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No. of Recommendations: 10
Continue to be wrong.

Modelers now adjusting their ridiculous death projections down to match real data. Surprise!

That's what modelers do. Adjust the buttons on the dash board in real time to match the facts on the ground.

But then? what use are these models? Other than to throw humanity into a worldwide panic.

No need to look any further than this board over the past month. Widespread panic. Prediction of a million dead in the US. Really?

Natural systems always more complex than the numerical models made to simulate them. Surprise!

"Well, we didn't think about that when we built the model." Of course you didn't. You couldn't.

Now that the virus models have been debunked in very short order, now time scrutinize the veracity of supposed "climate change" models.

Let the flaming arrows start sailing! (ducking)
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No. of Recommendations: 4
Good summary:

"Nature is indifferent to the human need for simplicity and ease of comprehension, however, and many natural phenomena are complex. Just think, for example, about the chain of biochemical processes that take place merely in order to relay information from the photoreceptors in your eye to the visual cortex of your brain. If you try to incorporate everything that actually happens into a model, it becomes unwieldy and difficult to use. In the end you find that you rely to some degree on approximations and conceptual frameworks that make a process easy to visualize but don't necessarily reflect the true nature of reality."

https://sciencing.com/limitations-models-science-8652502.htm...
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No. of Recommendations: 54
You can't start a decent disagreement going (your apparent goal) without stating something verifiable.

I gather all you're saying is that, even though everything is about as horrible as predicted, we shouldn't have panicked so much about it?
I guess that's fair.
Just because something horrible is happening is never a good reason to panic.
So, you're right.


Prediction of a million dead in the US. Really?

If that one is aimed at me, notice I for one most explicitly did NOT predict a million dead in the US.
I noted that there was not yet good reason to believe that a million dead in the US was highly unlikely.
There's still nothing yet to suggest it's highly unlikely.

Nobody has yet found a way to halt the spread through the US population.
About 11 states don't have a stay at home order yet, and as far as I know there are no state border closures yet.
So it's still eminently plausible that half or more of the US population will be infected in the next couple of years.
Nor has there been any big improvement in treatment methods, so as yet no solid reason to think mortality rates will fall a lot.

I'd do the multiplication, but then it would be one of those dang models you hate so much :)

Jim
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No. of Recommendations: 3
How does one model human behavior?

Very, very difficult. Impossible?

One of the big deficiencies of the Imperial model.

Example of very smart people being way short on their model predictions.
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No. of Recommendations: 1
One of the big deficiencies of the Imperial model.

What deficiencies?

Jim
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No. of Recommendations: 3
Now that we have more real data, were adjusting our model prediction down:

https://www.cnn.com/2020/04/07/health/ihme-updated-covid19-m...

Surprise!

(Glad he have real data)

"Forecasting is difficult, especially when the future is involved."
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No. of Recommendations: 1
"Some people just want to watch the world burn.”
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No. of Recommendations: 20
Now that we have more real data, were adjusting our model prediction down:

Though it's great to see downward revisions, note that this model predicts deaths only in the next
four months, and assumes social distancing stays in effect through August including business and school closures.
So it's only predicting deaths during the temporary lockdown, not what happens after that.

There is no longer term strategy to actually halt the spread, so at the moment it's about how many
cases can be delayed to (say) some time in 2021 when good treatments and/or vaccines may become available.
Having big success on delay is a very worthy goal they're making progress on, so it's good news.

Unfortunately we have to remember that this model isn't trying to predict the total mortality from the outbreak.

Jim
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No. of Recommendations: 9
Now that the virus models have been debunked in very short order, now time scrutinize the veracity of supposed "climate change" models.

One BIG difference between the virus models and the climate change models: reflexivity.

Because the virus models were so dire, they changed reality by forcing politicians in denial (like BJ) to change their course. They forced people to panic, thereby invalidating their own assumptions.

Nobody in power is taking solid actions to avoid climate change. "+1.5C over 50 years..." etc is too scientific and insufficient to arouse the active interest of the general public and hence of our "leaders" who specialize in leading from behind.
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No. of Recommendations: 9
"those dang models you hate so much :)"

I don't hate models. Am familiar with them. Worked with them (oil and gas reservoir, financial) for decades and understand their limitations. As with Coronavirus models, we updated and recalibrated them yearly with actual data, production, reservoir, pressures, fluid compositions.

Hate generally felt towards a person or group? Not a mathematical concept?

verb: hate; feel intense or passionate dislike for (someone).

Maybe hate more appropriate word for the board's intense and passionate dislike toward a certain US leader and anything he does, and/or anyone associated with him?

Hate speaks more to the hater than it does the hated?
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No. of Recommendations: 2
So they elected an arsonist to lead...
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No. of Recommendations: 1
There is no longer term strategy to actually halt the spread

The biotech world working tirelessly 24*7 on antibodies, therapeutics, vaccines and more than 100 clinical trials in progress around the world would disagree.

The strategy is not just a government policy.
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No. of Recommendations: 2
Now that we have more real data, were adjusting our model prediction down:
=========
Though it's great to see downward revisions, note that this model predicts deaths only in the next
four months, and assumes social distancing stays in effect through August including business and school closures.
So it's only predicting deaths during the temporary lockdown, not what happens after that.


My read is that is the number of deaths through August, assuming full social distancing through May 2020. I take that to mean current measures (in 43 states?) until the end of April, with deaths being the cumulative count at the end of August. (They project around 60 000 deaths, with a peak of 2212 deaths on April 12, i.e. in 4 days.)

As Jim says, this epidemic will be a long way from being over in August. And I have no idea how they possibly expect deaths to be zero from June 1st to the end of August, if the social distancing stops at the end of April. I'm not familiar with the model, but either the title is wrong (the part in italic in the previous paragraph or there's something else screwy. I'm guessing the title, because in the FAQ, they say At present the forecast, which assumes continued social distancing, only covers the next four months... Also, the graphs only go to the end of July. Perhaps by 'through August' they only mean 'until August'?

I don't know about the model, but the presentation needs a little work.

dtb
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No. of Recommendations: 0
How does one model human behavior?

Very, very difficult. Impossible?


I don't know...why not put a tracking device on a large sample of the population?
Then track everywhere they go and every other person they interact with or come close to and every transaction they make.

Seems like we have all the hardware already in place.

Mike
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No. of Recommendations: 7
My read is that is the number of deaths through August, assuming full social distancing through May 2020.
I take that to mean current measures (in 43 states?) until the end of April, with deaths being the
cumulative count at the end of August. (They project around 60 000 deaths, with a peak of 2212 deaths on April 12, i.e. in 4 days.)


The linked article noted, incorrectly:
But the newest version of the model underscores just how important social distancing continues to be:
It assumes those measures -- such as closing schools and businesses -- will continue through the
modeled period, which is until August.


It's maddeningly difficult to find the relevant information at the washington.edu site.
This is the only relevant link I found, just a press release summary.
http://www.healthdata.org/news-release/new-ihme-covid-19-for...
As you noted, the article appears to have been wrong.
The release says: " Our forecasts assume that social distancing remains in place until the end of May"
So, neither the April you mentioned nor the August that the article mentioned.

The press release also includes these passages:
“As we noted previously, the trajectory of the pandemic will change – and dramatically for the
worse – if people ease up on social distancing or relax with other precautions.
...
This is evidence that social distancing is crucial.”
...
“Our estimates assume statewide social distancing measures are continuing in states where they have already been enacted,
and for those states without such measures in place, it is assumed they will be will be in place within seven days,”
[meaning April 12]

It does not appear, as far as I can tell, to be an attempt to model any outcomes past the first wave or past August.
It's wonderful news that they estimate a lockdown till end May will result in such a reduced number of victims.
But it's easy to be concerned about what happens when they're lifted.

I think of it as like the problem of estimating how frequently someone coughs in a 5 minute period by watching them for the first minute.
Sounds good at first, but the person started by holding their breath for the first minute. You know that ain't gonna last.
And you have still gathered no data on what happens when they try to breathe normally.

I admit to some puzzlement why their model predicts essentially a flat line of caseload near zero in July and August if it's assuming lockdown ends at the end of May.
It currently forecasts precisely zero US hospital beds required on June 28 and thereafter.
It expects zero deaths after June 21. Neither the central estimate (60415) nor the high end of the estimated range (126703) changes after that.
https://covid19.healthdata.org/united-states-of-america
Am I reading that incorrectly? It just doesn't seem to make sense.
The virus won't be extinct, it will be present widely, albeit at low frequencies.
So why wouldn't it grow exponentially again when lockdown is lifted?
Is this presupposing a full blown 100% effective national contact tracing system with immediate forced quarantine of all those exposed?

Jim
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No. of Recommendations: 7
A model predicts millions of deaths if we don't do something.

We do something. There are not millions of deaths.

If you think that is the model failing, you are completely missing the point of what models are and what they do. They are not predictions of the future. They are attempts to estimate what might happen in some possible future.

****

I live in California. Our governor shut us down when we were at about 1/5 the case and death rate of New York State. New York's governor shut them down the next day. California currently is getting less than 1/10 the death rate of New York. It is looking like California will come out of this with 1/10 the deaths that New York comes out with.

I am looking forward (sarcasm) to the idiocracy of California slamming our governor for an expensive shutdown when in actuality a few thousand people died. What was all that money for? It will happen, I've lived with this idiocracy most of my life already, this is what they do. They are like bad seed children who rail against those adults and their constant demands to do some work, prepare, and take some care.

