Michael Fumento‘The … crisis we face is unparalleled in modern times,” said the World Health Organization’s assistant director, while its director general proclaimed it “likely the greatest peacetime challenge that the United Nations and its agencies have ever faced.” This was based on a CDC computer model projection predicting as many as 1.4 million deaths from just two countries.
So when did they say this about COVID-19? Trick question: It was actually about the Ebola virus in Liberia and Sierra Leone five years ago, and the ultimate death toll was under 8,000.
With COVID-19 having peaked (the highest date was April 4), despite the best efforts of the Centers for Disease Control and Prevention to increase numbers by first saying any death with the virus could be considered a death from the virus and then again this week by saying a positive test isn’t even needed, you can see where this is going.
Since the AIDS epidemic, people have been pumping out such models with often incredible figures. For AIDS, the Public Health Service announced (without documenting) there would be 450,000 cases by the end of 1993, with 100,000 in that year alone. The media faithfully parroted it. There were 17,325 by the end of that year, with about 5,000 in 1993. SARS (2002-2003) was supposed to kill perhaps “millions,” based on analyses. It killed 744 before disappearing.
Later, avian flu strain A/H5N1, “even in the best-case scenarios” was to “cause 2 (million) to 7 million deaths” worldwide. A British professor named Neil Ferguson scaled that up to 200 million. It killed 440. This same Ferguson in 2002 had projected 50-50,000 deaths from so-called “Mad Cow Disease.” On its face, what possible good is a spread that large? (We shall return to this.) But the final toll was slightly over 200.
In the current crisis the most alarming model, nay probably the most influential in the implementation of the draconian quarantines worldwide, projected a maximum of 2.2 million American deaths and 550,000 United Kingdom deaths unless there were severe restrictions for 18 months or until a vaccine was developed. The primary author: Neil Ferguson. Right, Mad Cow/Avian Flu Fergie.
Then a funny thing happened. A mere nine days after announcing his model, Ferguson said a better number for the U.K. would be only 20,000. The equivalent would be fewer than 80,000 American deaths. Technically, that U.K. number was buried in a table in the report under what might be called “a fantastic case scenario.” But could that reduction possibly reflect a mere nine days of restrictions? No.
Soon all the numbers were tumbling. Yet as late as March 31, the New York Times declared: “White House Projects Grim Toll from Virus” citing White House Coronavirus Task Force head Deborah Birx and director of the National Institutes of Allergies and Infectious Diseases Anthony Fauci, who in turn cited a model showing deaths up to 240,000. Still awful, but Birx explicitly backed off the Ferguson projection for which she had previously been the Grey Lady’s pompom girl.
Then suddenly Fauci announced a flat figure of “more like 60,000,” the same number the CDC says died of flu two years ago. Probably not coincidentally, until quite recently the agency said there were 80,000 flu victims that year, before lowering it to 61,000 – presumably because people were using that figure to compare to COVID-19 deaths. In any event, the 1968-1969 “Hong Kong flu” killed an estimated 100,000 Americans, or 165,000 adjusted to today’s population.
Moreover, as noted, the CDC now encourages coding a death of anyone “if the circumstances are compelling” even though they haven’t been tested at all. Yeah, wow; it’s not a “conservative myth.” During flu season, that means a lot of flu victims have magically become COVID-19 victims in addition to people who would have otherwise had cause of death listed as heart attack, diabetes, and other co-morbid conditions.
One reason Italy had so many “coronavirus deaths” seems to be coding, even though it’s still far more strict than the new CDC guidelines. Re-evaluation of death certificates by the country’s National Institute of Health showed only “12% with direct causality from coronavirus, while 88% of patients who have died have at least one pre-morbidity – many had two or three.”
Then Fauci finally said it. “I’ve spent a lot of time on the models. They don’t tell you anything.” A few days later CDC Director Robert Redfield also turned on the computer crystal balls. “Models are only as good as their assumptions, obviously there are a lot of unknowns about the virus” he said. “A model should never be used to assume that we have a number.”
Which, of course, is exactly how both a number of public health officials and the media have used the them.
