In the Asian Flu of 1957-58, they rejected lockdowns (2021)
aier.org
aier.org
The point of the lockdown was not to entirely stop the spread- that was obviously not happening. It was to slow the spread to avoid overwhelming hospitals.
It allowed us to reduce the lethality of COVID-19 by having more resources per sick person.
I assume experts look at a large number of factors to assess whether it should or should not happen and measure the disease by a variety of metrics.
Also I'd assume the urbanization, science, and ability to execute a lockdown changes over 62 years. Also the nature of work is different. Nobody was doing home computer video conferencing in 1958.
If anyone really wanted to compare it, they'd probably need to have some technical expertise in both diseases and also be some kind of specialized historian.
Adjusting for age/health Florida had similar mortality to California’s.
[1] https://www.mercurynews.com/2023/04/02/why-major-study-argue...
Expected deaths from an unchecked epidemic: 330 million × 50% infection rate × 4% fatality rate = 6.6 million.
50% infection rate is the estimated no-lockdown epidemic penetration rate that I remember hearing from an epidemiologist. 4% was the fatality rate in Italy, February 2020, when hospitals were overwhelmed.
Not even close.
Besides, even if the true number was 3.8% or 4.3%, that wouldn't change the conclusion.
First: we do not know that the lethality would have been without lockdowns. We didn't (often) see hospitals turning people away. The lockdowns succeeded in preventing that.
But more importantly, it's not fair to judge people based on the decisions they made in the past with the information they had then, using the information we have now.
Let's presume there was no difference in lethality. We did not know that at the time. But it was reasonable to believe it would be helpful and save lives.
Who is sitting there going "yeah but what if it doesn't save any more lives? Let's risk it because I've got a party planned next weekend".
And the point of the study quoted above is that jurisdictions that did not implement lockdowns did not experience increased mortality
How do you know this? Did you just read it somewhere or is there some meat to it?
There were still, far more deaths from overdose and suicide than Complications of Covid-19.
The original complaint is that hospitals were (and provably so?) given instructions on how to mark Covid deaths for statistical purposes, and those instructions were biased to inflate the numbers. At the time this was labelled as a "conspiracy theory" in order to drive the narrative, and afterwards it came to light.
"Medical examiner records" can't prove this, because they might as well have been doctored. No non-stupid person would write a report talking about patient having serious conditions A, B, and C, and then fill in a field for "cause of death" with "Covid", so of course you won't find any examples of that.
Let's all be honest, the media got caught with their pants down driving a narrative to fit what they felt was right and what "TPTB" wanted, and we're all in denial now about it. The truth will eventually come out.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9628515/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637007/
https://www.frontiersin.org/articles/10.3389/fpsyg.2021.6694...
A pretty uncontroversial view among those who had kids or worked with kids during that time.
Here is a great starting point from 2021. https://brownstone.org/articles/studies-and-articles-on-mask...
They are a failure. Globally. https://www.amazon.com/Unmasked-Global-Failure-COVID-Mandate...
One of the largest peer-reviewed European studies says... "These findings indicate that countries with high levels of mask compliance did not perform better than those with low mask usage." https://www.cureus.com/articles/93826-correlation-between-ma...
Why? Because they are ineffective and were only introduced as a way to busy or "pacify" the masses.
But the difficulty for proving masking helps is: 1. If it only takes ~20 virions to get infected, then masking has to have a basically perfect filtering rate and be worn perfectly 100% of the time, neither of which is obvious. 2. With an endemic virus even if masks slow down spread they aren't going to reduce your likelihood of being infected overall.
> Consistent use of a face mask or respirator in indoor public settings was associated with lower odds of a positive SARS-CoV-2 test result (adjusted odds ratio = 0.44). Use of respirators with higher filtration capacity was associated with the most protection, compared with no mask use.
I'll take a reduced risk, even if it's not perfect. Would you prefer to get covid one time, or five times? It's like arguing "why bother wearing a seatbelt, when it doesn't prevent injuries in accidents?"
Also, are you suggesting masking does not help with preventing influenza infection?
Also psychical stress from prolonged withdrawal from most if not all social interactions. We fucked up economy pretty badly and still seeing the results of it, made millions of financially very vulnerable people lose their job or family business overnight. And so on and on. Typical HN crowd is probably the least impacted so we are not a good sample to discuss this objectively.
In number of deaths alone yes it was without question the best course of action. The bigger picture, I am not so sure. And cases like Sweden show that restrictions were mostly stupid flexing of control powers by politicians who were generally during crisis clueless as fuck, and the harder they went the more they polarized non-trivial part of society. Again we still see the effects and they are not going anywhere, ever.
If I would be and old and frail person in high risk category, I personally would take all effin' risks and don't screw up childhood (and conversely rest of life) of my grandkids. But that's just me.
I wish this meme would die. Not only did Sweden not do well in the pandemic, they also ended up enacting a ton of restrictions, some more stringent than what much of the US had in practice.
