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Just curious, anyone have a plan, or preps for global pandemic?

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COVID-19 Numbers Are Bad In Ecuador. The President Says The Real Story Is Even Worse

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Ecuador has one of the highest rates of COVID-19 in all of Latin America – with 10,128 cases and 507 deaths in a country of just 17 million people.

But the situation may be far worse than what the official numbers show. In fact, one Ecuadorian official says it appears that thousands more people may have died of the disease than his government is reporting.

Jorge Wated, who heads a government task force charged with collecting and burying bodies in Guayaquil, the epicenter of the outbreak, said that in the first 15 days of April, 6,703 people had died from either COVID-19 or natural causes. Previous to the coronavirus outbreak, he said, the monthly figure was around 1,000.

 

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Wrt. wrong predictions etc., there's also a well-known tendency of people feeling competent despite having few objective reasons for that feeling (and of course, we're all familiar with the Dunning-Kruger effect). There's a particular personality type that feels impelled to express opinions (very prevalent type online!) even as they have no grounds to do so. Anyhow, I saw a humorous remark somewhere to the effect: "I see that all of January's Constitutional experts have now become epidemiologists by March".

Meanwhile there's a fascinating book on the subject of people's expertise and predictions:

https://davidepstein.com/the-range/

The basic idea is that an individual is actually LESS able to accurately predict events within their field of expertise, as well as outside of it, as that individual's knowledge grows - which is extremely paradoxical. Research shows that the reason for this is that old bugaboo, the "sunk cost" fallacy - essentially, as your expertise grows, you develop a more and more elaborate (and intellectually costly to maintain) framework to see your field through. When you are attempting to make a prediction, you naturally gravitate to your very costly (sunk costs) developed framework for guidance rather than look at the data anew. Basically, imagine that you've built a round hole framework - along comes a square peg... and of course rather than spend a huge amount of intellectual capital developing a brand new square hole framework, you try to twist the peg into a round hole, with disastrous results for your prediction accuracy.

This is also the reason why large crowds of amateurs - "crowd wisdom" - have traditionally absolutely crushed in accuracy the predictions of solitary "experts" - because they have less attachment to any one framework they can draw upon a very wide variety of knowledge to arrive at a more accurate prediction.

Anyhow, I think this subject is super interesting - and something we all can benfit by. Being less sure of one's conclusions and more sef-questioning can only improve one's performance. YMMV.

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Covid-19 causes sudden strokes in young adults, doctors say

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[...] Dr. Thomas Oxley, a neurosurgeon at Mount Sinai Health System in New York, and colleagues gave details of five people they treated. All were under the age of 50, and all had either mild symptoms of Covid-19 infection or no symptoms at all.
 
"The virus seems to be causing increased clotting in the large arteries, leading to severe stroke," Oxley told CNN.
 
[...] It is not common for people so young to have strokes, especially strokes in the large vessels in the brain.

 

French researchers to test nicotine patches on coronavirus patients

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[...]The renowned French neurobiologist Jean-Pierre Changeux ...suggested the nicotine might stop the virus from reaching cells in the body preventing its spread.

Nicotine may also lessen the overreaction of the body’s immune system that has been found in the most severe cases of Covid-19 infection.

Five threats to US food supply chains

  • Virus outbreaks at food plants
  • Agricultural reliance on guest workers
  • Supply chain mismatches
  • Increased food insecurity
  • Crunch on delivery capacity

 

 

Edited by Sibiriak

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This may be why the stock market seems to go up every Thursday when the massive new unemployment numbers are released: It means all those people are now getting an extra $600 per week and a lot of them are likely making more money than they did while employed. I believe this extra money continues through July 31.

 

She got a forgivable loan. Her employees hate her for it.

https://www.cnbc.com/2020/04/22/she-got-a-paycheck-protection-loan-her-employees-hate-her-for-it.html

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Here is an interesting NY Times article discussing the fact that case and death curves in Europe and the US aren't turning out to be symmetric like many of the models have been suggesting they would be. That is, the new cases and deaths aren't coming down nearly as quickly as they went up, likely as a result of the non-pharmaceutical interventions as implemented in the west haven't reduced the virus' reproduction rate much (if any) below 1.0. Here are some salient sections:

Even as they are updated, though, many of the models remain symmetric. Reality isn’t.