Only one significant model appears to have been correct. But wasn’t. The University of Washington’s Institute for Health Metrics and Evaluation has actually been dramatically reduced and reduced.
Model defenders declare the plummets were based on the success of severe restrictions of civil liberties. “It just means we won,” declared an article in The Atlantic. Wrong. The bottom range of the models presumes the best-case scenario. If the low end is 100,000, that’s the low end.
If epidemic models were just haphazardly wrong, we would expect about half the time they would be too low. Instead, they’re almost universally vastly too high. This isn’t happenstance but intentional. The single most cynical model is probably one regarding Sweden. Released online after the Swedish epidemic had already peaked, and with deaths at about 1,300, it nonetheless predicted a median of 96,000 Swedish COVID-19 deaths with a maximum of 183,000. WTH?
Basically the Swedes have shown dictatorial methods aren’t needed and thereby pose an incredible threat to all those who claim otherwise. This was apparently (yet another) desperate effort to convince the Swedes to lock down like everyone else – never mind that it comes after their epidemic has already crested.
The only “model” with any success is actually quite accomplished and appeared in 1840, when a “computer” was an abacus. It’s called Farr’s Law, and is actually more of an observation that epidemics grow fastest at first and then slow to a peak, then decline in a more-or-less symmetrical pattern. As you might guess from the date, it precedes public health services and doesn’t require lockdowns or really any interventions at all. Rather, the disease grabs the low-hanging fruit (with COVID-19 that’s the elderly with co-morbid conditions) and finds it progressively harder to get more fruit.
That’s not proof that public health interventions are worthless; merely that since the Plague of Athens four centuries B.C. and before, epidemics have risen and fallen quite on their own. Nobody needed Big Brother looking over their shoulder and cracking a whip; nobody needed to implode their economies and leave their citizens with tops reading: “I survived the ‘worst epidemic in history’ and all I have left is this crummy t-shirt.”
The models essentially have three purposes: 1) To satisfy the public’s need for a number, any number; 2) To bring media attention for the modeler; and 3) To scare the crap out of people to get them to “do the right thing.” That can be defined as “flattening the curve” so health care systems aren’t overridden, or encouraging people to become sheeple and accept restrictions on liberties never even imposed during wars. Like Ferguson, all the modelers know that no matter what the low end, headlines will always reflect the high end.
Assuming it’s possible to model an epidemic at all, any that the mainstream press relays will have been designed to promote panic. Take it from Fauci, who early on so eagerly employed them – they are to be ignored. Now and forever.
Michael Fumento is a former Investor’s Business Daily National Issues reporter and is also an attorney, author, and freelance journalist who has been writing about epidemic hysterias for 35 years. His website is www.fumento.com.
I & I Editorial Board
Using these vague “policy violations,” Google is now threatening to demonetize us. It’s part of Big Tech’s effort to silence conservative voices.
but we are supposed to believe the climate projections [which have already been shown based on lies] and let them impose the soylent green deal
Fauci and the FDA refuse to allow treatments or vaccines without controlled double blind randomized trials, but shut down the economy based on models with no scientific support. They’re just guesses based on guessed assumptions of guessed controlling parameters. The thing to take away from this is that in epidemics, like life generally the elites don’t really know any more than most everyone else. We need news media and universities to publish facts and what is known and refrain from speculation and assumptions. Given decent information, free individuals are in the best position to make their own decisions for them and their families.
It is not up to the FDA or Fauci to allow treatments using drugs that are currently approved. Physician use medications for “off-label treatments” all the time and in fact at our hospital, we are using (or at least trying) Hydroxychloroquine, Kaletra, Actemra and a number of other meds. With regards to a new vaccine, of course the FDA needs to approve it since some proposed vaccines have and might actually enhance the disease and make things worse.
