One thing about Coronavirus media coverage is, has always been, and remains: “Don’t trust the media.” Their reporting has been histrionic from the start, and really equivalent of “yelling ‘fire’ in a crowded theater.” The Panic Pandemic may not be entirely a creation of the media, but the media was the fanners-of-the-flame-in-chief.
“How many people have gotten the virus?” is a very important question but one on which the media had always obfuscated or given bad info, confusing confirmed cases with actual cases. This itself is of the central swindles of the entire Panic.
Needless to say, as we of the anti-Panic side have said all along, the actual number of those exposed to the virus is much higher than the “confirmed cases” reported. It’s a respiratory virus. It spreads between people in close contact. Get over it.
The swindle is this: Getting people to think of ‘Corona’ as a rare and terrifying killer-virus itself reinforces the Panic. Common things are not terrifying. Ironically, then, it is this deflation of the virus total that has been one way they were able to heat up the Panic and perpetuate it.
The answer to the question of “How many have gotten the Virus” is “many tens of millions,” and has been for a long time now.
Somebody ranting about an imminent virus apocalypse sounds less scary (more pathetic) if you know that tens of millions have gotten said virus and recovered. We need not heed his dark visions of doom the next time:
Major US antibody study finds tens of millions of Americans have gotten the virus, easily recovered
Thanks to a large-scale antibody study in the US, we can finally provide fairly firm data on how many people really got the virus in the US on a nationwide basis.
In early October, a study began to be picked up in the press. The study itself was published in late September and was based on fieldwork done in July. (Why it took so long to get this very important study done is anyone’s guess. We had major studies like this out of Europe and elsewhere already by May.)
The one-line summary of the study: 33 million residents of the US had been exposed to the Wuhan-Coronavirus by late June 2020. That is based not on the “rolling big Scary Number counts” on CNN. It’s based a large, random-sample antibody study.
(The paper: “Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study,” by Shuchi Anand, Maria Montez-Rath, Jialin Han, Julie Bozeman, Russell Kerschmann, Paul Beyer, et al; published September 25, 2020 in The Lancet; see pdf.)
The exact figure they calculated was 9.3% (confidence range: 8.8%–9.9%) of Americans had antibodies against this strain of coronavirus in the first two weeks of July 2020. Antibodies take some time to develop after the virus enters your system, usually around one week and sometimes two weeks. Given that testing was done for almost all in the sample between July 1 and July 15, this 9.3% estimate for US nationwide exposure reflects a nationwide virus penetration level as of late June.
There was major regional variation, with New York State the highest at 33.6%, an important point to keep in mind on any discussion on herd immunity (below).
What kind of error rates are there in the test used? I’m not sure, but there are people exposed to the virus who ‘recover’ (if they were ever sick) but do not develop antibodies. To account for the non-antibody people who did in fact have the virus pass through their systems, the number can be somewhat rounded up by several tenths of a point.
By mid-October (this writing), given that respiratory viruses will always spread, it may be up to 20% by now (=67.5 million US residents exposed to the virus).
A team at Northwestern University found 20% for Chicago, speaking to the Chicago Tribune October 9 but probably reflecting testing from September, suggesting to me a virus penetration of 20% in Chicago by sometime before September 1. As the rest of the country catches up, 20% nationwide is likely, by mid-October.
(Recall that up to 90 in 100 people never have any symptoms at all, some have mild symptoms [including President Trump] and only 1 in 100 has severe symptoms.)
There are three important questions worth asking:
- (1) If we are at 20% today, how much more is left to go? What is the exact herd immunity threshold for this virus?
- (2) What does the revelation of 9.3% antibody presence as of late June suggest about the true death toll (i.e., untangling “deaths with” vs. “deaths from”)?
- (3) Lockdown-induced deaths. If we can calculate a “deaths with” number, how does it compare to total excess mortality in 2020? If excess deaths are more than true virus deaths, the remainder must be accounted for. They were caused by the endless disruptions, stress, and dislocations of the Lockdown and the Panic itself, including Lockdown-induced deaths of despair.
The Herd Immunity Threshold
Herd Immunity is the mechanism by which all respiratory diseases are defeated, fade, and disappear from a population. The lockdowns disrupted this natural process in many places. The full mechanism was able to work in Sweden and some other places, including some that did lock down. (See Dr. Knut Wittkowski on herd immunity; sadly Wittkowski, a world-leading epidemiologist, was banned from Youtube for “violating CDC guidelines”)
What is the herd immunity threshold for this flu virus strain?
There has been a lot of confusion about this, with numbers as high as 80% proposed. This discarded all previous research on coronaviruses which suggested a much lower herd immunity threshold are much lower — flu viruses the corona family usually to achieve less spread within a population before fading out than other flu viruses. In other words, coronaviruses have a lower herd immunity threshold than, say, flu viruses from the influenza family.
Swiss Policy Research has reported on studies that corroborate these findings, finding a 20% spread is usually enough to break the back of transmission for this particular virus:
[T]he infection rate dropped as soon as about 20% of people had developed antibodies against the new coronavirus. This value is much lower than the initial estimate of 60 to 80%.
This is inevitably simplified, of course, and it is a mistake to assign some kind of iron value to herd immunity, as if it must be 80.0% (not 79%!), or 60%, or any figure. Every virus is different by its nature, every place is different, every time/season/year is different, every climate is different, every population is different. All these things can and do affect epidemic dynamics and thus herd immunity. Different areas of the same country, even different areas within the metropolitan area, will even have different herd immunity thresholds.