Not regardless of age though apparently?
Which is 0.37% of the population, which is not that high.
The US is right around 0.33%, but that’s also because only 1/3 of us have officially caught it.
Still? We've only just begun dealing with this fallout. It will be effecting us for a generation.
Edit - to elaborate, I just mean that it made sense to try to keep infections down and give companies time to develop a vaccine. Not sure why this struck a nerve.
Unlike in the 1950’s, we knew early on we could produce an effective vaccine. The vaccines that were eventually developed were designed within weeks of the viral genome being sequenced.
However we also didn’t know how to stop people from dying from covid at the beginning. It took some time discover pronation and other treatments that’s improved mortality.
Finally I would note that the articles celebration of a five times worse outcome as some sort of victory is survivor bias at its worse. “See, everyone who didn’t die didn’t die!”
I think there will be plenty of review of what was effective and wasn’t. Maybe lockdowns lasted too long. Maybe they didn’t. But this article is less than convincing.
Their metrics are also off. In 1959 the US had a population of 159m. In 2020 it has a population of 331m, a 208% increase. However in the pandemic 1.14m Americans died. They quote 0.119m deaths. How they come to the conclusion of “with a broader reach for severe outcomes than Covid-19 of 2020,” it’s hard to tell.
Of course, the source isn’t exactly unbiased.
How?
This is an interesting read… one of the challenges of the revisionism in articles like the original link is they simply ignore all context and lie about any context they include (deaths per capita being more extreme in 1959 etc). Besides being viral outbreaks almost nothing of the context is similar. That’s why people say in the thread this is an anti science article - a huge amount of the decision making context was the state of science and how amazing it is in 2020 vs any other time in all of humanity.
That claim is pretty inconsistent with the marketing slogans and messaging about the purpose of the lockdowns.
Similarly, the vaccines turned out to be ineffective for inducing sterilizing immunity, but good for diminishing the severity of symptoms, which would also result in fewer sick people in hospitals. But again, the marketing and messaging around the purpose and intent was completely incongruent with the reality, seemingly intentionally deceptive at times. If you didn't take the vaccine, you were literally killing and holding everyone else back because the vaccine would ostensibly make you immune and prevent the spread.
The biggest failure of Covid was the disingenuous communication.
"Disengenuous communication" is a vague term. I think rather that the biggest failure in the USA was for the government to intercede at all and to adopt protocols that delayed immediate treatment. By delay of treatment I mean that the protocol doctors were instructed (and later forced to use) was:
1. Tell the patient to stay at home (do NOT visit the doctors' offices) until (s)he can barely breathe and to then
2. Go to the hospital where they can hook you up to a ventilator and where you will die,
Rather than
3. immediately prescribe hydroxychloroquine (or ivermectin, or other...) and antibiotics (to prevent secondary infection, not the Covid virus) for home use, and go to the hospital only if it gets significantly worse.
Countries who jumped immediately to 3 had far fewer fatalities (indeed, far fewer hospitalizations) than the USA, who followed the steps 1-2 (and omitted 3 or did 3 in a hospital setting far too late to work), above. Consequently the USA suffered extremely high Covid deaths:
https://duckduckgo.com/?q=usa+covid+deaths+more+than+other+c...
Even worse, it appears that the USA "powers that be" did that to justify established institutions and corporations who stood to make billions of dollars creating not-yet-existent (and later incompletely tested) vaccines instead of providing cheap and effective treatments that were readily available and that worked.
Additionally, hospitals had years to figure out a better way of expanding their ICU's for emergency use. They also tend to operate on lowest required beds to save costs and began to quickly get overwhelmed due to their unpreparedness.
"It allowed us to reduce the lethality of COVID-19 by having more resources per sick person. "
This is false and there's no data backing this claim up.
The ventilator argument is brought up a lot so I wanted to share an article going over the related evidence:
"Based on the evidence at hand, we can reasonably say that there probably wasn’t a large impact either way from early intubation treatment paradigms."[1]
Uninformed and wrong. ICU staff worked through extreme conditions to save many. Ask a few how much better their patients would have fared at home instead.
> Additionally, hospitals had years to figure out a better way of expanding their ICU's for emergency use. They also tend to operate on lowest required beds to save costs and began to quickly get overwhelmed due to their unpreparedness.
Expanding ICUs is not a matter of furnishing rooms, it's a matter of finding people with the requisite skills and experience. A localized need can be met by moving people around, but when every hospital in the country needs to grow its staff there is no way to conjure up more of this sort of person.
Keeping in mind these are fat-tailed distributions. So an R naught of 2 is a estimate of a true R naught, and because using sample averages to estimate means (true R naught is a mean) is unstable (the fatter the tail, the less moments of the distribution there are), once the reported number starts getting upwards of 2, the less confidence you can have that that reported number represents the true R naught. You have know idea if the superspreaders are infecting 50 or 500 people.