Of course. And the drugs you’re trying, such as Kaletra, have all been demonstrated to show clinical benefit with placebo-controlled, double blind trials. I think you’ll find that if you look more carefully into the history of Kaletra, it’s benefit is either in terms of reduced viral load as an endpoint regardless of clinical benefit, on the assumption that reduced viral load corresponds with clinical benefit, or that instead of a placebo it will have been compared to something even more toxic, which itself was probably compared to AZT, one of the most deadly chemicals ever prescribed as medicine in modern times
It is kind of like global warming, it is dependent on the initial set of figures and if garbage going in, or just made up in the case of global warming, everything flows from that. And if wrong originally, you end up with predictions of 2 million dead in this case. Please, just like when there is a tornado or earthquake these shmuks that first get in front of the camera sensationalize every time for the press and ratings. They are never right.
agw is even slimier than you state, the numbers have been deliberately modified to fit the model
Please remember that the guestimation math used to build these models is roughly the same guestimation math used on the global warming models. And the trillions lost during this farce will be a pittance compared to the trillions wanted to protect us from global warming.
And what we need now, but will not happen, is for the government to admit that their models were wrong, that they overreacted, and that we should carry on. They cannot admit error.
If you’re sick or at risk, stay home. If you’re worried about getting sick, don’t accept visitors. Otherwise, business as usual.
Also every government bureaucrat or “expert” who pushed these blatantly false models should be publicly shamed and then fired and their pensions withdrawn to pay at least some of the cost incurred by their incompetence or purposeful fraud so to perhaps make this unlikely to happen next time. Then the government departments that pushed this nonsense should either be slashed deeply or closed entirely to again show that this sort of behavior will not be tolerated.
These “experts” value the praise they receive for their ideas much more than they value people’s lives and livelihood.
If a politician could get re-elected by admitting they are an idiot and wrong all the time, then they would do it all the time. The system is flawed because politicians are only winning if they get re-elected. It doesn’t matter what else they do right or wrong, because in their world winning is only determined by being re-elected. Don’t like it, just change the democratic system.
Great reporting, and what my husband and I have been saying all along. Just remember that it is a more deadly virus – especially for those with other comorbidities. Like cigarette smoking (65% of older males in China and Lombardy) and those living in the Red Zone for air pollution like China and Lombardy (see NASA photos of pollution before and after lockdowns).
Yup they work on Fear and people like to be afraid!!
We all could be wearing masks and done the same thing without causing our economies to tank and have exorbitant unemployment!!
Masks do not provide any benefit unless one is sick and sneezing or coughing. If you are sick, stay home. If you are not sick, the mask is nothing more than a silly comfort or mere virtue signalling.
“Masks do not provide any benefit unless one is sick and sneezing or coughing.” umm, people are sick, sneezing, and coughing. Thus, people wear masks to protect themselves and others.
There are always things we might do to ward off disease or injury, but who wants to go through life bubble-wrapped? “Social distancing” is the current intolerable prevention; I’m a hugger and other “handsy” activities person because I unashamedly demonstrate my affection for family and good friends – I haven’t killed anyone in over seventy years of failing to keep my distance and I don’t believe that WuFlu will change my record. We’ve been had.
A mask is a courtesy to protect others, not you.
Masks are design to protect other people. A surgeon wears a mask during an operation not because he or she is afraid of getting an infection from the patient but because they don’t want any germs they have to be transmitted into an open wound.
Interestingly smoking isn’t a comorbidity; if anything, smoking is almost a guard against covid. There’s now plenty of data showing that smoker hospitalisations and deaths as being underrepresented. Not intuitive, I know. The point isn’t to correct you with nitpick, but to add on to the theme that everything we’re being told is nonsense, (providing you pronounce ‘nonsense’ as ‘BullS**t.’)
Keep this in mind the next time someone tells you they have a computer model that predicts catastrophe unless we do something dramatic, especially if after 40 years of development and 20 years of predictions, it is still wrong. Does this sound like Global Warming/Climate Change?
“Does this sound like Global Warming/Climate Change?”
Why, yes. yes it indeedi do.
Considering the current column, a fundamental question then arises. Epidemiological models are forced to operate under suboptimal conditions at the very early stages of an epidemic. Many of the key modeling constants are simply not known (and in this case, were apparently subject to misinformation). In this case, the author claims the models failed badly. Actually, as the graph presented above shows, the modelers included (the shaded areas) confidence intervals that in fact showed a rather broad range of possibilities.