Yup. If we come out of this with 100,000 or less deaths in the US, that will be proof that it was all a conspiracy and we shouldn't have worried about it. Because how can an entire idiocracy be wrong?

R:(
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No. of Recommendations: 6
The Quacks told us 2.2 million people were going to die.
A well respected person here made a a bold prediction that 1,000,00 deaths were possible in US alone.
A week later the the hapless modelers said it was 240,000.
The day after that they cut it to 100,000.
Now they're down to 60,000.

If they keep up the trend, they'll have people rising from the dead in the next couple of days.

By the way, Happy Easter!
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No. of Recommendations: 2
We should have known better than to have trusted models:

"A model that has correctly predicted the presidential election since 1980 says Clinton will have a landslide victory"

https://www.businessinsider.com/moodys-analytics-predicts-hi...

Thud!
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No. of Recommendations: 6
A well respected person here made a a bold prediction that 1,000,00 deaths were possible in US alone.


Who was it that said that? I hope it was me. That estimate is still very much possible.

327 million Americans
50% infected, 163 million
50% of those have a symptomatic case, 82 million
15% hospitalized, 5% ICU, 2% death, 1.6 million

This assumes that adequate health care will be available to everyone, which will not be the case if too many of these infections occur at the same time. It also assumes that 'only' 50% are infected, whereas experts suggest the number is 30-80%.

On the bright side, there may be effective antiviral treatments in the next 6 months (although I don't expect them to have a very big effect.) There may be a vaccine in 12-24 months, but there may very well not be one (just like there are no vaccine for HIV, Hepatitis C, zika, dengue, Ebola, RSV, etc. etc., despite decades of research.) There may be more than 50% of people that have an asymptomatic case; if the correct number is 75%, for instance, then we would 'only' have 0.8 million deaths.

I think 1 million is still a pretty good estimate, actually, not just something that is possible. 1 million is likely, 2-3 million is possible, half a million is getting positively optimistic.

Despite the gravity of the epidemic, I still think the shutdown is a huge, antidemocratic mistake. These death numbers all stay about the same with or without a shutdown, it's just that we ALSO get economic collapse and depression with the current strategy, as a bonus. We should move quickly to the Swedish approach, letting the infection spread in young healthy people and protecting the old and sick (if that is what they want) while we expand treatment capacity, start getting substantial herd immunity in the young and, maybe, discover some better treatments.

dtb
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No. of Recommendations: 30
The Quacks told us 2.2 million people were going to die. A well respected person here made a a bold prediction that 1,000,00 deaths were possible in US alone. A week later the the hapless modelers said it was 240,000. The day after that they cut it to 100,000. Now they're down to 60,000.

What's your point? My mother-in-law is dying alone in a hospital of COVID-19. My cousin is a critical care nurse worried sick about her own health as she cares for the sick and dying. Another cousin is a first responder placing himself in danger daily to help others with COVID-19. My best friends 24 year old daughter was bed ridden alone in an apartment for two weeks, three states away. Who knows what the long term effects of COVID-19 will be on her lungs.

The families of those with COVID-19 have to sit paralyzed, waiting while their loved ones rely on the grace of strangers because this disease isolates us. It kills me to watch my wife suffer because she has to accept that her mother is dying alone. The suffering of people far exceeds any number of predicted deaths you come up with. So, I ask again,

What, exactly, is your point?
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No. of Recommendations: 0
You can look at all (20+?) posts from last two weeks and see basically the same "how could the models be so wrong".
https://boards.fool.com/lastposts.asp?limit=99&uid=20371...
Then proceed accordingly.


On this topic, I thought any posts or videos that show how changing just a few of the inputs to pandemic outbreak models drastically changes the outcome.
For example:
Well, look at R=2.3 (Covid-19) vs R=1.3 (flu, or Covid-19 with social distancing)
https://twitter.com/ASlavitt/status/1248407197572186122
Andy Slavitt @
@ASlavitt
The answer? That is exactly how exponential math works.

If 1 person infects 2.3 people on average, after 10 cycles 4100 are infected. But if one person infects only 1.3 people on average, only 14 people are infected. 7/
5:27 PM · Apr 9, 2020

Or here are some good videos that explain similar:
Simulating an epidemic:
https://www.youtube.com/watch?v=gxAaO2rsdIs
==> wash your hands. don't touch your face
==> social distancing
==> identify (early widespread testing) and isolate the people who are infected
Simulating an epidemic
3,020,574 views
•Mar 27, 2020
3Blue1Brown
2.71M subscribers


https://www.youtube.com/watch?v=Kas0tIxDvrg
Exponential growth and epidemics
5,257,591 views
•Mar 8, 2020
3Blue1Brown
2.71M subscribers
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No. of Recommendations: 33
"The Quacks told us 2.2 million people were going to die.
A well respected person here made a a bold prediction that 1,000,00 deaths were possible in US alone.
A week later the the hapless modelers said it was 240,000.
The day after that they cut it to 100,000.
Now they're down to 60,000. "


Perhaps you should try to understand the caveats and assumptions of each of those predictions. Then you would realize that the numbers changed because the assumptions changed. For example, the 2.2 million estimate was a baseline number if nothing was done to try and slow the spread. Well, obviously something was done to slow the spread so that number is no longer meaningful.

Another example, the 60,000 number you think is current is only counting deaths through August not overall deaths, many of which will occur after August.

No, many of the models have been fairly consistent. They may have changed a bit, but only in response to the way the world they were modeling has changed. Just a month ago, no one had heard the term "social distancing". Now the behavior is being used almost everywhere.

What is confusing though is that you are demonstrating as little knowledge of the models as you did weeks ago when you first started talking about them. You would think that if something interested you so much that you would repeatedly post about it, you would develop a better understanding of the subject over time. Yet you haven't.
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No. of Recommendations: 1
The Quacks told us 2.2 million people were going to die. A well respected person here made a a bold prediction that 1,000,00 deaths were possible in US alone. A week later the the hapless modelers said it was 240,000. The day after that they cut it to 100,000. Now they're down to 60,000.

When you post this to a bunch of other Quacks, you will get the type of response you are getting now. I just wish the quacks would move over to the virus board, please.

A Quack is a Quack and will always be a quack, I mentioned on this board already... *it* is truly a mental disorder!
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No. of Recommendations: 0
COVID-19 Projection Models Are Proving to Be Unreliable

https://www.nationalreview.com/corner/coronavirus-pandemic-p...

Really? Who would have thought;?)
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No. of Recommendations: 0
There may be thousands of people dying at home who are not yet being counted:

https://www.nytimes.com/interactive/2020/04/10/upshot/corona...
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No. of Recommendations: 0
> The Quacks told us

Why do you things you don't understand?
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No. of Recommendations: 5
Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.

- Warren Buffett
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No. of Recommendations: 64
The Quacks told us 2.2 million people were going to die.

Out of curiosity, have you considered skimming the Imperial College study, the source of the 2.2 million figure?
It predicted a total 22,400 deaths in the US.

What?! You didn't know that?! I'm shocked. Shocked, I say.

Of course that number doesn't get nearly as much press, because it doesn't make an exciting tweet or rant on the internet.

May I draw your attention to Table 4 on page 13, which shows the detailed estimate of total deaths for 100 different combinations of mitigation strategies.
None of them is a prediction of what will happen, because the authors make no assumption of what strategy will be taken.
Each of them is an best-guess prediction of the number of deaths if you follow that one of the 100 strategies.
(technically there are 4 different treatments of each of 25 different strategies, based on lack of precision on R0, but it amounts to the same thing: 100 outcome estimates.
As a baseline, one of the 25 strategies considered was "do nothing")

The detailed figures in that specific table are for the UK, but it notes the US estimates are similar so we can just turn them into percentages.

The peak number was (yes) 550,000 deaths in the UK and 2,200,000 for the US.
With strategy PC_CI_HQ_SD and "On trigger"=60 and R0 assumption 2.0, it forecasts a death 1.108% of peak.
So it is exactly as true to say that the famous Imperial College paper forecast a total of 22400 deaths in the US as it is to say that it forecast 2.2 million deaths in the US.

The truth is it didn't forecast either of those.
It didn't forecast *any* number of deaths.
It merely estimated what would happen in a whole bunch of different mitigation strategies, with, yes, "do nothing" as the baseline for comparison.
The country leaders are the ones that then picked the strategy to follow. Some picked "do nothing".

As an aside, the median figure of the 100 scenarios is 8.63% of the peak number.
So, to the extent that this famous Imperial College paper be thought of as a prediction at all, it would have been a prediction of about 190,000 US deaths.
That's in total, not just first wave, unlike the IHME (University of Washington) model which currently forecasts 61,545 deaths by August 4.

Overall, it seems to me that the Imperial model paper is pretty good given how long ago it was done.
It was published 26 days ago--a lifetime ago in terms of data availability.
Many lifetimes ago, in terms of needless deaths due to policy mistakes.
The test of its quality is the degree of match between actual total deaths and the forecast from
the scenario out of the 100 that most closely matches what actually gets implemented.

A well respected person here made a a bold prediction that 1,000,000 deaths were possible in US alone.

Yup.
Not probable, but possible. (full impact, not just first wave)

The first news which has changed my estimation of the likelihood is the study by Silverman and Washburne.
Even so, Covid-19 is now the leading cause of death in New York state.
The usual #1 cause of death is ischemic heart disease, at around 460 per day.
New York state reported 777 Covid-19 deaths yesterday.
We all hope that situation won't last long and that New Yorkers can get back to keeling over from heart attacks.