Many will be surprised to know that so many tens of millions of Americans have gotten the The Apocalypse Virus and never even knew it, with the 10%-by-late-June figure in the US now a matter of science and not speculation. So how much further till the herd immunity mechanism kicks in?
For all the demagoguery still ongoing over the virus, and for all the crowing by the pro-Panic side on how impressive are the victories they’ve won for their (anti-)Virus God since March, the joke may yet be on them: Herd immunity may already have been achieved locally in some places in which authorities and the pro-Panic coalition maintain their policy of major disruptions to life in all aspects. If so, their efforts are a total waste and possibly counter-productive even in epidemic management terms by undermining the herd immunity mechanism. The best course of action has always been to stay open (re-open) and just let it happen, as Sweden did.
Sweden has had all-cause deaths in the normal range since late June, with four to five weeks before that slightly elevated, following about seven weeks of significantly increased deaths from late March to mid-May. Sweden is going to end the year with full-year mortality not significantly different than its recent years.
The herd immunity mechanism is what allowed Sweden to pass through the flu wave (with minimal disruptions) and return by summer to total normalcy. Sweden ran several antibody studies, and we know that as of mid-April, 17% had antibodies, suggesting that level of penetration had been achieved already by or before March 31. The epidemic had another month left in it
The magnitude of that flu wave itself would have been less if Stockholm nursing homes had followed proper procedures but the broader point here is on herd immunity:
Given no mortality rise following the receding of the epidemic, we can expect this means Sweden achieved herd immunity and transmission could no longer sustain itself. The alternative explanation is that the virus disappeared in Sweden by magic. Whatever exact %-level that was, it will be different for the USA because the countries are different. (In fact, it is the same mechanism with every flu wave down through the centuries.)
As for the USA:
We see in the big antibody study by Anand et. al (Sept. 25, 2020) that New York state had achieved a virus penetration level of 34% by late June. We also see that the deaths-curve has hugged the ground, near zero, since then.
This suggests: (1) the herd immunity mechanism broke the back of the epidemic in New York state (the same way every respiratory virus is beaten, always and everywhere, year in and year out); (2) the herd immunity threshold in New York state was somewhere around 30% (which would be consistent with other coronaviruses).
Here is the graph of corona-positive deaths in New York state:
The pro-Panic side will, I am sure, insist the reason the curve stays so flat from June onward is because people obeyed the holy diktats of the Corona junta (on which topic, would you like anti-walrus insurance? I’ll sell it you at a discount).
The actual reason is the same as why the deaths curve for Sweden looks all but identical in shape, including the long tail (epidemic over): Herd immunity.
At least for New York, a tentative herd immunity threshold may be somewhere in the vicinity of 30%. None of the lockdown measures mattered. The only policy that would have made sense would be to have done the opposite: Keep everything open, allow the virus to circulate among low-/no-risk groups, and protect nursing homes alone. Refuse to allow Panic-pushers to force shutdowns, which only delays herd immunity and unnecessarily prolongs the disruption.
Deaths with the Virus vs. Deaths from the Virus; insights from the antibody study
The antibody study suggests two thirds of reported US corona-deaths are people who died of something else but were positive for the virus at time of death and were recorded as such. Virus Panic Death Inflation.
We know, from study after study elsewhere, that in a country with first-world demographics the all-population death rate from Wuhan-Coronavirus has been shown to fall between 0.05% to 0.20%, with the exact value more dependent on local demographics than anything else (just as the total death rate in any given year varies country to country in the first place) and any cases that locally exceed 0.20% being attributable to spread disproportionately affecting nursing homes. (The risk of death for anyone healthy under age 75 or so is less than the risk of regularly driving a car.)
There were reportedly 129,000 “COVID deaths” by July 15 in the US, the end-date of the Anand et. al. antibody study. This 129,000 stands against the 33 million who had gotten the virus two to three weeks earlier (rounding up, slightly, to 9.75% to account for non-antibody-producers).
Mathematically, this yields a 0.39% death rate (129k reported deaths / 33m who had the virus pass through their systems by late June). The problem with this is it would mean the same virus is somehow three, or four, or even five times more deadly in one country (the USA, computed 0.39%) as in others (usually in the 0.1% range). Can this be?
The alternative explanation, which we of the anti-Panic side have been pointing to since late March, is that the reported deaths are inflated. Not necessarily a conspiracy, but driven by the Panic. The majority of the reported deaths are fairly called “deaths with the virus, not deaths from the virus,” given the age-and-condition profile of the majority of victims — nursing home patients and persons hospitalized with other chronic and serious conditions.
Given that 33 million Americans had gotten the virus by late June, on a 0.05% death rate that would imply 16,500 “deaths from the virus.” A 0.20% death rate would imply 66,000 “deaths from the virus.”
Media claim: 129,000 deaths as of July 15.
Antibody test suggests: 16,500 to 66,000 true virus-caused deaths by July 15.
Between 50% and 85% of the media’s corona-deaths were “deaths with the virus present, but from some other cause.” This is a finding corroborated often, and easily grasped at a glance by the fact that most deaths are above age 80 and in nursing homes.
As of this mid-October writing, the number of true “Covid deaths” would, therefore, still likely be <100,000, not the 219,000 media-reported number.
I know some are bound to protest: You say true corona-deaths are only in the tens of thousands, but there were more excess deaths than that!