Let’s start with new cases. According to the Johns Hopkins Coronavirus Research Center, Italy had its worst day of new cases (6,557) in mid-March. A month before, it had almost none. More than a month later, it’s still having thousands a day. Spain started seeing an increase in cases in the beginning of March and peaked about three weeks later at 9,630. About a month after that, it’s still finding around 4,000 a day. Belgium rose and flattened, not dropped. So did the Netherlands.

Italy’s deaths peaked at the beginning of the month, slowed a bit, but have leveled off in the last week or more. Britain peaked about a week ago and has held somewhat steady. So has France. So has Germany. Spain, while coming down, is coming down more slowly than it went up.

What’s worrisome is that none of these countries have seen a rapid decline to mirror their rapid increase. The United States, of course, really hasn’t seen any significant decline at all.

Yet many are acting as if the original models still hold. Since they are passing their peak, they believe that it’s time to start talk about loosening restrictions.

That’s a big mistake.

In order to contain a future outbreak, a city, state or country needs to be able to identify and isolate enough cases to prevent the “effective R” — the number of secondary cases per infectious case — from getting above one. This means that every infected person has to infect no more than one other. Achieving that goal would keep the numbers of cases showing up every day from increasing.

Most of the models, though, assume an effective R that remains well below one after the peak. Good social distancing policies do that, and when modeled, that’s how we see rapid declines.

Few models, if any, predicted the flattening or stalling we’re seeing in Europe, though.

It’s not clear why this is so. It could be that even though policies have remained unchanged, people have been sporadically relaxing their efforts. We’re really not sure.

What we are sure of, though, is that the rate of transmission isn’t low enough to think we’ve succeeded in getting a hold of this pandemic.

“We’ve engaged in policies that have slowed the number of cases,” said Ashish Jha, a professor of global health and medicine at Harvard. “We could have chosen to smash the curve, not flatten it. That would get us to a much more manageable place to reopen.”

“Exiting too soon is like thinking you can cut your parachute off at 2,000 feet because it slowed you down,” said Carl Bergstrom, a biology professor at the University of Washington.

It would be better to wait. The ride down from the peak appears to take much longer than the ride up. When the realities don’t fit the models, it’s time to re-evaluate the models’ usefulness.

It is becoming increasingly clear that it is neither politically or economically feasible in the US to wait as long as would be necessary for new cases to drop to the point where contact tracing and testing of all new cases becomes feasible as the author suggests. 

Time will tell the consequences of restarting the economy before that point. It seems very likely that new cases and deaths will surge. But in the long run that may be inevitable regardless of what we do, and it's possible that a surge over the summer could blunt and even bigger surge in the fall/winter, like the huge second wave that occurred in the fall/winter of the 1918 pandemic after a comparable small peak in the spring.

--Dean

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17 hours ago, TomBAvoider said:

Wrt. wrong predictions etc., there's also a well-known tendency of people feeling competent despite having few objective reasons for that feeling (and of course, we're all familiar with the Dunning-Kruger effect). ...

Meanwhile there's a fascinating book on the subject of people's expertise and predictions:

https://davidepstein.com/the-range/

...This is also the reason why large crowds of amateurs - "crowd wisdom" - have traditionally absolutely crushed in accuracy the predictions of solitary "experts" - because they have less attachment to any one framework they can draw upon a very wide variety of knowledge to arrive at a more accurate prediction....

More BS to denigrate and silence anyone who points out the absurdity of the predictions which were the basis for the lock-downs.  How else can you explain why is part of the world under a lock-down, yet the total number of Covid-19 deaths so far is less than 200,000, compared to the 1,200,000 who died from the flu two years ago.  In other words, in 2020 a lot fewer people will die from the flu and Covid-19 combined, than from past pandemics during which no lock-downs were imposed.  But the major Western economies are in shambles, because bad, populist decisions were made based on bad data.

Large crowds of amateurs are often wrong. The vast majority of the world's human population is deeply religious and their lives are governed by superstition and ritual. In the US, close to 70% believe that ghosts exist. And all of these people seek guidance from "experts" who are specialists in the "word of god."  And they scoff at, and often employ violence against those who challenge the prevailing dogma.  So much for "crowd wisdom."