Jim
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No. of Recommendations: 5

Yup.
Not probable, but possible. (full impact, not just first wave)


Given the experience with Italy, I’ve been surprised at how quickly social distancing and shutdowns have shown effects in the US.
I was thinking that it would take longer and I expected deaths to ramp up to 5-10k at least.
This appears to be due to any combination of the following:

1. Smaller household units in the US (compared to Italy). Grandpa and Grandma do not live in the immediate proximity of the grandchildren.

2. Much better early compliance and individual initiative wrg to social distancing due to the fact that Americans had the opportunity to witness the Italian horror show week after week, whereas the Italians had no idea what they were in for and so were initially much more lax.

3. Seasonality. It appears to me that hot weather must have some kind of effect, otherwise the Philippines would be completely overwhelmed by now.

It’s always very interesting to analyse why your predictions have been wrong.
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No. of Recommendations: 2
"It predicted a total 22,400 deaths in the US."

Equally useless number, but at least now we have a lower bound :)

There was a guy on this board a few weeks who states the following:

"I can't say I see anything yet to suggest that seeing over a million US deaths is highly unlikely.

It seems distressingly plausible."

https://boards.fool.com/quotwith-great-optimism-i-look-forwa...

Imperial College: 22,400
Mungofitch: 1,000,000+

That's pretty wide range, No? hopefully, no one made knee jerk investment decisions based on the 1,000,000 estimate. As you recall, things were (and still are) very tense. People do dumb things under stress. I know from experience.

Jim, I respect you and have admiration for your intellect. You have benefitted many here, myself included, with your insights and knowledge.

Just pointing out that models and forecasts should always be taken with more than a few grains of salt. Especially, when they predict the future.

Best to you.
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No. of Recommendations: 20
Given the experience with Italy, I’ve been surprised at how quickly social distancing and shutdowns have shown effects in the US.
I was thinking that it would take longer and I expected deaths to ramp up to 5-10k at least.
This appears to be due to any combination of the following:


There is another big factor:
Fatality rates are still wildly uncertain since no large country has done a decent random sample percentage of tests.
We might know the death counts, but without knowing the infection count nobody knows the "infection fatality ratio".
(incidentally, "excess death" analyses in Italy and elsewhere suggests roughly half of fatalities are reported as Covid-19 fatalities, as a rule of thumb)
Without knowing the IFR, there is no way of distinguishing between two ends of the theory spectrum that fit the mortality data:
* The infection is far more contagious and widespread than estimated, but has a lower infection fatality ratio.
* It is less contagious and widespread, but more deadly.


The recent study by Silverman and Washburne is interesting, as a new attempt to estimate the IFR.
First step:
How many people show up at the doctor reporting influenza-like illness (ILI)?
These numbers are reported regularly in the US, broken down by location, if they meet certain diagnostic criteria. (temp over 100, for example)
Some fraction of those, in each place, will get tested for influenza, so you can estimate the fraction of patients with ILI that had conventional influenza in each place.
The rest have the diagnostic symptoms of influenza, but arising from something else.

It turns out that
* The number of these non-influenza ILI doctor's visits has really spiked a lot lately, and
* The spikes correlate very strongly with locations with known Covid-19 hot spots.

"So what?" one might ask. We already know there is a Covid-19 epidemic.
Well, it's the size of the spike above the expected levels.
Estimated non-influenza IFI March 8-28 exceeded the expected baseline by 23m cases.
That's a whole lot of cases of *something*. (or multiple somethings).

Now, there are lots of confounding factors.
Maybe there is another flu-like illness going around at the same time.
Maybe people are extra likely to go to the doctor because there's a pandemic (though the reported cases do have to meet the diagnostic threshold).

But, if the "extra" non-influenza IFIs were all from Covid-19, then there is a silver lining.
The time from symptoms to death is relatively well known, and working backwards from the 7000 official deaths in the later period, you arrive at an IFR that is lower than any other estimate.
Close to the 0.1% for normal seasonal influenza, in fact.
Needless to say, this does not match other analyses very well, other than the rather speculative report from Oxford.

Now, maybe there are holes in this.
As mentioned above, deaths from Covid-19 are probably far higher than--maybe twice?--the official numbers, for one thing.
But maybe, if we're really lucky, it's *very* contagious and widespread but with low death rates as a percentage of those infected.
It would be like a seasonal flu that's merely (a) reaching a way larger fraction of the population than usual, and (b) all at the same time.
That would make it worse than seasonal influenza for both reasons, but not ten times as bad.

The two ends of the model--not too widespread but very lethal, versus widespread and not so lethal--
give the same estimated number of deaths for a long time, but for the US only up until about next week.
Before the end of April the expectations for the two theories diverge wildly, so that might give
the answer before there is widespread random testing to get better case counts.

This good news doesn't change your rather grim prospects if you're already sick enough to be hospitalized.
But it does increase our estimate of the number of people near you who got it but did NOT need to get hospitalized.
No better news for you, but much better news for your town.

We all hope for the super-duper-contagious low-fatality-ratio theory to be the right one, as it would suggest an early peak and low final death total.

Jim
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No. of Recommendations: 27
Imperial College: 22,400
Mungofitch: 1,000,000+
That's pretty wide range, No? hopefully, no one made knee jerk investment decisions based on the 1,000,000 estimate.
...
Just pointing out that models and forecasts should always be taken with more than a few grains of salt. Especially, when they predict the future.


I hope you do fully understand that neither of those was a prediction of the outcome, right?
And that my 1,000,000 wasn't an estimate of what would happen?
Did you read my post?

It's kind of entertaining to see someone dissing things that aren't predictions for being bad predictions.
Incidentally, the grammar of the German used in your post is all wrong!

Jim
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No. of Recommendations: 0
* The infection is far more contagious and widespread than estimated, but has a lower infection fatality ratio.

* It is less contagious and widespread, but more deadly.


I don’t think it’s true that we cannot know this.
The number of deaths allows us to estimate at what rate the infection is spreading. If the number of deaths is doubling every 3 days, so is the number of infections.
This imposes certain upper and lower limits on how many infections there could be.
We also have data from limited populations (i.e. Diamond Princess) that tell us about the death rate. There was some Italian town for example with 4800 inhabitants which had a death rate of 1.7% of the population. I don’t know anything else about the demographic make-up there, but the hospitals were certainly pretty overwhelmed.


We all hope for the super-duper-contagious low-fatality-ratio theory to be the right one, as it would suggest an early peak and low final death total.


I have seen no data that would lead me to think that this is a credible scenario.
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No. of Recommendations: 1
One thing I forgot that could have contributed to the much quicker US success with lockdowns/distancing:

4. In Italy, nosocomical (in hospital) infections played a big role. The US probably (hopefully) learned from that and avoided it.
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No. of Recommendations: 5
We all hope for the super-duper-contagious low-fatality-ratio theory to be the right one, as it would suggest an early peak and low final death total.
...
I have seen no data that would lead me to think that this is a credible scenario.


I tend to agree. The "Oxford theory" seemed tempting but unconvincing.
Until, and except for, this recent study.

The "excess sickness" population does seem to be very large, around 26m in three weeks.
That's not a count--it's calculated by sampling from 2600 clinicians. But that's a big sample.

The data led them to estimate that there were 7m "excess" new US infections of something flu-like
but not influenza in the stretch March 8-14, compared with 7000 officially counted deaths three weeks later.
Dividing A by B is what suggests the low IFR.
The big unknowns is how many of those "excess for 2020" non-influenza influenza-like illnesses were actually Covid-19, and how many excess deaths are being missed in the official counts.

So, their work doesn't mean it's the high-spread rare-severity scenario, but it is consistent with that interpretation.
Strongly suggestive, say.
I figure it would not be unreasonable to shift one's best guess somewhat in that direction.
Even a little good news would be nice.

This is the paper
https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v...

From the abstract:
"... we estimate the symptomatic case detection rate of SARS-CoV-2 in the US to be 1/100 to 1/1000.
This corresponds to approximately 10 million presumed symptomatic SARS-CoV-2 patients across the US during the week starting on March 15, 2020.
...
We emphasize the importance of testing these findings with seroprevalence data"


I would have thought that somebody out there would be doing sample testing (PCR and serum if possible) of some broad population by now, to get an answer.
If the people were selected truly randomly from the population, it wouldn't take that many tests to get a pretty darned good snapshot estimate.
The biggest problem would be the low positive rate, meaning you need a big sample to catch enough for your percentage positive to have any statistical significance.
But...maybe it wouldn't be so low if the positive rate is high. Don't know till you try.
A quick search suggests this has started in Finland, Ohio and Canberra.

Jim
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I would have thought that somebody out there would be doing sample testing (PCR and serum if possible) of some broad population by now, to get an answer.


Early results of a study done in Germany suggest 0.37% infection fatality rate. If you Google 'Heinsberg study' you'll find it. Critics say it is too small, and the selection of participants not transparent, and not peer-reviewed, etc.

I take it as just one more hint that the real fatality rate may be around 0.5%, if not lower.
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Now, there are lots of confounding factors.
Maybe...
Maybe...
But...


Despite all the modeling and conjecturing, as far as I know refrigeration trucks were never needed in New York City to stack the dead bodies before. There are a boatload of known unknowns and unknown unknown with this pandemic, but the overwhelming death in New York City (and Italy) is a fact. At a minimum, any talk of this being roughly like a bad flu is sheer stupidity at this point.
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"Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.