At the beginning of the pandemic, we were seeing R naughts estimates at 2.5. That's shockingly high. People were right to take extreme members in the face of that kind of uncertainty.
These people enforcing their values with 20-20 hindsight when the decisions were made given limited data at the time are causing harm to our collective ability to make decisions in emergencies.
"R_{0} is not a biological constant for a pathogen as it is also affected by other factors such as environmental conditions and the behaviour of the infected population.
R_{0} values are usually estimated from mathematical models, and the estimated values are dependent on the model used and values of other parameters. Thus values given in the literature only make sense in the given context and it is recommended not to use obsolete values or compare values based on different models. R_{0} does not by itself give an estimate of how fast an infection spreads in the population".
i.e. R0 numbers are worthless. You can't compare these values between pathogens, you can't even compare them between different academic groups studying the same pathogen or between outbreaks of the same pathogen as modeled by different versions of the same software. There's no agreed way to measure this value empirically.
Instead they write a program to simulate the spread of a virus through population and then (in effect) brute force one or more of the inputs until the results of the simulation match what happened so far. These variables are often given names that sorta reflect an intuition about what they're supposed to do, but they're just names. The actual values are arbitrary and there's no effort to ensure the computed values connect to the names in any plausible way.
A funny example of this was a COVID model from University College London that used household size as a free variable. The model used this value to achieve curve fit for each country in the study, so they reported that the average Brit lives with seven other people (the paper has 73 citations).
https://miro.medium.com/v2/resize:fit:1400/format:webp/1*sEv...
More seriously, estimates of R0 for SARS-CoV-2 have ranged from 1.5 to nearly 6, maybe even higher. It's not even clear what the definition is because it's not time bounded in any way. So not a value that's really knowable, like the speed of light is.
InB4 "clouds don't exist, nobody can tell exactly where they start and end"
To get meaningfully constant values for infectiousness you'd probably need a microbiological / genetic theory that let you derive it from RNA sequences.
As others have already noted in this thread, the rate at which a disease spreads inherently depends on both the biology of the pathogen and human behavior. For example, if everyone goes to crowded nightclubs, then R0 for an airborne respiratory disease goes up. If everyone stays home, then it goes down. The biology of the pathogen and human behavior interact in complex, multidimensional ways; promoting condoms will reduce R0 for HIV, but not for influenza. The disease spreads only through human behavior, so the concept of infectiousness independent of that simply doesn't exist.
You are correct that R0 is determined by curve-fitting to actual case counts. It couldn't be otherwise though, since that's the only way to capture the actual human behavior. We expect different R0 in different situations, so the range you see for SARS-CoV-2 isn't a surprise. This is textbook stuff:
> Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R0. Because R0 is a function of the effective contact rate, the value of R0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R0 values (range 5.4–18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R0 values of 3.7–203.3 (23). This wide range highlights the potential variability in the value of R0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances.
https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article
As with your confused understanding of PCR specificity, you may have been misinformed by indefensibly oversimplified public messaging as to the definition of R0. (I had a comment deleted from /r/coronavirus in 2020, because they considered my statement that R0 varies with environment and human behavior to be misinformation; the citation didn't help.) The literature is once again available for anyone who wishes to read it, though.
So the confusion is the other way around. An outsider to the field would think R0 would be defined biologically given it's called the "basic reproduction number" and because epidemiologists themselves regularly make claims like "influenza has an R0 of this and measles has an R0 of that", but it takes all of five minutes to discover that the way it's calculated can't support such statements.
That's why as presently defined it's useless. If you can't compare the values with any other value, what are they for? Put another way, claims about R0 aren't falsifiable.
Epidemiology needs to develop far more robust methods that aren't just applying R-the-software to random datasets scraped from the web if it wants to be taken seriously as a field. There are very basic philosophy of science issues here. Argument by textbook gets us nowhere, because the textbooks are themselves written by people engaged in unscientific practices. The expectation by outsiders is reasonable, the actual way things operate isn't.
Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. Once you've got a base definition that lets different labs replicate each other's numbers, you can start to incorporate other aspects (under new variable names) like immune system strength, population density grids etc.
Such definitions won't let you give governments predictions of hospital bed demand right away, and indeed may not let you calculate much of real world value for a while, but the field isn't able to do that successfully today anyway. COVID models were all well off reality. What it would do though, is put epidemiology on track to one day deliver accurate results based on a firm theoretical foundation.
That said, SARS-CoV-2 really does spread faster than influenza; among other reasons, we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population that clearly resulted in R0 > 1 for SARS-CoV-2 resulted in R0 < 1 for influenza. That's a relatively trivial and obvious claim, but it's falsifiable and it involves R0.
It's pretty common to reduce a time series to a single number. For example, in economics, it's common to look at a compound growth rate per year, averaged over the period of interest. Likewise, in epidemiology, it's common to look at a compound growth rate per estimated serial interval, averaged over the outbreak and corrected for immunity acquired during the outbreak. That's R0, with all the convenience and all the flaws of any other simple aggregate statistic.
> Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only.
I hope you realize that biologists aren't all stupid? If they could somehow define "infectiousness of the pathogen alone, without environmental factors", then that would be incredibly useful, removing all the factors that complicate comparisons of R0. The fact that they've made no attempt to do so should be a clue that the concept that you're wishing for simply doesn't exist.
They do study growth rates in cell culture, or the amount of virus exhaled by a sick lab animal, or the amount of virus that a healthy lab animal must inhale to get infected with some probability. Those are well-defined and somewhat repeatable lab measurements, but they're not very predictive of spread in actual humans. Computational methods are even less predictive; the idea of calculating infectiousness in humans from a viral genome is mostly science fiction for now. They're trying, but this may be harder than you think.
We don't know this no, what you said here is just an assumption. There's a competing hypothesis (viral interference) which seems to explain the data better.
The way epidemiology currently works just cannot tell us which virus is more infectious. I agree that a rebase onto microbiology would be hard and maybe fail but the current approach has already failed. Doing difficult research that might not pan out is how they justify grant funding in the first place.
How would that explain why SARS-CoV-2 suppressed influenza, instead of influenza suppressing SARS-CoV-2? If viral interference occurs (which I agree it may), then the two simultaneous pandemics are coupled; but you'd still expect the virus with higher R0 to win.
That conclusion doesn't follow from the predicate. The model isn't a single simulation run. To mathematicians, the model is the entire framework. It's the SIR model, or the SIS model, or the SIER model, or my favorite model of immunity decay against pertussis... _those_ are the models. The virulence of the infection being modeled, resilience of the population, morbidity and mortality rates, rate and timing of population quarantine, vaccine adoption rate, vaccine efficacy, whether any of the aforementioned values are themselves functions of time... those are just parameters into your model. We absolutely compare the values between pathogens _within the same model_ when discussing the utility of the model.
But typically, the utility in reproduction ratio is as a point of comparison as you tune other parameters. This is part of why a comparison of reproduction ratio across models isn't recommended. It's not just that it's hard to draw meaning from the comparison, it's that the base assumptions of the models might be too different for the comparison to hold any meaning. The reproductive ratio between a continuous model with uniform population mixing is fundamentally different from the reproductive ratio approximated from discrete simulation. They may both broadly speak to "if everyone they saw was susceptible, how many people do you expect to get sick per sick person?" But what that ratio means is dictated by the context of the model.
A frequent utility in comparing ratios is to discuss intervention impact. One might write "Our model found that overall reproduction was reduced to a factor of 0.XYZ (comprehensive infectivity parameters a=nnn b=mmm c=ppp) when the population undertook vaccination schedule foo. Conversely, reproduction was reduced to a factor of 0.JKL for the same infection under vaccination schedule bar. See Figure 14 for complete population state levels over time."
That's all that is. It's a number that has some meaning in context, and one that is often removed from that context and unreasonably expected to retain its meaning. Averages don't mean as much if you don't know if your distribution is multimodal. It's the same thing - a summary stat that can give you a glimpse of the whole, but still just a summary stat.
> Overall, we report R0 values are likely be between 4.7 and 6.6 with a CI between 2.8 to 11.3
That's an extremely wide CI by any measure. It's a bit unclear how this is meant to be a contradiction. It looks like a good example of the issue. There is no universal theory or method for computing R0. Every single epidemiologist has their own unique approach which is then often discarded in time for the next paper, making the numbers incomparable.
Earlier, you wrote:
> It [R0] was never being established via lab work, as you might expect given its definition.
If you expected that "lab work" could establish R0, then you've grossly misunderstood its meaning and definition. It seems like you're looking for a concept of "R0 but for the pathogen alone, independent of environment and human behavior". That just doesn't exist though, any more than you could define the growth rate of a plant independent of weather and soil fertility.
> the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real
Morpheus: "What is real? How do you define, real?"
CIs that wide are just an obfuscated way of saying "we have no idea what's going on or what will happen". Anyone can make predictions that way. For example by the end my life my bank balance will be $4.7 million (CI $5.00-$50M). Those numbers aren't "real" in any meaningful sense. Anyone can express don't-know in sophisticated looking numerical form, and they wouldn't justify me claiming to be a financial expert on the back of them.
> any more than you could define the growth rate of a plant independent of weather and soil fertility.
You can define growth rate of a plant this way: define a standardized lab environment with regularized soil composition and artificial light/watering schedules. Then plant seeds and measure the dry weight at the end of a fixed time period. This will give you a number that's comparable across species. There are other definitions you could use because "growth rate" is slightly ambiguous in English (does a tree grow faster than a weed because the tree achieves bigger mass?), but that's the general idea.
What you shouldn't do is just grab datasets of wildly varying quality off the internet, fit an equation you just invented on the spot to that data, and announce you've discovered something real about the plants in the data. That would indeed yield a growth rate that doesn't tell you anything meaningful.