The CDC reports for 2020 Weeks 1 to 39:
- 2,425,784 deaths through Sept. 19th;
- 2,155,174 “average expected number of deaths” for the same period;
- 2,234,416 “upper-bound threshold for excess deaths” for the same period.
This means there could be 3.25 million deaths for the full year over the expected 3.0 million. But we’ve already seen that around two-thirds of the Covid deaths were deaths with the virus and not from it, leaving us a substantial number of excess deaths not attributable to the virus. During the peak-Panic period, we had people too terrified to visit hospitals and dying at home in record numbers of treatable conditions. We see the same thing in the world’s now-leader for brutal lockdown regime, Australia. Countries that cave in to full-Panic do indeed get people too scared to seek treatment.
Deaths of despair, including drug overdoses, have risen significantly, and that over the high-point they’d already achieved in the 2010s.
We are not given any official count on suicides, but reports here and there suggest they have risen by as much as 50% over 2019. This alone would mean tens of thousands of marginal deaths, none of which would have occurred without the Panic/lockdown pushing unstable people over the edge.
The year 2020 in the USA was supposed have 3,000,000 total deaths. It may have 3,200,000, with one-third to one-half of the excess attributable to the virus (with these deaths being primarily elderly or very sick people, putting light downward pressure on 2021 total-mortality, their date of death simply moved slightly forward) and one-half to two-thirds attributable to the Lockdown itself, the stress and fear it induced, and the pro-Panic side’s stranglehold on the discourse.
These unnecessary deaths will, in aggregate be younger and healthier. On any calculation of aggregate expected life-years lost, the Lockdowns/shutdowns.disruptions start to exceed the virus by huge margins, easily hundreds of times worse.
The “Dog that Didn’t Bark” Lockdown-induced population hit: Babies sacrificed to Corona-Moloch
The success of the pro-Panic coalition’s push to shut down over the virus and prolong the Panic will reduce births. This is a major and overlooked demographic hit from the craziness and is not to be neglected but is inevitably neglected because no one pays attention to “the dog that didn’t bark,” as the saying goes, to events that don’t happen but should or could. I have long been planning a separate post on this.
Every year, a certain number of people die and some are born, partly offsetting the loses to deaths. The number expected to be born was 2,750,000 in 2021 should be babies conceived April 2020 to March 2021.
Starting in mid-December, we’ll begin to see what kind of hit the Corona-Panic did to births.
A lot of couples are going to wait on having a child during the Panic and the recession. Those lost babies may never “come back.” Fertility lost to recessions tends not to recover in our society today. The 2008-09 recession caused a down-shift in fertility which never went back up in the ten years of recovery that followed. See how this happened in 1915-19 in Europe:
Given that we already have a demographic problem, including decades of sub-replacement fertility, the Corona-Panic is likely to cost us hundreds of thousands of births, hundreds of thousands of net lost births. This was all madness.
If just 3% of couples who statistically would have had babies born starting in December 2020 (nine months after the Corona-Panic began) decided to forgo having that child “for now,” that alone is a net loss of 82,500 births, which may exceed the number of genuine corona-deaths. If the fertility rate declines 10% for 2021, which is very possible, that’s a net loss of 275,000 expected babies never to be born, easily soaring past the total number of genuine corona-deaths. While the genuine corona-deaths may have lost a few years of expected life, the babies-not-born lost their entire lives. There is also probably dysgenic pressure here, as more conscientious people are more likely to forego the baby during the Virus Apocalypse Emergency.
The wild experiment that was Virus Lockdown of 2020 was in no small part about trading the lives of the young (including infants) for the lives of the elderly.
Looking back on the false alarm, seven months later
Data defeats Panic. That ought, anyway, to be the case.
The problem is, if good data is collected and released but no one sees it, does it “make a sound”? The long-time-in-coming large-scale nationwide antibody study got hardly any play in the media. You’ll say, not surprising under the Corona regime given how committed the media is to keeping the Panic alive. We are under a hostile, Virus Junta regime now.
The antibody study does help a lot of the pieces of the puzzle fall more firmly into place, including:
- How close the US may already be to herd immunity (pretty close), making all continuing Panic-driven measures unnecessary and even counter-productive;
- Just how inflated the media-pushed “Covid Deaths” total is (only one-third nay be genuine); and
- the scale of early deaths attributable to the Lockdown (probably more than the number of direct virus-caused deaths already, and that’s before mentioning the hit domestic fertility is likely to hit).
Welcome back! Looks like Corona Panic II is sweeping Europe- France, Netherlands, UK. Lockdown fever! Why there? I get it here, I’ve convinced myself that it’s about the Bad Orange Man, but Europe?
No sign of a let up in the Northeast – NESCAC has cancelled winter sports, Hockey East is probably going to play to empty rinks, and Obergruppenführer Charlie Baker is threatening to cancel Thanksgiving. Happy days!
I hoped the whole Corona-Panic would have passed by now. With flu season set to begin in earnest and the pro-Panic coalition in the driver’s seat seemingly even more than they were back in March/April, it’s safe to say that the Panic is here to stay through next March at least. A full year at least of major, unnecessary disruptions. (Just wait till they start seeing the fertility hit, reflected in live births +9 months after the start of the Panic and beyond.)