Edited by Ron Put

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34 minutes ago, Dean Pomerleau said:

Time will tell the consequences of restarting the economy before that point.

I've said it before, but now we have actual evidence, this is what "reopening" looks like:

German shoppers not rushing back as stores reopen
I expect the same results they are seeing in Germany.  (Stock market remains dangerously overvalued and prone to deep losses)

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map-of-finland-and-sweden.jpg

tn_sw-flag.gif
 Sweden (200 deaths per 1 M population)

Coronavirus Cases:16,755 Deaths:2,021

tn_fi-flag.gif
 Finland (31 deaths per 1 M population)

Coronavirus Cases:4,284 Deaths:172

tn_no-flag.gif
 Norway(36 deaths per 1 M population)

Coronavirus Cases:7,361 Deaths:193

I'm still very interested in how things play out longer term.  Just because it looks kind of bad for Sweden now, doesn't mean things won't just eventually "even out".  Based on interviews with their head policy advisor, it seems the mistaken assumption they made was that asymptomatic transmission was negligible.  Growing consensus suggests this idea is wrong.

Anger in Sweden as elderly pay price for coronavirus strategy

It will be interesting to compare the relative differences in GDP for the next 4-8 quarters between these three countries.

Edited by Gordo

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From the US update for today on worldometer:

New York State Governor Cuomo said that preliminary findings from an antibody study conducted on 3,000 people at grocery stores across New York State found a 13.9% had coronavirus antibodies, suggesting a 13.9% actual infection rate statewide (21.2% in New York City), which translates to an estimate of about 2,700,000 actual cases in New York State (10 times more than the about 270,000 cases that have been detected and reported officially). Governor Cuomo acknowledged that the official count reported by New York State (which still is not including probable deaths as recommended by the new CDC guidelines) of about 15,500 deaths is "not accurate" as it doesn't account for stay at home deaths. Based on Worldometer's count (which includes probable deaths reported by New York City) of about 21,000 deaths and the 2,700,000 case estimate from the new antibody study, the actual case fatality rate in New York State could be at around 0.78%.

That 13.9% actual infection rate for NY state is less than half the 30% infection rate estimated for that other hard-hit area, Chelsea MA. The "case fatality rate" estimate of 0.78% based on this antibody study is very likely an underestimate for three reasons.

One I pointed out in my analysis of the Chelsea data, namely that the long tail of the mortality curve will mean more than the current 21k people will die from the cohort corresponding to the 2.7 million infected so far in NY.

Second, the sampling they did for this NY study wasn't truly random. It was performed by randomly sampling 3000 people in 40 grocery stores around the state. People who go to grocery stores are those willing and able to put themselves more at risk of contracting the virus or perhaps who know or suspect they already have contracted the virus and recovered compared with those too scared or unable to leave their home (or e.g. nursing home). So the sample almost certainly overestimates the true fraction of people in the total population who have previously been infected by the virus.

Third is that people (including me up until today) have been conflating two death rates - the case fatality rate (CFR) and the infection mortality rate (IFR). From wikipedia, the CFR is defined as follows:

case fatality rate (CFR) — sometimes called case fatality risk — is the proportion of deaths from a certain disease compared to the total number of people diagnosed with the disease for a certain period of time. 

In contrast:

The term infection fatality rate (IFR) also applies to infectious disease outbreaks, and represents the proportion of deaths among all the infected individuals. It is closely related to the CFR, but attempts to additionally account for all asymptomatic and undiagnosed infections.[7] The IFR differs from the CFR in that it aims to estimate the fatality rate in all those with infection: the detected disease (cases) and those with an undetected disease (asymptomatic and not tested group).

So in actuality, it is the IFR (not the CFR) that is estimated to be 0.78% (at least) by this analysis since it based on an estimate of all those with a previous infection based on antibodies. To accurately estimate CFR requires being able to test all symptomatic people to determine if they have the virus, something we are far from able to do at this point. But it will undoubtedly be a lot higher than the IFR of 0.78%.