If the standardized lab environment is dry and sunny, then you'll conclude that a cactus grows faster than a fern, since the ferns will mostly shrivel up and die. If the standardized lab environment is moist and shaded, then you'll conclude that the fern grows faster, since cactuses will mostly die for lack of sun. So which is right?
The concept that you're looking for simply doesn't exist--the growth rate of an organism can't be defined except with reference to its environment, which for a virus that infects humans includes human behavior. (What rate of condom use should the standardized lab environment for HIV correspond to? How will you model the increased popularity of fentanyl?)
You are looking for CS-level rigor and simplicity in biology, but biology doesn't work like that. You are correct that many biological results were thus oversold to the public during the pandemic; but you're once again criticizing those public-facing oversimplifications, not any science as a practitioner would understand it.
Likewise you wouldn't try to measure the infectiousness of HIV in people, clearly. If you want to measure the relative "infectiousness" of viruses in humans using precise numbers then you'd need a controlled experimental environment, presumably something in vitro. That would miss a lot of factors that are important if you're trying to predict epidemics at the society-wide level, but OK, so be it. You need a firm footing of the basics before you can progress to more complex scenarios.
I don't really agree that it's unreasonable to expect CS-level rigor in biology. Microbiologists seem to manage it? It's expected that if two labs sequence the same organism they can in principle get the same DNA sequence, and if they do Xray crystallography on the same protein they'll derive the same structure. So we're not even comparing biology and CS here, we're comparing microbiology with epidemiology. The latter seems to be far closer to a social science in terms of its methods and rigor.
To be clear, it's also fine to do epidemiology using less rigorous methods if it was done in the way it mostly used to be done. When I read papers from the 80s they seemed to be much more appropriate to the actual data quality - largely prose oriented, very limited use of maths, presenting falsifiable hypotheses whilst admitting to the big unknowns. That's fine, science doesn't always have to be precisely quantifiable especially on the margins of what's known. But if scientists do precisely quantify things, then those quantities should be well defined.
That's how EE/CS stuff usually works (at least outside ML), building complex systems hierarchically out of well-understood primitives. The life sciences are different. There's almost nothing there we understand well enough to build like that, so almost all results of practical importance (a novel antibiotic, a vaccine, a cultivar of wheat, etc.) are produced by experiment and iteration on the complete system of interest, guided to some extent by our limited theoretical understanding.
This discrepancy has been noted many times; it's just a completely different way of working and thinking. If you haven't, then you might read "Can a biologist fix a radio?".
> Microbiologists seem to manage it?
A grad student in microbiology can grow millions of test organisms in a few days, at the cost of a few dollars, and get all the usual benefits of the central limit theorem. A grad student in epidemiology absolutely can't, since their test organisms are necessarily people. So you're quite correct that it's basically a social science, since it depends on aggregate human behavior in the same way e.g. that economics does, and is therefore just as dismal. Unfortunately it's also the best and only science capable of answering questions of significant practical importance, like whether the hospitals are about to be overrun. I'd tend to agree that stuff like Imperial College's CovidSim has so many parameters and so little ground truth as to have almost no predictive value. R0 seems fine to me though, and usefully well-defined, in the same way that the CAGR of a country's GDP seems fine.
In the life sciences, it's often possible to design an experiment under artificial conditions that will get a repeatable answer, like the growth rate of a plant in a certain controlled environment. It's much more difficult to use the result of such a repeatable experiment for any practical purpose; consider, for example, the steep falloff in drug candidates as they move from in vitro screens (cheap and repeatable, but only weakly predictive) to human trials (predictive by definition, but expensive and noisy). I'm absolutely not a life scientist myself, in part because I think I'd find that maddening; but essentially all results of practical benefit there came from researchers working in that way.
The infection rate is always conjectural, not really empirical. It’s impossible to discern without universalized, random, and thoroughly accurate testing, tracing, and tracking. Those conditions have never been met in any country or any pandemic. So what seems to be a measure of an existing reality is really true only in theory, not realistically discernible in the midst of a pandemic. At best, it is an estimate.
Masks are ineffective at lowering the R Naught. Social Distancing might.
My own government got very excited by the prospect of dictatorial central planning that lockdown mania created an enabling environment for and immediately set about writing all kinds of complicated rules about what food and clothing people were allowed to buy, as well as putting a prohibition on alcohol & cigarettes and imposing a curfew. Pandemic response immediately became a vehicle for imposing by fiat whatever pet policies ministers could vaguely link to it. Years later, they've all been rolled back, but the damage was done, and everyone still caught Covid.[1]
[1]: https://www.groundup.org.za/article/nearly-everyone-in-south...
If it was that deadly and every knew someone who died, they wouldn't go outside.
It's not quite that simple. Some people might be forced to for work or similar. Which the law can provide protection against.