And speaking of an overlong Corona-Panic: In April, I made a semi-facetious prediction that by September-October the presidential campaign season might evolve into: “Anti-Lockdown, vote Trump; Pro-Lockdown, vote Biden,” a Lockdown/Panic referendum. I remember thinking, “that’s an extreme scenario; no way could the Panic last that long once the data is in.” How naive!
Why Australia, which, since August, has run the most brutal lockdown regime of anywhere in the world? Why anywhere?
I think for the answer we have to go back to the critical period in the global Panic Pandemic, which is March. Much of the last seven months’ developments were set in March.
Despite much predictioneering of change a-coming, usually with reference to China, a US-led global order was still in effect as of March 2020. The US media set the tone. I think most of the EU countries that caved in to the Panic did so in full form only AFTER the US did. There was a time, really through mid-March, when most of Europe was following the Swedish response — which is to say, the anti-Panic response. (The other unsung hero of the anti-Panic side was Belarus, which is as far from the US sphere of influence as you get in Europe and which also refused to shut down. Neither Sweden nor Belarus are going to have any noticeable full-year 2020 mortality excess.)
I believe a careful review of “who pulled the Panic trigger and when” will reveal that key players caved in only after the US did, or exactly in line with the US. Once that was done, politicians became locked in the Demagogue’s Trap (once you indulge in demagoguery, irrational appeal to emotion to shore up support, it’s hard to backtrack; if you succeed, you’re trapped.)
US policymakers are also stuck in the same Demagogue’s Trap. These are not idiots, and many/most have to see that it was all a big false alarm (anyone who appreciates the “10% of Americans had gotten the virus by late June” finding and its implications will see it). But they’re stuck. This on top, of course, of the opportunists and the true-believers. The same individual in a position of power might have all three constraints acting on them (i.e.: Demagogue’s Trap, Opportunism [power grab], True-Belief in the Virus Cult).
My wife works in higher ed- she’s a Branch Covidian, although less so than many of the nuts she works with.
We were discussing, and I’d guess, Mr. Hail, that it’s back in one of your many posts, but is there a reputable sense of the ratio of people who test positive for Covid to people who get sick? I’d love to have those arrows in my quiver, and I’m too lazy to go back.
Corona Doom is all over the media this AM- concentrating on the less locked down (and in the case of SD hardly at all locked down) states, states that are full of deplorables and bitter clingers. Fabulous!
For every 100 people who get the virus, it appears that 2-3% truly get sick.
“Sick” is of course subjective, but I think the numbers we have tell us that <5% get sick enough to call in sick to work if they are the type who shy away from taking sick days to begin with, i.e., really getting knocked down and unable to work for a period of days.
If this finding, reported from multiple countries (China initially pulled the Panic trigger in part with wild, reckless reports about 20% of everyone who contracted the virus being hospitalized; the true rate is 10x lower), and the finding of previous virus research, that flu viruses of the corona family by nature achieve lower population spread than influenza flu viruses, we should expect about 1% of the entire population to come down sick with Wuhan Flu in 2020-21 in a true sense. A small portion of these will, but a small portion of us die every year anyway. (The people so panicked over the concept of human mortality should consider shopping around for religions, as they generally all deal with precisely this question.)
The chance of dying depends entirely on who you are. It’s just a small portion of those who get sick. On an all-population basis we’re talking about <50 in 100,000 and a degree of overlap with those who are dying anyway. In many cases, this kind of virus takes people in serious pain out of their suffering and may even be welcomed by a dying person as a coup de grace to end suffering (hence an old nickname of pneumonia is "the Old Man's Friend"). The natural death rate in a first world country today is 85-to-100/100,000 population/year. The <50/100k Corona-Deaths and and 90/100k natural-expected-deaths but these are overlapping circles.
See below for a long-form response, which I may make into a new post.
Good to see you posting again.
That big data/narrative gap, with us still, you’re right. Chris Hayes of NBC sent off three demands today.
1.- Masks mandatory for next year.
2.- Mass closures: “Have the federal government pay every last bar, restaurant, nightclub, theatre and concert venue to stay closed for the next nine months.”
3.- Life in a bubble: “National Test, Trace and Surveillance: …One way to think of the goal is: what if every place people had to congregate was like the NBA bubble?”
Chris Hayes adds: Trump/Republicans wont do these simple,easy,humane steps, because, they “Just don’t care how many people die.”
Does Chris Hayes really believe this one flu virus is worth all this? Does he not get that people will be hurt by it in all kinds of easily predictable ways? This is public policy child’s play.
Or are these insane policy proposals (indefinite extension and strengthening of the Panic regime) really all about Get Trump? Or something else?
Hey, you’re back up and at ’em! Thanks for writing in on PS, but I’ve been checking your site once a day to see if you had something new for us. Obviously you didn’t JUST get back to it, as this looks like a lot of work, not a 1 hour finger-flying polemic like I do most of the time (and that includes partial editing).
I look forward to reading this later in the day. I’m sure I’ll have a comment or two. Are you ever going to chime in under iSteve (or any of unz) again? You quit nearly cold turkey, which is something that seems harder for me to do than people say quitting smoking is.
I will be back.
I sat it out for a period of the early Panic and have now been out for a longer period, also induced by the Panic, but this one of the past few months has conveniently aligned with me being more busy in the past few months in any case.