But if we can’t trust epidemiological models that were at least honest about the potential inaccuracy of predicted trends, why should we trust climate models that have repeatedly been wrong, and are vastly more complex than epidemiological models? Explaining what happened in the past is not a valid test of a model. A million wrong models can fit a complex data set.
We can’t see this with the graph above, but often, when the solid line of “actual deaths” is drawn in, the line is well below even the shaded region. If the models can’t even get the best-case scenario right, it’s difficult to see why we should trust them — and this, despite the fact that this happens again and again.
Much like how, of a thousand or so of climate models, only five approximate reality, and even then, they were “as hot” as what actually happened.
Well, sometimes, it happens the other way. President George W Bush and President Obama and the experts promised us that if we didn’t act immediately, we would follow a certain curve of lost jobs that would peak and then go down; if we passed a stimulus, the curve and the peak would remain well below the “worst case scenario”. We passed the stimulus, and afterward, the actual curve made the “worst case scenario” look optimistic.
If we can’t get accurate predictions from economic, epidemic, and climate models, I can’t help but wonder: what’s the point of even having them if they can’t predict the future? Heck, I don’t even need them to predict the future. I just want them to approximate the future occasionally!
I entirely agree with you. If a model can’t make reasonable predictions about the future, there is either something wrong with the things they’re measuring, our there’s something wrong with the processing of the things they’re measuring
Yeah. And then there was the model propose by that great theoretician Donald Trump. ““We have it totally under control. It’s one person coming in from China, and we have it under control. It’s going to be just fine.”
And, when he said it, it was wholly based on what WHO and Fauci were saying, the experts.
Mr. Fumento, while agreeing with each of your arguments against bogus, self-serving, constantly-adjusted computer models, is not that graph at the top of this article projecting future deaths out to August, 2020, a model?
It’s a screenshot of the IMHE model prepared by Dr Murray in WA who is funded by Bill and Melinda Gates which Mr Fumento describes as the only one being correct though not really because of revisions.
It is the one graph that has been fairly close on the daily death numbers, but his methods are so idiosyncratic that it’s unclear whether he came close on purpose or accidentally.
Fundamentally you can’t do much of anything without a model; even a mental image is a form of a model. And you know this. But that’s not what he’s referring to, he’s railing against using the current crop of epidemiological models that claim to predict curves based on “do nothing” vs “best case.” And he is correct; this form of model is used in climate work with “business as usual” and “full mitigation effort” curves. And it has proven to be just as useless. The big problem appears to be the default setting (“do nothing” for epidemiology and “business as usual” for climate) where apparently this default is actually HERE BE DRAGONS since it seems to be wide open with maximum damage mode assumptions **intentionally** done to spur action. It is this default that angers everyone, it is this default that creates pandemic and climate deniers every single day because of how utterly wrong it is. And we all know it’s wrong. Face it — any model with a default setting created *specifically* to spur action is no longer a viable mathematical model, it is propaganda. A lie.
Scrap the gullible warming models while you’re at it.
I’ve been reading Michael Fumento’s great reporting in this area for many years. His analysis is always top notch.
Surprisingly he has repeated a widely reported inaccuracy though.
The Imperial College model was not revised 9 days after the original was published. The original itself contained the ~500,000 unmitigated number. It also contained three columns of different numbers considering a complex array of different levels of mitigation and removing and reinstituting those mitigation measures. The last column had a range of possible deaths of from 5700-48000 depending on how rigid and successful the measures taken were. The median was ~20,000 and that number was in Ferguson’s original paper.
Otherwise Mr Fumento’s point is, as usual, well made.
Ferguson also noted that mitigation would lengthen the length of the epidemic and might cause more than one wave, so the final number might have been more than 20,000. That is one of the great weaknesses of the epidemiologists’ numbers. They can’t even agree if flattening the curve significantly reduces total deaths or not.
Can’t anybody here play this game?