People who say this is no worse than the flu are therefore doubly wrong. First, 0.78% is already far higher than the 0.1% estimated mortality rate for the seasonal flu. In addition, as far as I can tell, the 0.1% statistic for seasonal flu is the CFR not the IFR, since we don't do the equivalent serology test to determine the actual percentage of the population who gets the flu every year and are asymptomatic. The IFR for seasonal flu if it were to be calculated would be much lower than 0.1% as pointed out somewhere in the middle of Peter Attia's recent podcast on the Spanish Flu, which is what got me thinking about IFR vs. CFR.

This can be clearly shown in the CDC data itself. For example, during the worst flu season in years (2017-2018) the CDC estimates there were ~45 million symptomatic illnesses and ~61k deaths, resulting in the oft-reported 0.13% case fatality rate. In the paper describing the CDC's methodology for determining "symptomatic illnesses", they do adjust for (i.e. try to include) people with flu symptoms who chose not to visit the doctors office. But there is no attempt to account for the many millions more who likely contracted the flu but were asymptomatic. So comparing the 0.1% CFR for the flu (which excludes asymptomatic people) with the 0.78% IFR for coronavirus (which includes aymptomatic people), is like comparing apples to oranges.

But the bottom line couldn't be more clear. Even hard hit New York City, with 21% of the population previously infected, is a factor of ~3x lower than that required for herd immunity.

--Dean

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39 minutes ago, Gordo said:

I told you guys last week's Remdesivir "news" was a fraud (why can't journalists do their jobs?), now it's official.

Last week's leak may have been premature and even a plant as you suggested, but it seems premature to rule out the usefulness of remdesivir based on this study.

In particular, the same (valid) criticism of my analysis of the veterans hydrochloroquine trial applies here. While remdesivir may not be helpful when administered to patients with "severe" covid-19 (as the study abstract describes their test subjects), it might be helpful when given earlier in the course of the disease.

--Dean

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1 hour ago, Dean Pomerleau said:

....

People who say this is no worse than the flu are therefore doubly wrong. First, 0.78% is already far higher than the 0.1% estimated mortality rate for the seasonal flu....

Garbage in, garbage out. What makes you think that these numbers are "true" compared to the numbers previously reported, on which you built your earlier, and terribly wrong, predictions? The usual infection rate from the flu is about 20% per season, before it dies down. Which appears to be roughly where we seem to be going with Covid-19. And which is roughly where all the other corona virus epidemics we've had this century ended up, too, before they became endemic and were lumped in with the general "flu" counts. Which is very likely to happen with Covid-19, once the scaremongering and the political fighting over the enormous mistakes made are over.

The CDC takes about two years to come up with a definitive count of attributable deaths. This was true for the 2017-2018 flu season, which was preliminary pegged at about 80,000 and then finally reduced to 61,000.

Right now, excess mortality rates are lower than previous years for the US as a whole and for Europe.  Which should at least give you pause.

---

1 hour ago, Gordo said:

Just because it looks kind of bad for Sweden now....

Gordo, I notice the glaring exception of Denmark. Is it because Denmark, despite the lock-down, has a much higher proportionate mortality rate than Norway or Finland? Since both Denmark and Norway were locked-down, how do you explain Denmark's considerably higher mortality rate?  And why do you think the small upward blip in Sweden (2000 total Covid-19 deaths, compared to the usual ~100,000 annual mortality rate and the much larger influenza mortality) is directly attributable to the lack of a lock-down?

Or, just look a little to the South-East and ponder the miracle of Byelarus? :D Or, are we going to argue that the Belorussians are so much more civilized than the rest of us, just like the Swedes apparently are?

Edited by Ron Put

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As I understand it, states are paying their normal unemployment benefits to people out of work due to covid-19 stay-at-home orders and the federal government is topping that off with an additional $600 per week.

That's a lot of money for states to be paying out, especially given the dire fiscal situation many states now find themselves in and the huge number of people out of work. Does anyone else think the push to reopen businesses is a way for states to push people off of unemployment? Apparently, if a state lets businesses open up and a worker refuses to return to their job out of fear of the coronavirus, they will have effectively quit and will therefore no longer be eligible for unemployment. 