On Steve Sailer‘s attitudes towards the Corona-Panic (now the Corona Long Panic, the institutionalized Panic) and some general thoughts on his commentator career as I’ve long known it:
I’ve seen signs that Sailer has turned at-least-tacitly anti-Panic himself. Would you say this is right? He asked a while ago, sardonically:
(Sailer, normally a much more careful wordsmith than the average man, gets this wording wrong; George Floyd was at some point positive for the virus, but nothing indicated he had “COVID-19,” which is, in theory, a disease. The virus that causes this so-called disease, indistinguishable from flu, causes no symptoms in something like 19-in-20 cases.)
This rhetorical question on Floyd is Sailer back in his usual style, indicating, to me, that may have snapped out of it. Sailer’s style is to ask critical questions of narratives shoved on us from above (the media, the government, academics, Hollywood; or in a term I’d prefer, the regime). Sailer was not necessarily narrowly data-limited, but he was numbers-oriented and also just outside the box of respectability but thereby granting him liberty to comment on things others don’t, and often getting them right.
A common comment you’d hear in the Sailer comment-section after the latest thing, “It’s Steve Sailer’s world, we just live in it,” or some colorful variant of the idea that Sailer seemed to have predictive power. That predictive power was just because he allowed himself to stand outside the confines of respectable discourse and look in on them, not as some street-corner ranter (which most people on Twitter sound like, including @realDonaldTrump about half the time) but as a voice of reason. This is the Sailer style I was familiar with since reading him since the 2000s. It’s fair to say his followership appreciated him for that reason.
The Corona-Panic affair of 2020 was a major break in character, and that Sailer’s commentariat ran so strongly anti-Panic, after an initial period of fog before the data came in, suggests most others saw the same; it was puzzling and discouraging. Of course he was far from alone, but the problem was he used his considerable strengths as a commentator and his seeming predictive power, bet the farm on entirely on the wrong horse this time, and then kept disputing with the racetrack authorities and other spectators on how the race was going.
I don’t get this number, Mr. Hail:
** 2,234,416 “upper-bound threshold for excess deaths” for the same period. **
Is that a misplaced decimal location, as in 223,000 (or so) excess deaths as an upper bound? I subtracted your 2.155 million “expected deaths” from the 2.426 million actual deaths, all during that same period (Jan 1st through Sept. 19th) and got ~ 271,000.
It may very well be me missing something, but could you explain that last number, or maybe all 3 if that helps you explain it better?
This is how I understand the CDC “upper bound” figure:
Say a city of 500,000 residents “normally” has 100 deaths/week for all 52 weeks of the year. Over a certain five weeks, it records 98, 99, 108, 105, and 101 deaths. Question: Is there cause for alarm at the 108 and 105? At first glance, it might seem so. In fact, of course, it’s a statistical question. Maybe there is normal variation within 95 and 105. Maybe sometimes it can go as low as 90 or as high as 110 per week. It is only if deaths were sustained above 110/week that “something’s going on.” In this example, 110/week would be the “upper bound.” The city had nothing to worry about.
(Even if the city did hit 115 and then 120 dead over two weeks, maybe it’s worth looking at the weeks and months before and after to see if things even out. The upper-bound is, should be, just a guide in the sea of data to help analysis. The 115 and 120 dead may reflect a flu wave but may be followed by a period of low deaths that “cancel it out” on medium-run mortality.)
The CDC does not provide any further explanation for its “upper-bound threshold for excess deaths” line because it is inevitably partly arbitrary. It reflects a certain z-score rise above the computed baseline figure. It’s just a signpost to say, “This is the value above which we recommend paying attention, something’s going on.”
The CDC website is difficult to navigate and there is understandably great pressure on them to make the data look like it lines up with the narrative. The long story short is:
– There was a flu wave, and
– There were deaths associated with the response to the flu wave (e.g., the increase in suicides and deaths of despair, and people too terrified to visit hospitals especially during Peak Panic).
The CDC is conspicuously making no effort to clearly show the magnitude of each effect, and you have to do a lot of reading between the lines to guess at the true level of each effect. More importantly, the backwards-looking data they provide ignores the bigger picture of the impact of the response on the coming years.
Trump had an NBC tv appearance last night and on why he opposes lockdowns said: “The cure should not be worse than the disease.” His instincts are often right, and he is right on that one.
Also on the “upper bound” threshold for when an increase in deaths is definitely cause for paying attention:
The EuroMOMO mortality tracking project for the EU uses two thresholds: “Normal Range” and “Substantial Increase” threshold, each a certain z-score above the calculated baseline (the baseline is, of course, purely hypothetical, a statistical construct). Here again is Sweden’s this year:
It appears that the CDC’s Upper Bound is somewhere between EuroMOMO’s top-end “Normal Range” (blue shaded area) and its “Substantial Increase” threshold (upper dotted line). If you use CDC Upper Bound,” the period of “excess deaths” is longer and greater in magnitude than EuroMOMO’s Substantial Increase for most countries.
If you look at country-specific health authorities, the thresholds are different again:
Switzerland’s health authority uses a different threshold than EuroMOMO does and its equivalent of an Upper Bound is still higher than EuroMOMO’s. So depending on who is interpreting the numbers, an exact period of elevated mortality (most usually, one associated with the flu wave in winter) will differ sometimes by weeks, depending on whose signposts you want to use. Any possible politicization of the signposts is a red flag.
One way out of the statistical muck is to find/give as much context as possible. Sweden, the 2020 natural experiment, has very good birth and death records going back a long time. Here is a clean graph of their variation in deaths back to 1851:
(Graph found via Swiss Policy Research.)