This is not a popular opinion but I think the fundamental mistake of lockdowns, at least in North America, was starting from a top-down approach instead of bottom-up. People are going to act like selfish assholes. We ordered them not to. I think instead of threats, we should have aligned their selfish interests with the public good by making it easy to sue someone for infecting you. Could you prove that in court? 99% of the time absolutely not. But the fear that killing other people might hit them in the pocket book would, in my opinion, have made a lot of the assholes put on a mask or skip that concert.
It was negligent to live as if there weren’t a huge pandemic.
I don't understand what point you tried to make. There were naturally essential workers that were excluded from the lockdown. So what? What's your point?
> My own government got very excited by the prospect of dictatorial (...)
I'm going to cut you right there because you're diving into loony conspiracy territory, and one which was already widely proven to be utter nonsense.
The point of lockdowns is to hinder the spread of a disease so that emergency services had a better chance of coping with the demand without being overwhelmed.
Where I lived, the local government had to commandeer a sporting venue to temporarily store dead bodies. Because hospitals and mortuaries found themselves over capacity.
Some responsible people staid home voluntarily. Others could not stay home because they were front line workers. And then there were the sociopaths and morons who even went out of their way to violate even basic health and safety rules, such as spitting on people on the street.
There are two variables here. How infectious the disease is and which percentage of the population can isolate themselves at home without society breaking down.
If everybody stays home you don't have hospitals, you don't have electricity, nobody picks up the garbage, and people will go hungry. Needless to say, that doesn't work. So what percentage of people still need to go to work? And it turns out you need a lot of people to work. From elderly care to daycare, from hospitals to supermarkets and their entire supply chain. And those people will inevitably get sick and infect their family and so the spread continues.
And how infectious is covid? Very, and variants increasingly so.
Which means you can use measures to slow down a disease like covid, but you have no chance of stopping it completely. And that's something some governments refused to accept, and they enacted a ton of erratic and ineffective countermeasures in a desperate attempt to do something impossible, instead of taking a more measured approach focused on slowing down the spread and increasing hospital capacity.
Some people will insist that government policy was in fact reasonable and measured, but it really wasn't. Deutsche Bahn still required masking in January. This year. 2023. I kid you not. It's totally absurd.
No, I do not think so. The curve in "flatten the curve" was referred to the daily number of cases, and the impact it's growth had on saturating health care services. Lockdowns hindered the spread so that services could be able to respond to the daily inflow of new cases.
> If everybody stays home you don't have hospitals, you don't have electricity, nobody picks up the garbage, and people will go hungry.
This is a totally disingenuous and completely wrong strawman, and one that springs either from intentional ignorance or outright bad faith.
No, you don't lock people up and expect everyone to stay in house arrest. You are pretending that the whole concept of "essential workers" didn't existed, let alone was a central point of lockdowns. People were arguing if occupation X or Y should or should not be classified as an essential worker explicitly because that meant either the workers should or should not stay at home.
I was surprised Germans didn't do anything about that policy. They did seem to widely ignore it, but it remained enforceable.
It was to slow the spread. It was as the parent comment said - to slow the rate of hospitalization so the medical system wouldn't collapse and have needless excess death from lack of capacity.
Herd immunity? Never happened because infection isn't preventing reinfection with a different strain.
If there had been actual, effective lockdowns early enough they might have worked but by March 2020 the cat was out of the bag. Japan's model of universal masking, testing, and outdoor interaction without lockdowns is the best model for the future.
Full lockdowns certainly wouldn't have been allowed to last long enough for the vaccine to be produced, especially if that research, development and production had taken as long as it was initially expected to take.
The western covid public health response was a bit of a disorganized mess, but even there it ended up being remarkably effective. I was amazed that we had almost two years with virtually zero flu infections, which showed the vast difference between the infectiousness of normal influenza and covid. Unfortunately, covid was such a stubbornly infectious little bugger that it only slowed the progression.
"Herd immunity? Never happened because infection isn't preventing reinfection with a different strain." This is exactly wrong. Once you get covid you may contract a different strain, and if you are a normal healthy individual it will be like a bad cold, if you have symptoms.
Re reinfection, good luck with that. Every time you catch covid you have a one in ten chance of long covid, and that's the current conservative estimate in meta analyses.
I went out of my way to study especially some of the mathematics used for modelling projections into the future.
I not only read about the SIR model, but poked around a few of its variants like SIRV, SEIS, MSEIRS, etc... I even ran a few up in Mathematica and toyed around with different parameters, trying to get a feel for how the errors accumulate and what can and can't be predicted.
Just that little tiny corner of the pandemic response is insanely complicated once you get into the weeds. You have to account for what fraction of the infected actually turn up for test. What fraction of the tests are false positives or negatives. How fast mutations spread and distort the test results. How other countries or even counties report their statistics differently. And so on, and so forth.
Once you get a good feel for a tiny part of the big picture like that, it makes it much easier to gauge who's spouting bullshit, and who's also been in the same weeds as you have and is covered in cuts and leeches.