Reopening before the federal guidelines suggest in order to save money on unemployment benefits seems like a pretty cruel thing for (Republican) governors to be doing, but it seems like it might be at least a part of their motivation.

From this VICE article on the topic:

Anne Carder, managing attorney at the Atlanta Legal Aid Society, told VICE that while she doesn’t have a firm answer yet, she’s “99.99 percent sure” that people won’t be eligible for unemployment benefits in Georgia if their businesses reopen and offer them work, but they don’t want to return because of general fears of getting sick. And, when it comes to gray areas in a person’s unemployment case, it’s up to state unemployment offices to decide, and those in conservative states can be more stringent. While one could appeal a decision, that could take a long time right now, given that unemployment offices are already backed up.
The businesses that are reopening are not clearly essential. For example, in Georgia, Governor Brian Kemp said that gyms, bowling alleys, and hair salons could reopen as early as Friday. There are a number of reasons why someone might think it’s not worthwhile to go back into these jobs and put their own health and the health of their loved ones in danger. But they might have to, or risk not getting any unemployment benefits at all.

Once again it seems like the average worker may soon get screwed.

--Dean

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The number of deaths from corona at this point match suicide rates in 2017. Of course we are in the midst of the storm, but I will say if this were terrorists killing 40,000 Americans we would be taking that very seriously. Hmmm.....perspective is a very weird phenomenon or as Shakespeare might have it

Why, then, 'tis none to you, for there is nothing either good or bad, but thinking makes it so. To me it is a prison. Well, then it isn't one to you, since nothing is really good or bad in itself—it's all what a person thinks about it.”

Data are for the U.S.

  • Number of deaths: 2,813,503
  • Death rate: 863.8 deaths per 100,000 population
  • Life expectancy: 78.6 years
  • Infant Mortality rate: 5.79 deaths per 1,000 live births

Source: Deaths: Final Data for 2017, tables 1, 3, 13 pdf icon[PDF – 2 MB]

Number of deaths for leading causes of death:

  • Heart disease: 647,457
  • Cancer: 599,108
  • Accidents (unintentional injuries): 169,936
  • Chronic lower respiratory diseases: 160,201
  • Stroke (cerebrovascular diseases): 146,383
  • Alzheimer’s disease: 121,404
  • Diabetes: 83,564
  • Influenza and Pneumonia: 55,672
  • Nephritis, nephrotic syndrome and nephrosis: 50,633
  • Intentional self-harm (suicide): 47,173

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Speaking of Sweden, the Swedish Public Health Agency apparently withdrew a paper intending to explain the justification for their maverick strategy due to an error in their analysis. From this Forbes article on the subject:

While the nature of the error was not disclosed, the Agency also said they would share more information about it once the revised report was complete.

During yesterday’s press conference, the deputy state epidemiologist Anders Wallenstein announced the report based on data models that indicated the spread of the coronavirus was much higher than previously thought. Among the claims were that for every one person tested positive for COVID-19, up to 999 others could also have the infection without knowing.

Swedish journalist Emanuel Karlsten queried this during a Q&A session at the press conference. Given there are more than 15,000 confirmed cases, the Agency’s estimate would mean that the total number of likely infections would be greater than the entire population of Sweden.

Oops. More realistic ratios of infected to diagnosed people at least in the US are in the neighborhood of 10-15x based on the serology studies we've discussed from New York and Chelsea.

New York has tested 4x per capita more than Sweden. If we (very generously) quadruple the number of positive cases Sweden has discovered to account for their lack of testing, Sweden would have ~16k*4 = ~64K diagnosed cases. If we use New Yorks 10x ratio of actual to diagnosed cases, that would mean Sweden would likely have 640k actual cases. 

Given Sweden has a population of 10 million people, that would mean 6.4% of the Swedish population would have been infected so far. That is ~10x short of herd immunity. With 2000 deaths so far, it would therefore take ~20k total deaths for Sweden to reach herd immunity. With 10M people, that is a death rate of 2000 per million citizens, which is ~4x the death toll that any country has experienced so far.  Given the US population is ~35x that of Sweden, their 20k deaths would equate to 20k*35 = 700k deaths to reach herd immunity in the US, assuming our hospitals aren't overwhelmed.