- Warren Buffett"


Do you think Buffett looks at the weather forecast and uses that to decide to bring an umbrella before heading out?
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Do you think Buffett looks at the weather forecast and uses that to decide to bring an umbrella before heading out?

Why not? Did not Mr. Buffett once say something to the effect that God created stock market analysts to make weather forecasters look good?
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"Imperial College: 22,400
Mungofitch: 1,000,000+

That's pretty wide range, No? hopefully, no one made knee jerk investment decisions based on the 1,000,000 estimate."


Do you not understand what Mungofitch said? Do you STILL not understand the models?

Perhaps before criticizing something you should try to understand it first so your criticism can be more credible.
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PhoolishPhilip -

I am struggling for the right words, but I wanted to thank you for sharing your burden. This is a challenging time for all of us - but your experience seems heavier and harder than most. I hope you can find the strength to be a comfort to your wife and best friend - and I hope your mother-in-law and your best friend's daughter can recover from this terrible disease. I am amazed at the courage of all medical workers and first responders - I hope your cousins remain safe and please thank them for their strength. Lastly, please find moments to care for yourself.

-Jeff
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Jim -

You said: I would have thought that somebody out there would be doing sample testing (PCR and serum if possible) of some broad population by now, to get an answer.

A broad nationwide survey of the United States was announced by the National Institute of Health yesterday (Friday April 10th). They are taking healthy volunteers over the age of 18:

After enrollment, study participants will attend a virtual clinic visit, complete a health assessment questionnaire and provide basic demographic information—including race, ethnicity, sex, age and occupation—before submitting samples in one of two ways. Participants working at the NIH Bethesda campus will have blood drawn at the NIH Clinical Center. Other volunteers will participate in at-home blood sampling. Neoteryx, a medical device firm based in Torrance, California, will supply at-home blood collection kits. Researchers will ship each study participant a Mitra®Home Blood Collection Kit and provide detailed instructions on collecting a microsample of blood and mailing it back for future analysis in the laboratory.

https://www.nih.gov/news-events/news-releases/nih-begins-stu...

-Jeff
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So, their work doesn’t mean it’s the high-spread rare-severity scenario, but it is consistent with that interpretation.


It’s also consistent with the scenario where people are now highly sensitized to random and/or imagined symptoms they would otherwise ignore.
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It seems this study would support the lower IFR hypothesis. Related to the Silverman and Washburne study Jim referenced. Suggests that there are in fact 2,300 to over 115,000 infected persons in a region reporting only 446.

https://abcnews.go.com/US/sewage-analysis-suggests-england-m...

John
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Despite all the modeling and conjecturing, as far as I know refrigeration trucks were never needed in New York City to stack the dead bodies before.


I think they buried people in Central Park during the Spanish Flu epidemic.
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So, their work doesn’t mean it’s the high-spread rare-severity scenario, but it is consistent with that interpretation.
..
It’s also consistent with the scenario where people are now highly sensitized to random and/or imagined symptoms they would otherwise ignore.


Yes, I mentioned that factor.
It's some combination of the following
* a greater propensity to visit the doctor for any given level of symptoms, because people know there's an epidemic
* a lesser propensity to visit a doctor because of lockdowns or fear of catching something there, coincidentally matching government instructions
* an inability to visit a doctor who is unavailable - too busy, office closed, working at the hospital, etc.
As a guess, the first one might be a meaningful factor.

Offsetting that are things like the notion that lockdowns should be suppressing all kinds of influenza like illnesses (ILIs), whether Covid-19 or not.
So, you would expect fairly low ILI counts recently.
But the counts have been very high.
The lockdown locations have correlated with where the Covid-19 has been most prevalent, yet the "excess" ILIs also correlate with where the Covid-19 has been most prevalent.
It's messy because of timing--a lockdown won't affect counts before it was enacted locally.

There shouldn't be a problem with a greater level of reporting, though.
You need to have a given threshold of fever and specific symptoms to get counted.
So, once somebody has decided to see one of the doctors who reports, the count should be accurate from there.
Imaginary symptoms shouldn't be a material factor.

Jim
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You said: I would have thought that somebody out there would be doing sample testing (PCR and serum
if possible) of some broad population by now, to get an answer.
...
A broad nationwide survey of the United States was announced by the National Institute of Health
yesterday (Friday April 10th). They are taking healthy volunteers over the age of 18:


Probably a good move, but not what I was thinking of personally.
If it's a set of volunteers, it's not random.
If it's not random, you can't extrapolate the results to the population at large.

Jim
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Despite all the modeling and conjecturing, as far as I know refrigeration trucks were never
needed in New York City to stack the dead bodies before. There are a boatload of known unknowns and
unknown unknown with this pandemic, but the overwhelming death in New York City (and Italy) is a
fact. At a minimum, any talk of this being roughly like a bad flu is sheer stupidity at this point.


Do understand that I am not belittling the disease.
I'm not one of those "don't worry, it's just another flu" deniers.

My comments should be read carefully:
I noted that a certain study suggested that the infection fatality ratio (IFR) could be lower than other estimates, and could be as low as that of seasonal flu.
But IFR is just one factor in the severity of an epidemic.
Even if true, the IFR can only be lower than other estimates (good) because the case count is vastly higher (bad).
The death counts to date (high and bad) are unchanged. Nothing in what I said, nor implied by the study, suggests otherwise.

I specifically mentioned that, even if the low IFR were true, the Covid-19 outbreak would still be far worse than seasonal influenza for a variety of different reasons.
The biggest one is that, for this hypothesis to be true, the disease would be hitting a very much larger percentage of the population--larger than any seasonal influenza--due to vastly higher contagion.
Plus the fact that it is compressed in time: the disease can and does sometimes crash local health care systems.
And of course the rapid spread means there is no seasonal vaccine available.

As a very wild guess, it seems plausible that even if the paper's results were true to the optimistic side,
it might still be maybe 3-6 times worse than a bad influenza season in terms of total fatalities.
Far more deaths in absolute numbers, and a far worse spike in caseload with all that that entails.
A bad influenza season generally causes over 50000 deaths in the US.
A figure 3-6 times that is not anything to belittle, nor would I.
But it would still be nice news, since it's much better than (say) 10-25 times worse, the sort of figures you might get from other studies.

Emphasis on "very wild guess".
All we know for sure is that at any given level of the population getting infected, a lower best-guess IFR estimate would be better news.

What the researchers infer from the data might be right, or it might not.
But it is superficially interesting.
The number of people visiting doctors with "reportable threshold" influenza-like illnesses, but not influenza,
and in excess of what would be normal for this time of year, was running around 200 times the number of reported Covid-19 cases in the same time frame.
(extrapolated from a big sample)
Plus, geographically, this effect was biggest in places that Covid-19 is known to be relatively prevalent.
A lot of those doctor's visits are presumably Covid-19 cases, but we don't know how many.
Some unknown fraction of those cases will be other things, meaning the hidden Covid-19 cases would be lower than that figure suggests.
But all asymptomatic and mild (e.g. fever under 100F) Covid-19 cases would still be uncounted, meaning the undiagnosed Covid-19 cases would be higher than this metric could catch.
There are wild unknowns in the interpretation, but even with big error bars an "excess" of 23 million non-influenza
symptomatic influenza-like illnesses in three weeks is something in need of an explanation.
Surely some disease, or set of diseases, is causing that bump.
There is a known epidemic going on with generally very low testing rates, so there is one obvious potential explanation worth considering as a candidate.

From a high level perspective, we'll know soon.

If I were a betting man, I would wager on the more optimistic (IFR<0.2%) interpretation being mostly wrong. (a bet I'd love to lose, of course).
My own speculation is that first wave "peak daily cases" might be hit soon in the US, but that the curve will not end up being symmetrical but positively skewed.
Big rise, peak, very long slow fall because of a nearly steady stream of new cases for quite some time.
Why this idea?
In Italy daily deaths 15 days before the peak were 20% of the peak.
But 15 days after the peak, deaths were still 67% of the peak, suggesting a slow wane.
Given the relatively spotty lockdown situation in the US, it seems reasonable to posit a weaker fall-off rate than in Italy.

Jim
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A broad nationwide survey of the United States was announced by the National Institute of Health
yesterday (Friday April 10th). They are taking healthy volunteers over the age of 18:

Probably a good move, but not what I was thinking of personally.
If it's a set of volunteers, it's not random.
If it's not random, you can't extrapolate the results to the population at large.



The word ‘volunteer’ is a red herring - human trials always use volunteers. A bigger problem is the odd exclusion of people with symptoms consistent with COVID-19 or even with known exposure to a case. Since no one knows how many people that represents, extrapolation of the results will be difficult.
EXCLUSION CRITERIA:
Confirmed history of COVID19 infection or exposure
Current symptoms consistent with COVID19 infection


This is sort of like doing an election poll to see who will win the election, but excluding people who are party volunteers or related to one. But since 95% of the population will not be excluded, this shouldn’t matter much.

A more serious failing is that they don’t specify how people will be recruited beyond saying: Sampling Method: Non-Probability . I can only hope that they are at least trying to recruit people randomly, so that the prevalence results can be applied to the general population, or at least to the vast majority of people without symptoms or known contact, and not just getting people who ‘volunteer’ to be tested (who respond to an ad, for instance.) I doubt it, but we can’t be sure, because they don’t say what they’re going to do, even in the ‘Study Design’ section.