Here’s a test to check the accuracy of models: reverse electrical polarity on their computers so everything runs backwards. Then run their models. The global warming models should be able to accurately predict what the weather was for the past 150 years, right?
I know, I know. . .
Doesn’t always work, that “working backwards”. That infamous formula for “calculating” the value of financial derivatives was devised and “tested” just that way; turns out it missed a lot. See “The Thrilling Adventures of Lovelace and Babbage” for some leads on that event.
The problem with “running backwards” is that modelers find themselves feeding in more and more known details — and sometimes not even that! sometimes it’s just random fluctuations — that make it appear to be able to predict the past, but have absolutely no predictive ability.
Stock trade modelers sometimes fall into this trap, and their models will sometimes even work for a time — maybe a few weeks, maybe even a few months — before some random new event comes and throws a wrench into the works, and the model blows up in peoples’ faces.
I concur with the author; the only data we get from the models, and those who push them, is just how bad it is to listen to these people going forward for anything. We need to kick these people, these experts, to the curb because they have in fact keep real researchers and science from moving forward. They have exploited academia and the grants; they have mastered how to get huge funding for their own gains, and have intimidated and smeared anybody who gets into their pockets or way of making a living pushing these models as scientific proof their whole lives.
Look, people die and that’s a fact – no one gets out of here alive: find God; get a life; do whatever it is you do, but get a grip on how the species (Mankind) has perpetuated itself on for centuries before these clowns came up with their computer models. It is know as social interaction among all species on the planet; eating live bats is not my thing, and I wish China the best on that, but to each their own. In whole, or congregate, it seems to have worked over time.
This economy thing, not so much – where did this idea come from? We are all going to die? OK Jim! Can I at least make a buck? A selfie here would be good for posterity – unless we all die and go extinct, I think it would be cool!.
This is the dumbest thing I have seen in my life.
And once again we circle back to money – Federal money being made available for Wuhan Klan Flu expenses. Absent this, all the restrictions and Constitutional right-bashing disappear.
but what about the black death in Europe in the that lasted 4+ years in the 1340s?
or the Spanish flu of 1918?
I think the article makes sense… but only if you have absolutely no background in policy-making (e.g. economics or political science, and statistics). The models are not intended to come up with specific numbers to scare people (although they can certainly help, good there). They are mainly intended to support policy-makers to make decisions based on the incomplete information that we have.
While in precise sciences we can talk about precise numbers, this is not maths. Statistics is about probabilities and trying to provide at least some information to make sure the outcome is better (or “less worse” if you prefer).It is basically the best we have at the moment, and much better than just deciding by our guts. Otherwise we would be doing much worse off.
That is the big challenge we face from a decision-making perspective. So, if you have a better model that predicts this better I entirely invite you to share it. Otherwise, this maybe be more detrimental, making people believe that all information is garbage. Some of it is… yes… media. But some of it comes from some of the most brilliant minds we have. We are human though, and therefore it makes sense that incomplete information is the best we have to come up to conclusions. That happens everywhere, not only in pandemics.
Except that Farr’s Law is a model that is close enough and the numerical models need to demonstrate that they are superior. The author did share it.
The models aren’t the problem. It’s the people who pay for the models who have agendas for the model’s outcomes are the problem. Leftists wanted high numbers, so we got high numbers – even if they made no sense.
They should have been tweeking and rerunning the model every day as more data came to light.
But they stuck with the excessively high numbers for too long. Was it because they didn’t want to admit their bad guess or was it because they wanted high numbers to hang out there long enough to shut down the country?
People outside of science don’t grasp a model needs time to gain accuracy (see cone of uncertainty). What did the model say at Christmas? Nothing, because it did not exist. Also, does Farr’s Law apply to a lab-manipulated virus ? If not, then what the author labels “over reaction” might be justified caution.
I do not see how the origins of the virus will affect Farr’s Law. The forces that cause a man-made virus to be deadly will be no different from the ones that make natural ones deadly — and once released into the wild, even a man-made virus, for all intents and purposes, is a wild virus.