Note - I'm not saying that accepting such a high death toll in order to reach herd immunity is necessarily the wrong strategy. If all countries are ultimately going to have suffer through enough deaths to reach herd immunity (e.g. if a vaccine is very far off) and Sweden can handle the rate of hospitalizations they are experiencing now and importantly will experience over the remaining ~9/10th of the path to herd immunity, then their strategy of sucking it up and not shutting down the economy makes perfect sense. But the available evidence suggests they likely still have a long way to go.

It will be interesting if/when Sweden reports serology tests on their population to see just what fraction of people have actually been infected. 

--Dean

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59 minutes ago, Mike41 said:

The number of deaths from corona at this point match suicide rates in 2017. Of course we are in the midst of the storm, but I will say if this were terrorists killing 40,000 Americans we would be taking that very seriously. ....

Perhaps. Yet, there are about 33,000 annual deaths from firearms (11,000 or so murders, the rest accidents) and we do little about it.  Or, if we drop the speed limit to 50 mph, we can save about 15,000 lives a year, yet we also choose not to do it. Obesity kills or disables millions, yet we actively promote it.

Every few years a bad "flu" season kills considerably more people than Covid-19 has, yet we don't shut down our economies to reduce the death toll.

But you are absolutely right: It's all about perspective and when panic is created, our collective perspective gets grossly distorted. And then we make and justify really bad decisions based on such gross distortions.

 

29 minutes ago, Dean Pomerleau said:

Speaking of Sweden, the Swedish Public Health Agency apparently withdrew a paper intending to explain the justification for their maverick strategy due to an error in their analysis. ... Oops.

--Dean

Yeah, and you withdrew your predictions of a quarter of a million to half a million dead in the US from Covid-19, which was the justification for the lock-down.

Oops.

But damn, those Swedes must be really incompetent and stupid, right?

Edited by Ron Put

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4 hours ago, Dean Pomerleau said:

But the bottom line couldn't be more clear. Even hard hit New York City, with 21% of the population previously infected, is a factor of ~3x lower than that required for herd immunity.

--Dean

Actually, the only thing clear is that you misunderstand how herd immunity is achieved and that the required infection/immunization rate is highly variable, depending on the cause.

"The required percentage that would have been required to establish herd immunity against previous influenza viruses ranged from 13% to 100% for the 1918–19, 1957–58, 1968–69 and 2009–10 pandemic viruses, and from 30% to 40% for the 2008–09 epidemic virus."

https://www.sciencedirect.com/science/article/pii/S0091743512000588


As I have repeatedly noted above, the seasonal flu generally disappears when infection rates rates reach between 15% and 40%. There is no particular reason to think that Covid-19 is dramatically different than the "flu" (which nowadays includes four other corona viruses which emerged in the last two decades), thus it is highly likely that some of us will be sick with Covid-19 during the next season, and the next, and the next. It is also far from certain that we'll have a vaccine in the next decade.

So, Covid-19 is likely here to stay.  I only hope that the voters are smart enough to make sure that the "leaders" who destroyed the economy based on bad data are gone after the next election cycle.  

Edited by Ron Put

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A new study [1] of 5700 covid-19 patients in NY found that ventilators were surprisingly ineffective.

Of the 5700 patients initially enrolled, 2634 either were discharged or died during the study. Of those, 373 were admitted to the ICU and most of those (320) were put on a ventilator.  Overall, 21% of the 2634 patients died. But the death rate was 88% for those unfortunate enough to require mechanical ventilation.

--Dean

------

[1] Richardson S, Hirsch JS, Narasimhan M, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. Published online April 22, 2020. doi:10.1001/jama.2020.6775

Abstract
Importance  There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).

Objective  To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system.

Design, Setting, and Participants  Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates.

Exposures  Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission.

Main Outcomes and Measures  Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected.

Results  A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/minute, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. Mortality for those requiring mechanical ventilation was 88.1%. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1).