Dtb
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There are a boatload of known unknowns and unknown unknown with this pandemic, but the overwhelming death in New York City (and Italy) is a fact. At a minimum, any talk of this being roughly like a bad flu is sheer stupidity at this point.


This comparison with the flu has become the new Hitler - never to be used in any comparison. But you and Jim and others should drop your holier than thou kick - it’s a perfectLy reasonable comparison to make.

SARS-CoV-2 is a bug that’s ‘newer’ than the common flu bug (less immune experience), and so would seem to have the potential to infect more humans. It also seems somewhat more contagious, so in normal, non-lockdown conditions, it spreads faster (despite a longer incubation period - five days instead of two.) It’s lethality, in symptomatic cases, is much higher than for symptomatic seasonal flu (about 1-2% instead of 0.1%), but the really big unknown is the proportion of asymptomatic cases, which we thought until recently was about half, but may be far smaller. Recent information from influenza-like illness excesses, sewage analysis, and jurisdictions like Iceland with high rates of testing, indicate that we can be cautiously optimistic on that score.

The Gangelt cluster analysis is the most convincing, and suggests that the infection fatality rate is 0.37%, much better than the 0.65% that previously seemed like the best estimate, and that would correspond to a rate of asymptomatic disease more like 80%. That would still mean we might get 80%*0.37%=0.3% of the population dying, which is still a million Americans, or 20 times the ‘bad flu’.

The somewhat less certain conclusions of the Silverman study would put us much closer to a bad flu, but with highly concentrated deaths in a few weeks rather than the typical whole winter. That would be fantastic news, if it’s right, but it’s too soon to say. Seroprevalence surveys that have started already should answer the question pretty definitively in the next 2-3 weeks, and we will know whether we can start ending the lockdown quickly or whether it has to be lifted slowly, starting with the young, healthy 60% of the population (below 50) for whom this coronavirus really is ‘just a bad flu’.

Dtb
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There shouldn’t be a problem with a greater level of reporting, though.
You need to have a given threshold of fever and specific symptoms to get counted.


Yes, but in normal times, most people don’t go to the doctor just because they have a fever, unless they need a certificate for their employer.
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There shouldn’t be a problem with a greater level of reporting, though.
You need to have a given threshold of fever and specific symptoms to get counted.
...
Yes, but in normal times, most people don’t go to the doctor just because they have a fever, unless they need a certificate for their employer.


Yes, exactly as I noted in my post.

These are two distinct factors which you've mixed up here.

Concern #1: there may be a greater propensity to visit the doctor with any given level of illness.
Concern #2: among those who visit, the worry that doctors are counting people as ILI who are not meeting the diagnostic criteria.

Your post quoted above is in two parts:
* my comment that [specifically] concern #2--changed reporting by the doctors--is not a problem with the numbers, quoted without context so the meaning is unclear
* your comment that concern #1 is a statistical problem, which it is, exactly as I emphasized elsewhere in my post.

The full quote was:
"There shouldn't be a problem with a greater level of reporting, though.
You need to have a given threshold of fever and specific symptoms to get counted.
So, once somebody has decided to see one of the doctors who reports, the count should be accurate from there.
Imaginary symptoms shouldn't be a material factor."

This whole paragraph is asserting solely that concern #2 is probably not a material worry for the statistics.
Apologies if that wasn't clear. To rephrase:
From among patients we already know are getting examined by a reporting physician, the patient's
reason for booking the visit is probably not an input to whether the doctor decides it's an ILI case.
The statistical worries are among the processes prior to the start of the examination.
Those include, among other things, concern #1: a different likelihood of seeking a doctor's visit due to knowledge of the Covid-19 epidemic.
Which, as we both have noted, could be a big issue.

Offhand it seems unlikely to be enough to cause anything like 23 million extra non-flu diagnoses, but I'm sure it happens.

Jim
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This comparison with the flu has become the new Hitler - never to be used in any comparison. But you and Jim and others should drop your holier than thou kick - it’s a perfectLy reasonable comparison to make.

...
Seroprevalence surveys that have started already should answer the question pretty definitively in the next 2-3 weeks


Very nice overall summary DTB, kudos.

A couple of addenda:

One of the conclusions from the Silverman et al study is that the trajectory of death counts could tell us this answer much sooner than widespread antibody tests will.
To paraphrase unfairly:
Say you call a smoothed peak in daily US deaths during the first wave around April 21 as "early" and around May 6 as "late".
The degree of lockdown in the US to date probably isn't enough to cause an early peak in daily mortality in the "slower spread / higher mortality" scenarios.
But is is enough to work with an early peak date in the "faster spread / lower mortality" scenarios.
So, if we soon see US daily deaths on a trajectory consistent with an early peak, it bolsters the "high spread / low mortality" end of the estimates.
The expected curves diverge starting more or less now. Quite a lot within a week.

Separately, I'm not being holier than thou about doing factual comparisons with seasonal influenza.
I made one. (well, I quoted one).
It's entirely valid to compare any aspect of one disease to the other. Numerical comparisons can be very useful.
It's not valid to minimize the current epidemic by suggesting that the broad impact of is similar to that of normal seasonal flu. The data don't support that assertion.
I wasn't pleased with being falsely accused of that.

It's probably valid, just ill advised, to point out that Covid-19 is less severe than seasonal influenza--on a few very specific metrics.
Paediatric fatality rates, for example.

Despite trying to use great care and carefully phrased specific parameters, I still seem to have got jumped on for it, so I agree there is a certain Godwin thing going on!

Jim
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The sad thing is that the Chinese, more precisely, the CCP, must know how many people really died and have for sure already done studies on the infection and real fatality rate.

They don't make their data public because they would have to admit that all the numbers they published up to now are totally bogus. I mean, New York alone will have 3 times the total fatalities of the whole of China? Who are they kidding.
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They don't make their data public because they would have to admit that all the numbers they published up to now are totally bogus.
I mean, New York alone will have 3 times the total fatalities of the whole of China? Who are they kidding.


Maybe.
But in another way I believe the Chinese numbers more than the US numbers.
Not because they are inherently more truthful.
The Chinese did more of the testing needed, and the case count closer to stable than is the US count, so the error due to any fibbing is a relatively constant number of cases.
In the US the presumed truthfulness advantage might not outweigh the errors, shortfalls, lags, and unevenness of testing, while the case count target is changing very rapidly.
It has been like a diligent but nearsighted fellow doing his level best to count the animals in a stampede.
It doesn't take fibbing for his numbers to be bad.

Given the large error bars in the US, for some near-upper-bound estimate X we may have greater certainty
that China has had fewer than X cases to date than we are sure that the US has had fewer than X cases to date.
Maybe for X=2 million, for example.

That notion only breaks down if there is in effect an ongoing raging epidemic in China this month: the entire caseload drop was fake.
Could be.
But despite censorship, that would be fairly hard to hide in the modern age.
They could probably hide several hundred deaths new per day without much difficulty, but (at a guess) perhaps not several thousand per day.

Jim
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They don't make their data public because they would have to admit that all the numbers they published up to now are totally bogus.
I mean, New York alone will have 3 times the total fatalities of the whole of China? Who are they ============
Maybe.
But in another way I believe the Chinese numbers more than the US numbers.
Not because they are inherently more truthful.



I also tend to believe the Chinese data, because other Asian countries have shown that it is at least possible to do what China claims to have done. The question is, is that a worthwhile achievement? The number of fatalities until now is not the relevant result, what should count is the number of fatalities in say the two years after the beginning of the epidemic, when we might have a vaccine. New York will get its deaths right now, and then it will be over, whereas most of China’s deaths still lie in the future.

Perhaps they will succeed in slightly reducing the total number of covid deaths, both by never exceeding treatment capacities and, perhaps, by postponing infections long enough for their population to benefit from new treatments and prophylactics. If you don’t mind living in a totalitarian state while you wait for that possible denouement, then yes, you can reduce the number of covid deaths, at the expense of extreme human, social and economic disruption, with a certain difficult to estimate number of deaths from that, too, which should also be counted.

Clearly, it is easier to manage a herd of animals, and optimize the disease rate, if you can do whatever you want with them. I am grateful to live in a democracy where it is harder to manage them, since they will not just take orders, although we have proved to be a fair bit more docile than I would have thought. Being allowed to also cheat on the numbers makes it even easier for China to meet its targets, and I have little doubt that that is also part of the equation, but arguably not the most important part.

Never has New Hampshire’s slogan seemed more compellingly true.

Dtb
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CNN)An influential model tracking the coronavirus pandemic in the United States now predicts that fewer people will die and fewer hospital beds will be needed compared to its estimates from last week.

As of Wednesday, the model predicted the virus will kill 60,000 people in the United States over the next four months. That's about 33,000 fewer deaths than the model estimated last Thursday.
While the US is still expected to face a shortage of about 16,000 hospital beds, it will need 168,000 fewer beds than previously expected, according to the new analysis.

***New data on the pandemic's trajectory -- from the United States and around the world -- has been fed into the model almost every day, driving the changes.***

https://www.cnn.com/2020/04/07/health/ihme-updated-covid19-m...

LOL. Good thing we have models, that need to updated daily with new data, to tell us what's going to happen in the future:)
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Apparently this isn't the first instance of Imperial's Neil Ferguson spreading widespread panic:

POSTED ON APRIL 12, 2020 BY STEVEN HAYWARD IN CORONAVIRUS
WHAT WOULD WE DO WITHOUT EXPERTS?