Kerosene! When I was 16, I crushed my finger between a 300 lb engine block and a steel frame. My dad yelled, “stick it in the Kerosene!” That was in the early 60’s. Come to find out, he was correct. Kerosene stops bleeding among other things. Common knowledge, as they call it. Could snake oil have been kerosene? Hum… Anyway, this enlightening article somehow makes me think of Kerosene, Snake oil, and Salesmen. The WHO has been touting itself as experts on all things Health. Yet time after time, it seems that it is all about MONEY. Where is the Beef? Sorry, but I cannot help but believe the American taxpayer has been bilked for a lot of money based on false stats. This virus will run its course. Common sense dictates that we should stop using models created by “experts” who have been proven wrong time after time. We are being sold a bill of goods based on false predictions. Thanks to the many common-sense physicians who get it.
Models also bring in vast amounts of funding for the same people that are creating the models…
Thank you for continued reasoned analysis. It is time to return to the new normal, where individuals choose the appropriate risk tolerance based on their individual circumstance and level of fear – but without government mandated restrictions that come at great cost to the economy and a great increase in domestic violence, internet trafficking and a host of other unintended consequences. The partisan nature of this response has been disheartening and the over-reporting of COVID deaths absolutely dishonest and anti-science. I will follow the “Trump treatment plan” if I or my family member becomes infected – based on research by a trusted and knowledgeable physician in my family who understands the potential side effects (he understands, I understand, that no treatment is proven to FDA standard until multiple studies which take time that we do not have) – and I hope, I pray, use of a ventilator can be avoided.
I am an emergency physician in the US. I was logically skeptical of the threat posed by Covid-19 based on the initial data and the opinion of those in leadership positions. Leaders essentially across the world downplayed any significant risk even into March. Government health care experts (including Dr. Fauci initially) echoed these sentiments while agencies such as the CDC and WHO took a lackadaisical approach. Travel restrictions were late and limited. Testing and data collection was also late and limited. There was no coordinated effort to instruct citizens to take precautions – wearing masks, washing hands, limiting large gatherings, etc. Unfortunately, China has been dishonest from the start and delayed informing others of the threat. The data from China is not only unreliable but most certainly fabricated – the number of deaths might easily be 100 times the official number. This false data clearly indicated the threat could be contained well below the usual seasonal influenza. Prior experience with past pandemics with catastrophic predictions that were never fulfilled also informed my skeptical opinion. I agree that the models were ridiculously pessimistic and therefore, not useful. The final death toll should be well below the initial catastrophic projections. But the mortality numbers in the US will be quite high. They currently stand at over 40,000 and could easily reach 100,000 – 150,000 or more over the next 2-3 months by my estimation. Many who have died required prolonged intensive care. And there is significant morbidity as well with many who survive requiring supplemental oxygen or ventilator support. The surge in severe illness has severely stressed our health care system and many more might have died had we not enacted “lock downs.” The lock downs likely both limited the spread and also limited other activity which might have increased the strain on our system – limiting major trauma for instance. If the system became overwhelmed, many more might have died for lack of care – both from Covid-19 and other illness. Having seen this disease up close, I am now aware that it is significantly worse than I anticipated – although not as bad as certain preposterous models suggested. And the impact cannot be easily defined in simple final mortality numbers. We also will not know how much these numbers were affected by our counter measures. It does seem that models suggesting a range of possibilities might have prompted better initial assessment and management of this threat. This might have involved more stringent initial travel restrictions from the area of the outbreak, aggressive early testing and quarantining and better guidance from health authorities on individual precautions. This might have averted the later draconian counter measures which were likely much less effective and much more costly. China’s dishonesty certainly played a role. Nevertheless, leaders and public health authorities could have performed far better. Hopefully, we will learn from this experience and develop a more responsible and less damaging approach in the future.
Sweden is shaming the world as the control study in this grotesque economic experiment. Brazil, too. But Sweden was among the worst–via their hack warm-monger (the UN’s Bert Bolin)–to lead us down what could be a worse path re global warming than what we are doing with coronavirus.
The most we can hope for is that people finally critically examine the role of models and self-interested modellers in bringing economic ruin. My recent book “Scientocracy” documents this repeated idiocy.