Conclusions and Relevance  This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

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Last video I posted featuring Dr. John Ioannidis (  looks almost identical but it was "Episode 1") he had called for more data -

Now (Episode 4)  he has more data - some based on his own research and provides a - in my assessment - fairly balanced evaluation with his take on its implications, where we are, and considerations going forward.  Worth a listen:

 

 

#2

Dr. David Katz has been a controversial figure in the nutrition and health policy space.  Regardless how you feel about him or his nutritional assessments, I found this thought-provoking, and he also raises some of the same questions with regard to the balance required in a cogent opening up / health mitigation strategy:

 

 

 

 

Edited by Mechanism

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I think this link was posted earlier in the thread, but I just found the article on Ars Technica and read it:

The value of lives saved by social distancing outweighs the costs

"Social distancing means a net benefit of $5.2 trillion, according to the analysis."

It is extremely important to get some kind of idea about the cost of the economic disruption under different scenarios of containment, so naturally, such analysis are of great interest. However, I find this particular analysis (as presented by Ars Technica) very troubling. 

It seems to me extremely prone to the GIGA problem and if one can't agree on the basic number inputs, then the analysis is going to be hopelessly skewed. 

It all starts with the key figure here of the "value of a life" in dollar terms. As defined it seems to me worthless insofar as trying to measure economic worth. Before we go any further, let me state that I don't personally put $ amounts on a human life and this is not a moral but purely economic question. 

Here is how they came up with the $10million/per life value:

"US federal agency guidelines have needed to put a price on life in order to set policy on things that sometimes kill people, like driving. To do this, they use a figure that estimates how much extra money people will pay to save an additional life. For instance, take the higher pay that comes with riskier jobs: when you look at how much extra a group of 10,000 workers gets paid when their job comes with a higher risk, it comes out to around $10 million for each additional probable death in the group.

That figure of $10 million, the so-called “value of a statistical life,” is the figure used by federal agencies, and it’s also the figure used by economist Linda Thunström and her colleagues when they calculate the cost and benefit of social distancing.

Waa? Come again? This is some kind of BS that's special. So it is not actually a calculus on how much economic productivity to expect from a statistical person over time minus cost of maintenance, retirement etc.? Instead, it's some kind of estimate of what other people might pay to save a life? What is the relationship between that willingness to pay and actual economic worth of a life? Nothing as far as I can see. It's obviously complicated, which is why I think people have resorted to this dodge - but it's still a dodge and completely made up number.

With a number that's essentially arbitrary, I have zero faith in the rest of the analysis - the poster boy for GIGA.

Instead, I wonder about the economic impact of this disease if it's seen in demographic and fiscal terms. 

One of the reasons why I find comparisons to the 1918 flu so troubling insofar as economic impact analysis, is that CV is a very radically different disease that happened to a very different economy and a different social environment. There are broadly three classes of concern:

1) The 1918 flu hit "by and large" young people and people in their prime of economically productive life - CV hits people "by and large" who are elderly, retired or at the twilight of their economically productive life (number of years). This strikes me as a monumental difference. 

2) The economy in 1918+ was much more labor based compared to today. Consequently, a massive loss of human labor took a real chunk out of economic production. Today, with automation and non-human productivity inputs (automation, robotic, information processing etc.) a loss of human labor has a very different impact - it can be compensated and worked around much faster than 100 years ago. 

3) The loss to the economy on the demand side in 2018 was radically different from today - you had no unemployment insurance, social security, medicare and other social safety nets. Obviously today the government is much more responsive in purely fiscal ways, so the economic impact will be very different.

For these and other reasons, I think we should look with extreme scepticism on any lessons to be drawn from the economic impact of the 1918 flu for CV today.

Getting back to the $10million question - we can broaden this to the question of economic impact per person in demographic terms. If indeed CV mortality is the greatest among the retired or close to retired, then there is relatively less productivity lost - the $10million figure is useless, it might be much lower than $10m, all the way down to $0. As has been pointed out by statisticians and budget analysts, large numbers of old people dying can very easily translate into a massive net financial gain - curtailing social security and medicare costs, and even freeing up capital (passing it on to younger heirs who are more economically active and liable to spend and put it into the economy etc.).

Far from CV being a "cost" in a lost life in this scenario - it is a benefit. Hence the $10m figure is completely misleading. Again, I'm not arguing "for" anything in moral terms, I'm just looking at the economics - I personally think every life is precious.