Tyler Cowan of George Mason University and Phil Magness of the American Institute for Economic Research have dug up a few archival news stories about the “expert” predictions for potential pandemics over the last 20 years, featuring one now familiar “expert” who is very much in on the COVID-19 action:

https://www.powerlineblog.com/archives/2020/04/what-would-we...

Bird flu, swine flu, mad cow..... mad lamb?

Oh my!

The mad modeler (boy who cried wolf er...mad lamb) appears to love the limelight.
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Bird flu, swine flu, mad cow..... mad lamb?
Oh my!



I'm not sure what you're advocating here...perhaps you can clarify.

People shouldn't try to estimate what might happen with bad diseases--it's better to fly blind?
They should take on only really simple problems that are easy to forecast accurately?
These guys are all goofs and your forecasts would have been much more reliable?
Journalists always attempt to maximize panic by quoting only the upper limit of any range?
It's particularly easy to trash other people's work if you never actually read the original?

Jim
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Sadly, there are fairly large number of people in this country who have an "acceptable" number of deaths from COVID-19 in mind regardless of whether or not they could have been prevented with a minimum level of competence and desire.

I'm not one of them. Zero deaths would have been my acceptable number.

I'm sorry for your situation.

Mark

What's your point? My mother-in-law is dying alone in a hospital of COVID-19. My cousin is a critical care nurse worried sick about her own health as she cares for the sick and dying. Another cousin is a first responder placing himself in danger daily to help others with COVID-19. My best friends 24 year old daughter was bed ridden alone in an apartment for two weeks, three states away. Who knows what the long term effects of COVID-19 will be on her lungs.

The families of those with COVID-19 have to sit paralyzed, waiting while their loved ones rely on the grace of strangers because this disease isolates us. It kills me to watch my wife suffer because she has to accept that her mother is dying alone. The suffering of people far exceeds any number of predicted deaths you come up with. So, I ask again,

What, exactly, is your point?
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From the final frame of this strip:

https://fivethirtyeight.com/features/a-comic-strip-tour-of-t...

"In the words of Mark Weir, Director of Ohio State University's Ecology, Epidemiology, and Population Health Program: 'All models are wrong. It's striving to make them less wrong and useful in the moment.'"

My takeaway is that when error bars are so large, it means our behavior can influence the outcome. The value in the models is they help quantify our actions. They empower us to make decisions. If that leads to the models being "wrong," well as the professor says, they were wrong anyway. That's not the point. The goal is to make them wrong in the right way.

Rob
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I see this thread is now over 60 posts. So there's a lot of "talking" going on, but there doesn't seem to be much agreement being reached. So I'll offer this background and thought.

Well over a half-century ago, when I first moved into management positions with Exxon, I attended a training course called Games People Play - The Psychology of Human Relationships. It talked about how people interact and how to recognize what's really going on.

So I went to WikipediA to check my memory and see if it was still being taught. It is, in greatly expanded form apparently. I must have attended an early course, because we only talked about ten games or so. Now they're listing over 30. But I'll stick with what I recall in relation to this thread. Much simpler.

One game was "Ain't It Awful." The person playing that game with you did a lot of complaining, but didn't feel the need to offer any solutions. He/She just wanted to complain.

Another game was "I'm OK - You're Not OK." Basically I'm right and you're wrong - I know more than you do. (And why listen to what you say, you're not OK.)

I see both games going on in this thread - easy to identify who's playing the games.

Now I'm willing to accept it if Charlie Munger tells me that he's right and I'm wrong. He's probably correct. But I won't accept that from someone who obviously doesn't understand models - and doesn't want to learn. Just complain.

What I also recall from the course is how to respond when you recognize the game being played.

Just refuse to play. In this case, use the Ignore button.
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Based on the models I've been running for NYC, I project that about May 19, 2020, assuming we maintain 
the current level of social isolation, we will have racked up about 26,000 deaths and tested about 
400,000 positive for COVID-19 (assuming  number of test kits is similar to the past) and hospitals
should be caught up with few on ventilators.  .  Of course, if we abandon social isolation earlier,
we will have a higher death rate.  Without much broader testing, the true fatality rate can't be
measured.  I'm assuming that it is "only" about ,8% (or less), but this illness seems to compensate for 
that by being extremely contagious.

Some useful demographic metrics which support Jim's view (based exclusively on New York City):

https://www1.nyc.gov/site/doh/covid/covid-19-data.page
(Bottom of page has full data set links on Github)

The data in this report reflect events and activities as of April 13, 2020 at 9:30 AM.
All data in this report are preliminary and subject to change as cases continue to be investigated.
These data include cases in NYC residents and foreign residents treated in NYC facilities.

 NYC COVID-19 Deaths

                          No      Underlying
          Underlying  Underlying  Conditions
          Conditions  Conditions  Unknown  Total

Age Group
- 0 to 17        3        0        0         3
- 18 to 44     222       26       36       284
- 45 to 64    1215       54      180      1449
- 65 to 74    1181       23      307      1511
- 75 and over 2114       25      796      2935
Sex
- Female      1721       32      526      2279
- Male        2825       92      773      3690
- Unknown      189        4       20       213
Borough
- Bronx       1280       13      107      1400
- Brooklyn    1234       49      463      1746
- Manhattan    500       18      192       710
- Queens      1482       43      469      1994
- Staten Isl.  237        5       88       330
- Unknown        2        0        0         2

Total         4735      128     1319      6182

Underlying illnesses include Diabetes, Lung Disease, Cancer, Immunodeficiency, Heart Disease, 
Hypertension, Asthma, Kidney Disease, and GI/Liver Disease.

********************************************************

NYC COVID-19 Cases

Total Cases:  Total 106813

Median Age (Range) 
    50     (0-110)

Age Group
- 0 to 17          2070  (2%)
- 18 to 44        40454 (38%)
- 45 to 64        38797 (36%)
- 65 to 74        13483 (13%)
- 75 and over     11782 (11%)
- Unknown 227
Age 50 and over
- Yes             54938 (52%)
- No              51648 (48%)
Sex
- Female          49617 (47%)
- Male            56765 (53%)
- Unknown           431
Borough
- Bronx           23352 (22%)
- Brooklyn        28035 (26%)
- Manhattan       13705 (13%)
- Queens          33468 (31%)
- Staten Island    8198  (8%)
- Unknown 55
Deaths 6182

*******************************************************************

NYC COVID-19 Hospitalizations
.               Ever Hospitalized
                  Cases   %           Total Cases
Age Group
- 0 to 17           198 (10%)           2070
- 18 to 44         4460 (11%)          40454
- 45 to 64        10691 (28%)          38797
- 65 to 74         6558 (49%)          13483
- 75 and over      7427 (63%)          11782
- Unknown             1  (0%)            227
Sex
- Female          11967 (24%)          49617
- Male            17165 (30%)          56765
- Unknown           203 (47%)            431
Borough
- Bronx            6774 (29%)          23352
- Brooklyn         7537 (27%)          28035
- Manhattan        3934 (29%)          13705
- Queens           9729 (29%)          33468
- Staten Island    1348 (16%)           8198
- Unknown            13 (24%)             55
Total             29335 (27%)         106813

Percentages are row percentages, calculated percent hospitalized among overall cases each category.
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"Prediction is very difficult, especially about the future." Nils Bohr


"Those who have knowledge don’t predict. Those who do predict don’t have knowledge." Lao Tzu
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No. of Recommendations: 3
Some useful demographic metrics which support Jim's view (based exclusively on New York City):

Just to clarify, personally I don't have a view on it.
I was merely mentioning a recent study.
The implications, if true, seemed to be at once interesting, rational, and optimistic.

It's a very Bayesian question being posed:
Given the prior that you know there is a flu-like pandemic going on, then:
if you see a surge of millions of extra sick people who have consistent symptoms but you know they do NOT have influenza,
and their locations correlate strongly with the known epidemic locations,
what disease do they probably have?

Jim
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Never has New Hampshire’s slogan seemed more compellingly true.

I've read they've changed their slogan to read 'Live Free AND Die.'
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LOL. Good thing we have models, that need to updated daily with new data, to tell us what's going to happen in the future:)

My model of your cluelessness predicted you would have figured out what people were telling you about models long before now.

But your continued dog-with-a-fake-bone posts provide excellent data with which to update my models. Thank you.

R:
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A Comic Strip Tour Of The Wild World Of Pandemic Modeling

https://fivethirtyeight.com/features/a-comic-strip-tour-of-t...
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More on the the more than dismal science. The boys who cried wolf. Modelers now take a place below economists:

As Politicians Lean on Disease Modelers in Health Crisis, Some Scientists Fear a Backlash

Academics say their models are often imprecise and depend on incomplete data (Really? Now you tell us?)

LONDON—Politicians and government officials trying to chart a course through the global coronavirus pandemic have relied heavily on a specialized set of epidemiological experts: disease modelers.

As they make decisions affecting the health and livelihoods of hundreds of millions of citizens, world leaders have turned to projections of infections and deaths by these scientists, who by their own admission are working with a bewildering array of unknowns as they build their forecasts.

***Some leading modelers say their discipline is being asked to provide a level of certainty that it is unrealistic to expect, especially given how little is known about the new coronavirus. And they fear that they could become scapegoats for politically unpopular policies.***

“Any model that gets within 50% of the actual result has done well,” says Keith Neal, a professor in the epidemiology of infectious diseases at the University of Nottingham. “It is not an exact science.”

https://www.wsj.com/articles/as-politicians-lean-on-disease-...