At the end of the day, the numbers said 'x'. Everyone that sounded like they knew what they were talking about agreed with 'x'. Everyone who sounded like they were making things up to suit their selfish agenda never used terms like MSEIRS, "infection-to-test ratios", or any such thing. They also seemed to use many different numbers, sometimes making up new numbers in the same conversation. More commonly, they wouldn't use numbers at all, and talked instead about freedom or human rights. Emotions and feeling that might get hurt.
Here's a number: so far at least 6 million people have died, of which 1.1 million were in the United States alone.
The numbers said it would have been a lot worse if prompt action wasn't taken. Ten times more deaths or even worse were entirely within reasonable projection bounds.
Yet... people keep arguing as-if 6 million people hadn't died. As if hospitals hadn't been overwhelmed. As if we lived in some sort of counter-factual universe, or we could choose to live in such a place through sheer force of will.
As if we could berate the virus into not mindlessly replicating exponentially in order to appease our God-given right to freedom and profit.
Did you know that not one healthy individual under 30 died from Covid?
Yet, we locked them down, took away their education and ruined their social development at a young age. Shame on you.
In fact, it's not even close. The article claims 116k US deaths attributable to the Asian flu. Meanwhile, there were 10x that number of excess US deaths during the COVID pandemic.
https://ourworldindata.org/excess-mortality-covid
As others have pointed out, the AIER is a conservative think-tank. I don't know where they're getting their casualty numbers from, but it's not from any reputable source.
This is clearly biased garbage.
Edit: COVID directly caused 1.1 million verified deaths in the USA. We were second in the world in COVID deaths as a percentage of the population.
First the population size, as per the article, was half what it is now. So you can either double the asian flu deaths or half the covid deaths to account for this.
That still leaves a huge gap, but this is where demographic analysis comes in.
Usually you see this as age adjusted deaths.
In this case, the population of the US has been aging for a long time so you would expect more deaths from covid than the Asian flu for an equally deadly virus.
The other demographic change that is important, especially when talking about covid is rates of obesity, which is something else that has grown over time in the US population.
That's a really long winded way to say you can't just compare raw numbers.
The article doesn't give an explanation of how they came to their conclusion, but if you take population size, age distribution, and obesity rates into account, I could see the adjusted numbers being close.
Those are confounding factors, but I think it's quite a stretch to believe they would provide a 5x adjustment (after accounting for 2x population), especially if you also adjust for the drastically improved quality of and access to healthcare, which would skew the adjustment in the other direction.
>Among 192 405 448 persons receiving a total of 354 100 845 mRNA-based COVID-19 vaccines during the study period, there were 1991 reports of myocarditis to VAERS and 1626 of these reports met the case definition of myocarditis.
https://jamanetwork.com/journals/jama/fullarticle/2788346
Myocarditis is one of very few side effects from the vaccine, all of which are vanishingly rare. It's well understood that the vaccine was highly effective in reducing the severity and spread of COVID and is one of the safest vaccines ever produced.
>How well it works: Moderna’s initial Phase 3 clinical data in December 2020 was similar to Pfizer-BioNTech’s—at that point, both vaccines showed about 95% efficacy for prevention of COVID-19. Later data on real-world effectiveness for adults showed that the protection from the mRNA two-dose primary series wanes over time, but booster doses bring the immune system back to robust levels.
https://www.yalemedicine.org/news/covid-19-vaccine-compariso...
You didn't ignore the mob, you ignored the science. You listened to a mob of your choosing, and now you are misinformed and continuing to spread misinformation.
I have had all of my children vaccinated with the standard course of immunizations, but I am glad I held off on this new vaccine. My children had COVID, and they are healthy by definition. Tell me how acting on common sense, the history of natural immunity in science, and data that was readily available on who was susceptible to COVID very early on contrary to what "The Science" (aka Social Policy and Politics) was saying , is listening to a mob? Use the same date source you used to pull your VAERS numbers and see how obvious it was who was susceptible by a margin early on, and maybe you'll be able to understand how I arrived at my decision to not vaccinate my family who already had COVID.
>...and is one of the safest vaccines ever produced.
This December 14, 2023 will be three years from the first vaccine given. "one of the safest" is a blanket statement and "one" although a quantity is non-quantifiable in that sentence. The article you site is from data collected from December 2020 to August 2021 only 8 months from first vaccine. Pfizer vaccinated their control group from the initial vaccines, so that long-term study was squashed by Pfizer. And the adverse effects data are still being collected and examined in light of reporting criteria and classification. Yet, the blanket policy to vax everyone without prioritizing by harm-benefit analysis and ignoring the science was not just negligent but intentional to push a social policy over the data. Three years is not long-term in the vaccine world. Tell me, if a long-term effect comes up like higher incidences of leukemia or other diseases or undesirable side effects 5 to 7 years from now, will you look back and feel good about the non-scientific pushing of a vaccine on those who were well outside of any reasonable benefit-to-risk ratio? I am glad my young children didn't get the vax. None in my family have had another COVID symptomatic infection or tested positive since we had it the one time. Meanwhile, "healthy" coworkers with 2 or more boosters have had multiple infections with loss of work even when working from home because they were that sick. So do you agree with vaccines and boosters for the non-susceptible demographic who have already had COVID? I'd be curious on your viewpoint on this.