CV also has elevated mortality for folks who have severe - and often multiple - co-morbidities. If large numbers of sick people of any age die and most "healthy" people don't - that is a net saving from the point of view of further medical costs down the line as co-morbid people represent a higher cost of medicare and healthcare in general. Again - for this group of people, on a population level CV far from being a "cost" might transpire to be a benefit - so the $10m figure is misleading.

CV also apparently strikes certain groups of people at a significantly higher rate - let me make yet again crystal clear that I do not in any way imply there is some kind of inherent difference in the worth of human lives based on such criteria, I DO NOT, I'm just looking at it from a purely economic view. If those populations on average contribute less to economic wealth, and might cost more, then CV works to "benefit" not cost, or at least the "cost" effect is attenuated. For example, if the population of Togo in Africa were substituted with Norway's population (roughly equal population sizes) - the economic productivity "worth" per capita (wealth generation) would be at extreme variance. Again, in this scenario, the blanket $10m figure per person makes no economic sense. Meanwhile we know that rates of CV deaths are 2.5 to 3 times greater in some populations in the U.S. - making it so the impact of CV absolutely cannot be substituted 1:1 in economic terms.

The bottom line to this is that I don't think we have a good grasp on the ultimate cost of this pandemic. Undoubtedly it will be gigantic and unquestionably tragic as every life is a priceless gift. But the decision as to whether to go for a Swedish style reaction, a more extreme form of lockdown or anything in between, and the decision as to when to re-open the economy cannot be taken if we don't have reasonable data on the likely economic impact. Comparisons to the 1918 flu don't hold water IMHO. I therefore see the kind of analysis highlighted in the Ars Technica link as fatally flawed - there are massive variables being ignored or indeed twisted: it has almost no value other than as a starting point of discussion. YMMV.

 

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3 hours ago, Ron Put said:
4 hours ago, Dean Pomerleau said:

....

People who say this is no worse than the flu are therefore doubly wrong. First, 0.78% is already far higher than the 0.1% estimated mortality rate for the seasonal flu....

Garbage in, garbage out. What makes you think that these numbers are "true" compared to the numbers previously reported, on which you built your earlier, and terribly wrong, predictions?

Dean has a good track record of carefully building cases for his opinions supported with references and while there are times I'm not in full agreement with his conclusions I'd be hard pressed to find something he has written I'd be willing to argue was "terribly wrong".   By contrast you have made no case at all for your accusation that he has made terribly wrong predictions.  What are the predictions in Dean's exact words and how are they terribly wrong?

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3 hours ago, Ron Put said:

 

3 hours ago, Dean Pomerleau said:

Speaking of Sweden, the Swedish Public Health Agency apparently withdrew a paper intending to explain the justification for their maverick strategy due to an error in their analysis. ... Oops.

--Dean

Yeah, and you withdrew your predictions of a quarter of a million to half a million dead in the US from Covid-19, which was the justification for the lock-down.

What is the prediction Dean withdrew?  Quote the words.

The justification for efforts to limit the spread of Covid-19 are the possibilities of what might occur with insufficient action.  That the worst of the possibilities hasn't yet occurred is NOT a failed prediction.

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Tom, I agree with this author that “using a population-wide average VSL standard for assessing COVID-19 policies is inappropriate insofar as such an average does not accurately capture the age distribution of expected COVID-19 decedents.”  This was one of your points.

When I was in grad school it took me a little time to wrap my brain around the QUALY - which is not a measure of economic impact of a lost life ( as you pointed out, a lost life can be net savings to society notwithstanding the tragedy - that is another matter ), but rather a societal willingness to lose $ per lives saved and moreover in systems with constrained resources it attempts to provide an objective paradigm for trade offs such as for determining the relative merit of investing in public health measure 1 vs. public health measure 2.  Society needs some way to maximize good and comparable metrics are the best we have right now, as unfair as it feels to choose one protective program over another.
 

I agree with you about the problems with VSL, i feel QUALY analyses are more suitable.  If there is a better system than QUALY analyses ( not done in your cited study), I am not aware of it, but would be interested in learning and being enlightened.

https://www.forbes.com/sites/theapothecary/2020/03/27/how-economists-calculate-the-costs-and-benefits-of-covid-19-lockdowns/#37b4207c6f63

Edited by Mechanism

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