The 2020 flu wave is visible at the end, but you’ll note it is not hugely above other spikes even in recent years of modern medicine and first-world conditions. The other thing to notice, which many may miss, is the weak spike in 2020 follows a mild 2019, making the two-year period 2019-20 look similar, if smoothed out, to 2017 and 2018. This is a point I’ve made repeatedly during the Long Corona Panic (see Part XI).
The mild 2018-19 flu season applies to almost all the countries in Europe. A lot of poor-health, frail people among the elderly, who would have died in 2019 were it a normal flu year, instead survived, to die instead in 2020. This itself explains a good portion of the 2020 Corona deaths.
Greetings Mr. Hale,
Thanks for another fine report.
You are probably already aware of this, but from the following CDC report:
Click to access mm6936a5-H.pdf
Characteristics of symptomatic adults ≥18 years who were outpatients in 11 academic health care facilities and who received positive and negative SARS-CoV-2 test results
Reported use of cloth face covering or mask 14 days before illness onset
(The first column is percentage of SARS-CoV-2 positive patients, the second column is for negative patients.)
Never 3.9 3.1
Rarely 3.9 3.8
Sometimes 7.2 4.4
Often 14.4 14.5
Always 70.6 74.2
It sure looks like face diapers may be highly efficient breeding grounds for respiratory diseases. Who’d’a thunk it?
But, Science! and, Experts!
OK, to reply to my own comment. If we assume that none of the negative patients were in the hospital for a respiratory illness, then my earlier thought is contradicted. Interpreting numbers without a proper context can be dangerous, as you keep pointing out, Mr. Hale.
Robert, This is the part of the report you are highlighting:
What we are seeing there is a sample of 154 people who tested positive for Wuhan-Corona in July 2020; 33 say they had a family member who was positive previous to them (the likely transmission route in all or almost all of those 33 cases must be family). This leaves us with 121 positive cases who did not receive it from family. Where did they get it from? 89 had no idea and 32 report known close contact with a previous positive case but the transmission route is to some degree uncertain.
Now the more attention-getting part: 108 of the 154 reported “always” wearing a mask/face-covering and 22 more report “often” wearing a mask, total 130 of 154 (85%) being self-reported diligent pro-maskers. The “control participants” (people coming in for a test and testing negative) had apparently the same rates of mask wearing, which is the headline, and which in somewhat garbled form has become a talking point in October. Trump used this last night during the NBC town hall (so called “town hall forum,” actually an interrogation by a partisan journalist with members of the public distributed as props in a religious-like pattern, spaced out in the dark, like priests observing a ritual).
What if we deduct transmissions-by-family in this sample? What if all 33 of the people who reported a previous positive by a family member were diligent “maskers”? That would knock us down to:
121 people in the sample got The Virus from someone in the public (non-family), and in three quarters of these 121 cases they have no idea where they might have gotten it. Of these, at least 97 were “often or always” mask wearers, or 80%+.
Comparing with the control sample (people who specifically came in to get tested and were negative) may be of less use than a comparison with general population mask-wearing rates. Were four-in-five people wearing masks “often or always” when interacting with others in July 2020? It seems like it was less, which would counter-intuitively suggest masks are correlated with higher risk of infection.
The two potential quality problems with the data that I see are:
(1) Selection bias, in the form of the sample being people who came in for tests (rather than some randomly selected sample of the public at large) introducing possible problems, especially with this mask rates things; people coming in for a test would probably lean pro-mask in any case. Remember that by the time this data was collected in July, some tens of millions had gotten the virus already (antibody study) but only a tiny portion knew it, or would ever be tested. Those most concerned would presumably be those coming in for tests.
(2) Reporting bias. Whether to actually trust people’s self-reported mask-wearing rates, especially given the social pressure to conform to pro-mask culture and the legal mandates (though I haven’t heard of people actually fined for being without a mask on the street). How many of these several hundred were massaging the truth on their rate of mask wearing?
In any case, general conclusions can be drawn. They are those of the “we already knew that, people are doing academic studies to confirm common sense” variety: Respiratory viruses spread most often among household members, and there is no way to systematically avoid cold and flu viruses without being a hermit.
Response to GAnderson’s comment above:
See also my brief reply above.
In fuller form, I’d propose five conceptual categories for understanding the Wuhan-Corona flu wave of 2020 in its full context:
People can be one of the following:
(1) Were or are now positive for Wuhan-Corona but had no noticeable symptoms (asymptomatic);
(2) Were or are now positive for Wuhan-Corona and have/had mild-to-moderate flu symptoms (“under the weather);
(3) Were or are now positive for Wuhan-Corona and are/were really sick for a time, with a call-in-sick-level flu. (We have all gotten flu at some point and know it can knock you down a few days, even the ‘regular’ varieties, if they ever break through your immune defenses);
(4) Negative (never positive) for Wuhan-Corona; had no real flu symptoms in 2020;
(5) Negative (never positive) for Wuhan-Corona but have had moderate-or-worse flu symptoms in 2020 since March.
From antibody testing we now can say that as of this writing, 20% of all Americans have had bodily contact with the virus. Therefore 20% fit into one of the categories (1), (2), or (3). The other 80% of people fit into (4) or (5).