Your entire premise is based upon having identified the peaks in existing data, without exploring the limitations of that data, validating your method of identifying those peaks, and with zero caveats to your claims the likes of which you’re requiring from those building the models.
If I’m to divine your methods, you’re looking at number of new confirmed cases per day, and picking the largest most recent number and using that as your peak?
It seems it would be wise at least to point out some possible weaknesses in that data, such as the poor testing coverage (which also addresses many of your other complaints). New cases has appeared somewhat flat. This *could* mean the virus is not growing (or receding, but just generally spreading linearly), or it could mean we’ve reached our current testing capacity. It’s grossly inadequate to at least explore that possibility when the most consistent message we’re hearing about *any* of this from the experts is that we don’t have enough testing. If you’d like to challenge that message, feel free! By all means, that’s a far more intelligent line of inquiry than what you’ve asserted here.
Moving beyond that, surely you have data from other previous deadly pandemics to show that virus growth only has one global peak, and not several mini-local peaks on its growth trajectory to an ultimate peak in a given country? No? So, even assuming we *do* have adequate testing to show actual growth trends, perhaps your identified peak isn’t as rock solid as you would have us believe? Perhaps it’s based upon assumptions you’d rather we not ask too many questions about?
And, finally and most inexplicably, you point to a discrepancy between the best case scenario in early models and the current trend and both discount *any* effect the measures put in place have had, and suggest the *only* explanation is that they were too pessimistic about the possible negative effects, and thereby *couldn’t* be that they were not optimistic enough about the effects of suppression. When the data itself, both in the US and throughout the world, shows a consistent correlation between distancing measures and a very specifically time-delayed slowing of the disease spread.
What I will grant you was a weakness of the models is the same weakness put forth by all of you doubters: everyone seems a little too content to treat the entire US as a single geo-political zone on the assumption that the growth in all geo-political zones will mirror the same trajectories and operate on the same dynamics, without the appreciation or understanding that the US is the largest and earliest country to deal with this thing, both in terms of population and geography, behind China, and that it perhaps makes much *more* sense to break up the US into several sub-zones and understand that some of those sub-zones acted early and others acted late. And so you would have much more success in areas that acted early and much less in areas that acted late.
Other large countries, like India, got started later than us. I know they’ve been home for a few weeks, I haven’t done a direct comparison to their response vs. the US but they’d be a much better comparison country. Unfortunately, the only one ahead of us is China, and they put in much *more* restrictive measures than the US, and few people trust the data coming out of there, regardless of its accuracy.
You strategically avoid a direct comparison between Covid and the other diseases where you talk about the modeling projections. Excepting AIDS, where you quote a 5 year period for an ongoing disease, total deaths for the diseases you mention top about ~9,000. Covid comes out to 175,000 and counting, in 5 months. So, if your point is that modeling isn’t 100% accurate, I completely agree with you. But if your point is that because it’s inaccurate, we should do nothing to try and change things, I completely disagree with you. Covid is orders of magnitude worse. And the models are part of what allow us to react far enough in advance to actually do something about it.
I’ll note that both the worldwide and US responses to Covid are dramatically different than those earlier diseases, and that response reflects the increased deadliness. So, I’m really, ultimately, not sure what point you’re making with your article here, pointing out the inherent inaccuracy of predictions. If it’s that, if we base our actions on the predictions of models that we’ll always overreact to our detriment, that is falsified by recent history where we *did not* react so strongly, and we did not have the concomitant deaths from not reacting strongly. If it’s that Covid is the same as the earlier diseases, that is also falsified by the fact that it’s killed so many more people. If it’s that the disease would have taken a similar trajectory without the action that we’ve taken, assumes facts not in evidence.
So, what’s your point? Really? Prediction is hard? Yeah, we know. So what?
It is time to return to the new normal, where individuals choose the appropriate risk tolerance based on their individual circumstance and level of fear – but without government mandated restrictions that come at great cost to the economy and a great increase in domestic violence, internet trafficking and a host of other unintended consequences. The partisan nature of this response has been disheartening and the over-reporting of COVID deaths absolutely dishonest and anti-science.