Why didn't they make clear the levels of uncertainty before causing the world to goi into complete lock the down?

Any model that get's within 50%? That's a laugh. Some were several orders of magnitude off.

Now that we've destroyed the global economy, these educated idiots need to be called to task.

Meanwhile the world wastes billions/trillions to fight another invisible threat - global warming. All of this based on worthless models.

If we weren't so dumb, we'd be stupid.
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WSJ: The Bearer of Good Coronavirus News

Stanford scientist John Ioannidis finds himself under attack for questioning the prevailing wisdom about lockdowns.

By Allysia Finley
Updated April 24, 2020 5:14 pm ET

... scientists are almost never unanimous, and many appeals to “science” are transparently political or ideological. Consider the story of John Ioannidis, a professor at Stanford’s School of Medicine. His expertise is wide-ranging—he juggles appointments in statistics, biomedical data, prevention research and health research and policy. Google Scholar ranks him among the world’s 100 most-cited scientists. He has published more than 1,000 papers, many of them meta-analyses—reviews of other studies. Yet he’s now found himself pilloried because he dissents from the theories behind the lockdowns—because he’s looked at the data and found good news.

...

Dr. Ioannidis calls the coronavirus pandemic “the perfect storm of that quest for very urgent, spectacular, exciting, apocalyptic results. And as you see, apparently our early estimates seem to have been tremendously exaggerated in many fronts.”

Chief among them was a study by modelers at Imperial College London, which predicted more than 2.2 million coronavirus deaths in the U.S. absent “any control measures or spontaneous changes in individual behaviour.” The study was published March 16—the same day the Trump administration released its “15 Days to Slow the Spread” initiative, which included strict social-distancing guidelines.

Dr. Ioannidis says the Imperial projection now appears to be a gross overestimate. “They used inputs that were completely off in some of their calculation,” he says. “If data are limited or flawed, their errors are being propagated through the model. . . . So if you have a small error, and you exponentiate that error, the magnitude of the final error in the prediction or whatever can be astronomical.”
https://www.wsj.com/articles/the-bearer-of-good-coronavirus-...
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Dr. Ioannidis calls the coronavirus pandemic “the perfect storm of that quest for very urgent, spectacular, exciting, apocalyptic results. And as you see, apparently our early estimates seem to have been tremendously exaggerated in many fronts.”

Does he think this is over already?

Scenario 1
Herd immunity when 60-70% has had it (Dr Osterholm) 320M * 0.6 = 192M
Infection fatality rate from recent NY study 0.7%
Total fatalities 192M * 0.007 = 1.3M
Add on for when the hospitals get overloaded and for indirect deaths.

Scenario 2
VP Pence said yesterday this will be behind us by Memorial Day (I assume he meant 2020).
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I posted this elsewhere but:
https://www.bbc.com/news/world-52425825

"There is currently no evidence that people who have recovered from Covid-19 and have antibodies are protected from a second infection," the WHO said in a briefing note.


Obviously it states "no evidence", but if true, that would seem to be terrible news. Anyone who had a tough time surviving a first round battle with the virus, would have to be terrified that they could get it again. Or if you had it once with minor symptoms but could get it again with a much worse outcome?

Lots of questions that still need answers. While life will go on for many, I don't see this behind us anytime in 2020 or things go back to normal for quite some time (i.e., not until treatments, vaccines and lots of answers are found).

Rich
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"There is currently no evidence that people who have recovered from Covid-19 and have antibodies are protected from a second infection," the WHO said in a briefing note.


Obviously it states "no evidence", but if true, that would seem to be terrible news.



Infection may or may not confer lasting immunity, obviously 4months of experience is not enough to settle the issue. But no evidence? For most viruses, no, for almost all viruses, infection results in the host producing antibodies (humoral immunity) and T-cell sensitivity (cellular immunity) that lasts at least a year, and usually years, and most often for life.

So far, I see no reason to doubt that this is also the case for the new coronavirus. Antibodies (IgG, IgM, IgA) are produced as expected, and reinfection seems to b rare, as expected. Some people have been testing positive a second time, sometimes even after testing negative on the PCR test, but they don’t seem to be sick, and it is not clear whether they hav persistent infections (perhaps with an initial false positive PCR test, or are shedding non-viable viral RNA (excreting dead virus from their previous infection), or havr reacquired an infection but with adequate immunity to prevent serious illness.

The virus may still mutate, escaping the immune response from initial infection, or immunity may lapse, as it does with some viral infections. We certainly lack the long-term experience to say. But to say there is ‘no evidence of protection’ is a huge overstatement, and even the WHO has felt obliged to walk that one back already.

dtb
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"There is currently no evidence that people who have recovered from Covid-19 and have antibodies are protected from a second infection," the WHO said in a briefing note.

Having read the briefing note, I think their key point was to make sure that governments don't
merely assume that antibody tests are a sign of immunity so powerful that they can risk lives on that reasoning. [Yet].
e.g., do you OK a recently sick doctor or cook going back in to work?
The antibody tests aren't quite perfect yet, and the immunity evidence isn't in yet, so don't rush.

So, cheer up.
It's probably also valid to note that there is no evidence that people who have recovered are NOT protected from a second infection.

About the worst that I've heard is that some people tested positive (presumed asymptomatic), then negative (presumed recovered), then later got very sick.
One leading theory is a fairly high rate of false negative PCR tests.
But it would be good to thoroughly rule out a second infection as the explanation.

Jim
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Many years ago a professor of engineering I admired very much, and an expert modeler using even fancier mathematics than the run of the mill modeler--he was an engineer who held patents for some of the early advances in digital computing--once pointed out that when we ignored a variable we deemed important because we didn't have data we were in effect giving it a value of zero, the only value we knew to be wrong.
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Having read the briefing note, I think their key point was to make sure that governments don't
merely assume that antibody tests are a sign of immunity so powerful that they can risk lives on that reasoning. [Yet].
e.g., do you OK a recently sick doctor or cook going back in to work?
The antibody tests aren't quite perfect yet, and the immunity evidence isn't in yet, so don't rush.

So, cheer up.



Don’t worry, I am cheerful already, as I see governments everywhere slowly coming to their senses and reversing course. They will save face by saying we had to control the epidemic before we opened up (thereby losing control again), but the important thing is that they stop the lockdown madness, not that they necessarily give the correct reasoning.

You can’t have certainty about any of the assumptions, including the pessimistic ones, but the operating assumption should be that most people who have been infected will be immune.

And yes, that includes doctors and cooks who are already routinely returning to work when their illness is over.

dtb
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Must Watch:

https://www.thegatewaypundit.com/2020/04/must-see-video-cali...

About 1 hour...worth every minute
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I am not sure which side of the debate I am on, but the following anecdotes are important consideration.
--
In Massachusetts

Troubling trend at Massachusetts hospitals that are seeing sharp declines in the number of patients seeking treatment
for serious conditions such as heart problems, renal issues and cancer.
Tufts Medical Center and Newton-Wellesley Hospital have both seen about a
50% drop in patients visiting their emergency departments, hospital leaders said Thursday.
Of the 18,000 hospital beds in Massachusetts, more than half lay empty, in a health system that is usually filled to about 80% capacity.
In fact only 3900 are occupied.

From a Doctor in London, UK

Normally in April around 30,000 people would be diagnosed with cancer.
This month I'll be surprised if it's more than 5,000.

https://twitter.com/ProfKarolSikora/status/12543629887835586...
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Seems certain that we are going to "open up" and that deaths will greatly increase and will be expected and tolerated. Purely from an investment perspective it seems also certain that as those with the mocho presentation experience personal connections or family/themselves to get the virus, that this will change views as to how to deal with this far more that distant calls for shutdown.

My next door neighbors here had a huge party yesterday, 21 kids at their lake home, They are of course of the political side that discounts the virus, they are very hard workers and having to cut back work has been simply intolerable. Being retired myself I fully understand, at their age I'd have been the same. Grandparents sat distant from the kids, but did much of the cooking and such.

I have family that is literally ready to EXPLODE with the social stuff, they are leaders and maintaining their standing/status is at the core of their very nature. They lead the business, volunteer, and charity groups in my area...they are super energetic wonderful people.

The virus is going to get wings in my view. My very simple thesis is that this spread is going to far more alter behaviors than any forced shutdown from some far away political authority. It is going to be interesting to see how this works itself out...of course if I live to see it! LOL!
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mungofitch: "There is currently no evidence that people who have recovered from Covid-19 and have antibodies are protected from a second infection," the WHO said in a briefing note.

mungofitch: "Having read the briefing note, I think their key point was to make sure that governments don't merely assume that antibody tests are a sign of immunity so powerful that they can risk lives on that reasoning. [Yet]."

That is where I have stood on this debate from the inception.

But DTB and other reasonable sounding posters seem to be very comfortable with making such an assumption.

ASSUMING (sorry for yelling with caps, I don't know how to italicize with my Fred Flintstone computer)... but assuming that people with antibodies are protected from a second infection, when there is an absence of evidence to support the assumption, and when you are talking about substantial suffering and loss of life if you are wrong, requires a value judgment about the value of human life when you enter the debate.

DTB staved off that line of debate a week or two ago when he said, "people will compare me to Hitler" or something like that, and that was a good tactic rhetorically speaking.