Only 2.35% of the population have been lab-confirmed positives (8 million test-positives as of today, according to the CDC). For those other 17.65% — the positives who never knew it, who were never tested — we can assume they were in categories (1) and (2). If anyone had been hit with a bad flu, it seems certain that it almost all cases they’d seek out help and get tested, given the news drumbeat on this since March. It is also very easy to find free testing sites. So it’s safe to assume the positives-but-never-tested passed through it still on their feet.
Among test-positives (2.35% of the US resident population), evidence from various reports suggest ca. 80% fit in category (1), no real symptoms at all (though in some cases the person might be pressured to say, “Oh, yeah I do kind of have a headache,” but wouldn’t particularly have noticed without being told). A further circa 15% fit in category (2), mild-to-moderate cases. This leaves up to 5% having tough cases, knocked off their feet and needing rest all day, etc., and it is from this group that a small portion die (as with any flu).
This kind of spread is found across all countries and situations for which we have good randomized testing. A July review of the Wuhan situation shows “87%” of those actively transmitting the virus in December 2019 to February 2020 had no symptoms or very-mild symptoms; the initial Panic was sparked by Wuhan, but in retrospect it was all quite a false alarm — the actual positive rate was very high.
Of the <20% test-positives who develop some sort of noticeable "flu," including the relatively mild cases (which, in a less-PC age, might have been known as Wuhan Flu, in the way the 1968 pandemic was called Hong Kong Flu, to the extent people bothered paying attention to it in late '68 into early '69, which seems to be not much), it seems one-in-four is hospitalized, for an overall hospitalization rate of 5% of all test-positives. When bringing in untested positives, the total hospitalization rate among those exposed to the virus drops to 1%. (See here.)
The picture of how many of us are in each category, as of this writing, looks something like this:
Wuhan-Corona flu wave’s effects in this US population as of mid-October
The US resident population in Q3 2020: 340m? (wait for census results to be released in January)
(1) 55m (16%): Positive* for Wuhan-Corona but had no noticeable symptoms (asymptomatic);
(2) 14m (4%): Positive for Wuhan-Corona and have/had mild-or-moderate flu symptoms;
(3) 2m (<1%): Positive for Wuhan-Corona and got really sick (the "call-in-sick-level" flu);
(4) 230m (67%) Negative for Wuhan-Corona, no flu symptoms;
(5) 40m? (12%): Negative for Wuhan-Corona but have had at-least-mild flu-like symptoms in 2020 since March.
* – ‘Positive’ in each case here refers to true-positives, including both test-positives and untested persons who were positive at some point. The sum of these two we know from antibody testing. ‘Negative’ means never-positive, not exposed to the virus.
It’s hard to know , absent rigorous testing including antibody testing, which category an individual falls into. If you were never particularly sick all year, you could be a (1) or a (4). If you were mildly sick at some point but did not test, you could be a (2) or a (5).
A subset of (3) are those hospitalized. Reportedly a cumulative 430,000 have been admitted to a hospital “with COVID19” which rounds to 0.13% of the total population. This is against 2.35% of the total population being a confirmed positive (test-positive). A sidebar caveat is that not all of these are necessarily hospitalized as a direct result of the virus, but may have several contributing problems, even several viruses at work against them at the same time.
In any case, we end up with 5-6% of test-positives (not total positives, which includes tested and untested) hospitalized.
What of this hospitalization rate? Is it high? Given Corona-Panic-Mode, we might expect the rate to be pushed up above what it would be had no one noticed there was a New Virus Never Seen Before.
A real-world example: They ‘hospitalized’ Trump despite his symptoms putting him in category (2). There is no way they would have sent him to a hospital given his symptoms had no one known there was a New Virus Never Seen Before.
Melania’s symptoms were mild and in normal times she likely would have classified herself (1), but given the hype she may have talked herself into being a (2) or straddling (1) and (2). Their their son was a definite (1), along with the majority who get the virus. It was all not a very big deal for the First Family in health terms, but a for a few days it caused a familiar cycle of media squawking.
The idea that major restrictions are a good idea given these kinds of numbers is just silly at this point. Let it pass.
Another conclusion from this five-category analysis that tries to account for all 340 million US residents:
Maybe 50 million people in the USA have had moderate-or-worse flu-like symptoms in the past eight months — here, categories (2) plus (3) plus (5) — but 75% of these were Wuhan-Corona negatives, definitely caused by ‘regular’ flu viruses (i.e., category (5) here), and some portion of the Wuhan-Corona positives also probably were positive for other flu viruses so it’s hard to say exactly what the origin of a specific illness is. Maybe people in (5) were hit by one of the influenza family, or another coronavirus (not Wuhan-Corona), or a rhinovirus, or something else. If you get a flu-like illness that knocks you down, what’s the difference what you call it?
On this last point, an anecdote:
I witnessed the mental Cult of Corona weaken within an individual I know back in about late August. She came down with flu-like symptoms, weakness, sapped energy. Then one day, after a few days under the weather, she thought she’d lost her sense of smell. Having heard this was as symptom of The Apocalypse Virus, she got really worried, and her behavior changed; she began to practically say last goodbyes to friends and family. She called the doctor. They advised her to go in and get tested for The Virus. She went in. It was negative. All of a sudden, her symptoms immediately lifted and she was in good spirits! An amazing, instant recovery! The news that one could get flu-like symptoms from some virus that is not Wuhan-Corona somehow broke the spell, at least for a time, in this person’s mind.
Thanks. Most helpful for a lazy old guy.