The reason that doesn’t work, Bill, is that individuals don’t just risk themselves, they risk everyone they come into contact with if they get sick, and they risk the functioning of our entire healthcare system.
If it just came down to an individual getting sick and dealing with the consequences themselves, then your approach is the one we would have taken from the beginning. It’s the fact that we all have to deal with the consequences of others getting sick that have caused us to *not* leave it up to individuals and their own risk tolerance.
“The reason that doesn’t work, Bill, is that individuals don’t just risk themselves, they risk everyone they come into contact with if they get sick, and they risk the functioning of our entire healthcare system.”
You are assuming that individuals are unable to assess the risks they present to others; this is an unfair assumption, because individuals *do* make these considerations when deciding whether to stay in or go out.
If anything, our public officials have been less capable of evaluating risk than individuals — all they see is the viral risk; they seem to be completely blind to economic risk, and all that entails.
And yes, people can die from this. People aren’t getting the vaccinations and cancer treatments (among other things) they need because of the shutting down we have done to protect ourselves from the virus. People *have* died from this, and people *will* die. But bureaucrats seem incapable of seeing this.
“You are assuming that individuals are unable to assess the risks they present to others”
Not at all. I’m just saying that they don’t have ultimate authority to act on their individual assessment of a community risk – the community has ultimate authority. Same as any law enacted for the common good that abridges personal behavior. Like speeding. We set a community standard and hold people to it, regardless of the individual’s assessment of the risk to themselves or others.
“public officials […] seem to be completely blind to economic risk, and all that entails.
And yes, people can die from this. People aren’t getting the vaccinations and cancer treatments (among other things) they need because of the shutting down we have done to protect ourselves from the virus. People *have* died from this, and people *will* die.”
I know. You know what I’d like to see? Some numbers on that. Because I know, no matter what, people will die. They’ll die from Covid, and they’ll die from other things. And we can’t prevent that. What I’m aiming for is minimizing death. And, subsequently, operating on the assumption that minimizing death maximizes the speed and magnitude of economic recovery. And I think the shut down is the way to do that most successfully.
“Not at all. I’m just saying that they don’t have ultimate authority to act on their individual assessment of a community risk – the community has ultimate authority. Same as any law enacted for the common good that abridges personal behavior. Like speeding. We set a community standard and hold people to it, regardless of the individual’s assessment of the risk to themselves or others.”
This works best when it’s the community that’s taking the action. We’re not doing that right now; we’re acting as a nation, or at the very least, we’re acting like States. And it’s New York City that’s setting the policy for the rest of the nation. In previous pandemics, only places that were shut down were places with outbreaks — it was localized — and everyone else went about their business.
The problem with what we’ve done this time is that it causes the rest of the nation to needlessly suffer because of a few high-risk areas. What’s worse (and New York State is in particular guilty of this) we’ve known early on that the elderly are particularly susceptible, but several States have had the policy that elderly patients who were diagnosed with COVID-19 were sent back to their nursing homes, rather than sent other places until they recovered from the disease. If we’re going to control the disease, we need to quarantine the sick! But we haven’t been doing that.
I’d like to point out that, using your methods, the US has a new “peak” on April 24th, a full 3 weeks after your original assertion of the peak being on April 4th. Sweden *also* has a new “peak” on the 24th.
So it seems as though your original premise is deeply and fundamentally flawed, in exactly the ways that I predicted, and were indeed obvious.
Predicting pandemic cases and deaths through a computer “model” is only as good as the person inputing the data used to determine outcome. I saw this at Ft. Leavenworth, when we ran computer “models” to project battlefield casualties. Teams of officer students wargamed The Battle of the Bulge as an example. A rouge operator input the data which resulted in ZERO German casualties and 10 times the actual American casualties in the course of the 1944-45 battle! So, when I first heard that the CDC was basing their logistical needs on “models,” I KNEW that there was a stick in the mud!
ANYTHING to make President Trump look bad! Geez!
Models have their uses, but prediction is NOT one of them.