cases" there was in the data. So with "Total Cases" curve, your not really comparing apples-to-apples.

It is more valuable to view the deaths curve or critical/serious cases curve, if trying to glean meaning of current trends for the virus, and how our actions are affecting the severity of the virus. Here's the latest data from WorldOMeters drill down data:

The most recent doubling period of critical cases is the past 5 days.

covid19-serious-critical-cases-2020-03-26.jpg
 
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Perhaps some small reasons for optimism:

https://www.dailywire.com/news/oxford-epidemiologist-heres-why-that-doomsday-model-is-likely-way-off

https://www.dailywire.com/news/epid...admits-he-was-wrong-drastically-revises-model

Punch line: it's possible this had spread more widely earlier than was known and that we might therefore be at a later stage of the pandemic than we presume. Changes nothing about present circumstances of course.
I do not understand the reasoning behind the Oxford study. Here's a link I found of it (Google search results really suck these days, brings up links to useless articles and no study): https://www.dropbox.com/s/oxmu2rwsnhi9j9c/Draft-COVID-19-Model (13).pdf
  • It explicitly states that their approach rests on the assumption of low hospitalization rate. Uh, why?
  • Its proposed model shows that 62% of Italy would've been exposed to SARS2 already by 3/6, with herd immunity already since they assume R0 of 2.5. WTF? If that were true, we would've already seen the drastic effects of that.
  • Completely ignores the effects of quarantine/isolation/lockdown.
These are smart people, this is their field of study, what are they doing? Or am I reading it completely wrong?
 
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USA political ideologies taken to the extreme are really pissing me off right now. One good thing about times like this: it flushes the real leaders out of the woodwork and they start uniting people into common action/purpose.

At the national level, the Jeopardy music is still playing, we're still waiting.
 
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