Have you seen this, same topic as you’re on here:
“Herd Immunity Progress by State”
New York, New Jersey, Massachusets, Connecticut, Louisiana all close to herd immunity by the that calculation.
Hawaii (crazy-level, no-exit-plan lockdown), Alaska, and some of the rural and smallest states are farthest from herd immunity.
Great source. Thanks. I wonder what equation they’re using to get those Herd Immunity Progress estimates, but the general picture is certainly correct.
Excellent talk by Tom Woods on what’s wrong with Covid-ism, Lockdown psychology, forceful points about costs that are overlooked/ignored (though even he fails to mention the fall in the birth rate likely to follow the economic crash and prolonged Moral Panic, with net lost babies alone almost certain to easily outnumber genuine extra flu deaths in 2020-21).
Passionately made points about Covid-ism vs. the purpose of human life. Presented effectively and efficiently; a 45-minute downloadable talk by Tom Woods, probably much more effective than anything I’ve written especially for those who prefer the spoken word over the written. Listen or download here:
Also available on Youtube (for now), the video of his October 9 talk:
(See also Youtube backup version, audio only.)
Woods is a solidly and evangelically anti-Panic. I liked him before but have a new respect for him. He is of course one of the USA’s most famous political-economic libertarians, so add that to the list of “Who’s Who among the pro and anti Corona-Panic coalitions,” not that libertarians being against Lockdowns is the shock of the century.
I’ll add a good word for Knut Wittkowski; Woods makes the same points that world-leading epidemiologists like Wittkowski were making early on in the Panic. Wittkowski is still today under an perma-ban from Youtube after getting millions of views on a documentary series about the Coronavirus Crisis in which he appeared, produced by major documentary film producer Journeyman. That was early April, slamming lockdowns as insane, needlessly damaging and counter-productive even towards the monomaniacal goal of suppression-of-one virus. If Woods’ anti-Panic talk gets lots of views, will the censors of late-2020 ban it like they banned Wittkowski in late April/May?
I agreed with your previous posts on Covid19, but your attempt here to calculate the fraction of excess deaths due to the lock-down is not persuasive. Your argument is that 9.3% antibody test rate can be used to calculate the percentage of Americans who were exposed to SARS-COV2. You round up to 9.75% to account for non-antibody producers. How do you know non-antibody producers are less than 0.5%? If you agree that herd immunity is mostly achieved with less than 20% showing antibodies, that suggests that 60-80% of people are non-antibody producers. The possible mechanisms are undetected antibodies from previous exposure to a non-Covid19 virus and T-cell immunity. If this is the case, the population of people who had been exposed to SARS-COV2 by late June could have be 3-4 times higher than 33m. Using the low end of the range, the all-population death rate from SARS-COV2 becomes 129k/(33mx3) = 0.13%. This is within the range you indicate for other first world countries and so leaves no room to assert that there were excess deaths from the lock-down.
While I agree that there were excess deaths due to the lock-down, I do not think your line of reasoning has estimated it.
I agree with you that the question of exactly how many died from the effects of the lockdowns/Panic is hard and there is no magic-wand answer.
We know with certainly that there have been such deaths already, with the well-documented spike in (treatable) heart attack deaths all across the world being one case.
It’s also clear there will be more deaths attributable to the lockdown and its after-effects, with the other common example cited is cancer screenings. People who get cancer spotted early have a calculably higher chance of survival. With the Coronavirus Panic of 2020, people stopped going in for cancer screenings or were banned from going in for that and all other “non-essential” health procedures. This will lead a (theoretically calculable) number of early deaths, which will not be all at once but spread out over the next few years. The question we again ask is, did the Lockdown-pushers even attempt to make such a calculation in March when they pulled the trigger on shutdowns?
White House adviser Scott Atlas has taken to saying “Lockdowns kill” to try to get this meme out there. It seems precisely because the effect can be hard to measure and is diffuse that there is no “lockdown rolling death count” for CNN to flash on screens that the phenomenon can achieve such a low level of awareness and be dismissed kind of as a conspiracy theory.
Another estimation method for Lockdown-induced Deaths — obviating the need for antibody tests or for juggling national health authorities’ reporting on “COVID19 associated deaths” with their “deaths with COVID present” and their “pneumonia deaths” and their reported all-cause deaths — is using all-cause mortality comparisons from place to place. We’ve been blessed with several true natural experiments running, including Sweden and Belarus (post on the latter forthcoming) who did not lock down, but this also applies to places with harder lockdowns and softer lockdowns.
Belarus: When all is said and done a year from now, given that Belarus “weathered” the full Wuhan-Corona flu wave with effectively no policy change at all, we could compare Belarus’ all-cause per-capita deaths in 2020 (age-normalized if possible) with the same for other places. If others show a higher excess, that could be a signal of near-term Lockdown-induced deaths (medium-term and long-term lockdown-induced early deaths are going to be much harder to sort out).
Belarus’ mortality data through the end of June is out and in it we see that Belarus may end 2020 with 0.05% more of its population dying than was usual in the late 2010s, as in 1.30%–>1.35%. This one-time, one-year +0.05% mortality bump is much lower than the effect on mortality of the economic dislocation effect of the Soviet breakup, a well known story in which deaths rate rose for many years, only recently recovering back down to something more natural. Going on Belarus’ mortality data back to 1990, we can see it had several flu waves causing spikes that look just like Wuhan-Corona. (The same is true in all Western countries; flu waves that are occasionally bad are a fact of life.)