But I think there is a substantive difference at the core of this debate that is being glossed over and which is very real. Once you make that assumption then you are then predisposed to embrace the evidence that supports the assumption - i.e., you are more able to make the claim that the shutdown will kill even more people, and to accept facts that you think prove that claim, because you assumed it to be true in the first place.
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I am going to my urologist for a scheduled appointment tomorrow and am asking to be put back onto a medicine I formerly took for three years. Why, if I do not need this medicine am I asking for a prescription?

Because it may save my life as I have several factors working against me: history of IBD, late 60's male and type A blood. IBD (and many other autoimmune conditions) have a propensity for significantly higher IL-6 interleukin responses than the non-autoimmune general public. The link below is informative regarding many aspects of why Covid-19 affects some folks far worse than others and how some medicines appear to have beneficial aspects, especially in reducing the cytokine storm over-reactions of the immune system.

https://www.theatlantic.com/health/archive/2020/04/coronavir...

Everybody stay well as long as you can. Help is coming everyday with the medical community on full crowd-sourcing mode making cause and effect observations.

Uwharrie
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But DTB and other reasonable sounding posters seem to be very comfortable with making such an assumption.

Well, to be fair, I think there is a very useful distinction to be made between an expectation and an assumption.

As with DTB, I think we all expect there to be some useful amount of immunity in some large fraction of recovered patients.
Probably the WHO expects this too.
It's a good guess, barring signs of rapid functional mutation rates. (which have been mostly absent so far).

But it would not be appropriate to build life-critical laws around the assumption that this will be so until there is some evidence to support it.

Jim
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There is currently no evidence

Hate these ambiguous CYA kind of statement coming from experts. They are all clueless.
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But DTB and other reasonable sounding posters seem to be very comfortable with making such an assumption.

ASSUMING (sorry for yelling with caps, I don't know how to italicize with my Fred Flintstone computer)... but assuming that people with antibodies are protected from a second infection, when there is an absence of evidence to support the assumption, and when you are talking about substantial suffering and loss of life if you are wrong, requires a value judgment about the value of human life when you enter the debate.


Glad to hear I sometimes sound reasonable!

I think we can safely assume that people are immune to COVID-19 once they've had it, at least for a while (a year or so), or that if they are reinfected, they will have a mild case, perhaps asymptomatic. I don't think it is correct to say that there is no evidence for this: it is the case for almost every virus, people reliably produce antibodies against the virus as they do for others, and so far, people don't seem to come down with second bad cases of COVID-19, after nearly 4 months of experience. It is self-evident that we don't know that this will hold beyond 4 months, since the virus is only that old, but that is always true with new viruses.

What is the alternative to assuming immunity? For every new virus, every mutation of an influenza virus for instance, we basically assume that it will be like the other ones. We don't shut down the world every year when a new influenza virus appears, just because we don't have solid proof of long-term immunity to the new variant. We use experience with similar viruses, and judgment, and early experience with the new one, and it would require extraordinary new data to propose forcing people to stay at home for a year or so just to be sure the new virus isn't quite different from the usual pattern.

But more fundamentally, the argument against a shutdown does not hinge on this unanswered question about immunity. Even if some people DO come down with a second serious case, choosing to end the lockdowns requires no value judgment about the value of human life. Given the fact that there is unlikely to be any drug or vaccine that fundamentally alters the prognosis of this disease, at least within the next year or so, the judgment you have to make is whether a year-long lockdown, which could potentially be a multi-year lockdown, will cause more loss of life than the virus itself. It is a little hard to quantify the effect of bottling everyone up in their homes, causing financial ruin on a scale not seen for a hundred years, massive unemployment, bankruptcy of a large proportion of businesses, ruined government finances for generations, etc., but it is quite conceivable that this will be greater than the 0.2% of the human population which will be killed by COVID-19.

The fact that most of the deaths will be in people that are already in their last few years of life means that stringent control measures are quite likely to cause as many deaths, and cause the loss of many MORE years of life, than the virus itself. You can value life as highly as you like, but at some point, measures aiming to control this epidemic are likely to tip over into causing more suffering and death than doing nothing at all.

I don't advocate doing nothing at all: protecting the vulnerable, increasing treatment capacity, accelerating vaccine and drug development, slowing the propagation by reasonable physical distancing measures (including wearing masks), isolating cases, tracing contacts and quarantining them, staying at home when you have suggestive symptoms, all these are sensible ways of reducing the death toll by slowing the spread of the disease, without unduly damaging the 99.8% of humans that will survive COVID-19, or the 99.999% of humans under 50 that will, who also have a right to live.

DTB
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(sorry for yelling with caps, I don't know how to italicize with my Fred Flintstone computer)

Just put this string of 3 characters
< i >
(but scrunched together with no spaces) before the stuff you want to italicize, and put the (scrunched together) string < / i > afterward. (You can do Bold by replacing the "i" in the string(s) with the letter "b".) It will look like the example below, except with "i" instead of the "u":

<u>text you want to highlight</u>

Tails
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As with DTB, I think we all expect there to be some useful amount of immunity in some large fraction of recovered patients.
Probably the WHO expects this too.
It's a good guess, barring signs of rapid functional mutation rates. (which have been mostly absent so far).


I do not know the facts, but from what I see on the Internet, there have been one, two, six, seven, or 31 mutations of the virus so far and many are not very dangerous, and a few are extremely dangerous.
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I do not know the facts, but from what I see on the Internet, there have been one, two, six, seven,
or 31 mutations of the virus so far and many are not very dangerous, and a few are extremely dangerous.


I read a report on the genetic tree. (Forster et al.)
There have been mutations, but at a fairly slow rate.
There seem to be about three prominent parent clusters, with apparently fairly similar disease profiles.

Disease wise, the most notable distinction is that one of the three biggish clusters seems (?) to "stick" in Asia.
There are plenty of cases derived from it in various places, as with the other two clusters, but cases of that specific variation don't seem to spread outside Asia without at least one further mutation.
The maximum genetic "distance" between variations is about 40 point mutations, most of which so far seem to do little.
The distance to the closest known bat virus is about 17 changes.

Jim
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<testing>
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These are their estimated IFRs by age range.
Remember, this is percentage of infected people including asymptomatic, not percentage of disease cases or diagnosed cases.
Ages IFR
0-20 0.0490%
21-40 0.0176
41-50 0.0476
51-60 0.1076
61-70 1.0280
71-80 4.6620
80+ 9.0400


It is numbers like this that causes many people to question the wisdom of shutting down the country. Basically for anyone 60 and under, there is a 1 in 1,000 chance of dying. And even at 80+ it is just under 1 in 10.

Of course seeing hospitals being over crowded scares most people but how common was that? In NY probably very common, ditto for parts of Italy, but what about most cities?

As a comparison, a 40 yr old man has a 0.24% chance of dying in a year.
https://www.ssa.gov/oact/STATS/table4c6.html#fn1

I'm probably missing something (a lot?) here.
Rich
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It is numbers like this that causes many people to question the wisdom of shutting down the country. Basically for anyone 60 and under, there is a 1 in 1,000 chance of dying. And even at 80+ it is just under 1 in 10.

...

I'm probably missing something (a lot?) here.
Rich


No, you’ve pretty much nailed it.

We’ve had 1599 deaths in Quebec, 1 of them below 40 years old. It’s basically a non-issue for the young half of the population, and no big deal if you’re under 70 and have no major health issues. Older, sicker people should be protected, but 80% of the population should just go ahead and let themselves be infected and get it over with.

They should do this over the course of a few months, not all at once. The current infection rate in most of Europe and the USA is actually too slow, but some distancing makes sense for another couple of months, so it doesn’t go too fast. Sweden has got this about right, but almost all other countries will regret causing so much harm for so little benefit.

dtb
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About Sweden.... my friends there have clued me in about how non Swedish media are misinterpreting what Sweden is doing.

Most of the reporting about Sweden does not take into account the inference of words when translated.

For example: my friend's office was told that they "may" work from home. Cathrine told me that "may" from her government had the same real meaning as "should". All native Swedes, immediately took the "may" as the full US meaning of "should".

She contacted me and was very worried because the government reiterated the work at home notice with a "should". Should has a stronger meaning than here. She never expects to hear her government ever say, "citizens must" do such and so.

The cultural/language differences are causing problems with immigrants from Italy, who argue that the word "should" has no meaning, because it gives them an option to continue as they please.

Perhaps, we outside of Sweden misread some of Sweden's instructions like their immigrant population?

Only in Stockholm are some young people ignoring instructions. My friend said Swedes normally stand 2 meters apart....?!

Habits and culture differ and translations can skew the results.

IMHO....
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DTB - are you in a medical field ?
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yes, I am an md working in the obscure field of public health...
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OK, need 10 mins of your time to hear this and see if this seems like snake oil.

Background:
Dr. Bruce Patterson talks about a drug called Leronlimab by *Cytodyn (a small company).

They filed a BLA for HIV drug today. They also stumbled on the fact that this drug may help with Covid19. There is an ongoing FDA clinical Phase 2b/3 trail.
Dr. Bruce Patterson is claiming that Covid19 is a "Rantes disease" and
is publishing emergency IND results in NEJM.

https://78449.themediaframe.com/dataconf/productusers/cydy/m...

11:00 - 21:00

*I am a shareholder of this highly speculative stock
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sounds like hype but who knows? i would read a transcript but i’m not going to listen to a presentation

dtb
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