EDIT: Your name explains it all ;)
This is only after 3 years of the vaccine's first dose. That is a pretty large percentage - 2.8%.
I sadly expect to see more and more of these studies with other unsettling results. To the universities who pushed it upon the healthiest of us, they should be ashamed of saying they did it in the name of science when young people were the least affected, and the elderly could be prioritized.
[1] https://onlinelibrary.wiley.com/doi/pdf/10.1002/ejhf.2978
But I would also note that when you are dying or your loved ones die waving age or obesity adjustments to claim it’s not that bad doesn’t work very well. 1.1m deaths is still 10x more, and even on a per capita, it’s still 5x greater net “mourning.”
Whether the same pathogen would have been worse in one decade vs another I think is sort of irrelevant. The public health goal is the reduce mortality over all.
(N.b., I recognize you probably didn’t mean to minimize anyone’s loss - but I am trying to make the point that demographic analysis might or might not be explanatory of the severity of 2020, it doesn’t make 2020 any less severe)
I am not trying to change your argument in anyway but that specific statement is not correct or its using different numbers than direct COVID deaths. The US is 15th. Peru was #1, and EU members Hungary (#3), Croatia (#6), Slovakia (#10), Greece (#13) and Romania (#14) are all ahead of us.
(Sorry I am pedantic about covid numbers) =)
Yes? That's the definition, no?
Cows are deadlier than sharks, cars are deadlier than planes, even though you're more likely to survive a cow attack or a car crash?
The former is much deadlier - it's basically guaranteed death - but the latter is so much more frequent that it claims more lives even though the vast majority of people survive it. Usually "deadlier" refers to the chances a given event will kill you if it happens, not normalized for the frequency of those events.
The flu most certainly claims more lives, but I doubt most people would agree with the sentence, "being shot in the head is less deadly than the flu."
Yes, getting shot in the head is deadlier than (catching) the spanish flu.
But the Spanish flu was deadlier than firearms/WW1.
That's often the limit of analogies.
No, it pretty much always refers to the case fatality rate.
In more visceral terms, if the doctor says you either have MERS or SARS-CoV-2. What is your first thought, question? Which is more deadlier?
My friend killed himself because of lockdown. I guess he is a covid victim. Very convinient.
Edit: There were 1.1 million COVID deaths in the USA alone.
Pink is total; orange is COVID; blue is non-COVID attributed excess.
https://zmichaelgehlke.com/images/mortality/covid-mortality-...
If you told people "hey, this year the flu is going to be very bad, probably killing 10 times as many people" would the average response be to close schools and many businesses for over a year? Or would the response be to limit lockdowns to vulnerable populations, rather than blanket lockdowns on most of society?
edit: COVID placed a 20x burden on hospitals versus the flu even with lockdowns. That's without counting the increased difficulty of treating a novel disease, and increased time in hospitals for covid vs flu.
Ionnadis was slammed for claiming such a low IFR of 0.27%. But later studies have since reaffirmed his estimates: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613797/ Even when restricting the analysis to the period before vaccinations, the IFR was between 0.3% and 0.4%. Ionnadis was way closer to the mark than the claims of 1.0 or 1.5% IFR that were common at the time.
Lesson learned for the liberals is to listen to those on the right at least from time to time. If they sometimes behave like idiots it doesn't mean they really are. Republicans got it right on the Covid question.
https://www.nytimes.com/2023/03/30/opinion/sweden-pandemic-c...
I'm wondering how a reading of that article could lead anyone to quote it as an example of 'no lockdowns' - its very explicit that in many ways the measures taken in Sweden were even more restrictive (after 2020) than its neighbors in Norway and Denmark - both of which fared better than Sweden, both in deaths-per-million and also in the effect that the pandemic had on the economy.
The truth is that while the initial (and quite brief) open policies were in place, the authorities mistakenly thought that herd-immunity was close to being achieved, and that the astronomically high number of deaths (at several stages, the highest number of deaths-per-million in the world), were worth the reward of achieving it. The government even had one workshop where they discussed with relevant authorities how they would handle the negative press from other countries when Sweden could announce they were 'over' the pandemic (this was naturally before the catastrophic fall and winter of 2020).
After the deadly winter, the government more firmly took the reins from the health authority, and people outside Sweden would be surprised to hear how many restrictions were actually in place: including bans on group meetings, caps on the number of people who could enter a store at the same time, high school kids studying from home, and so on.
But most of all, Sweden is a country where people have lived with 'social distancing' for centuries: Stockholm has the highest number of single-person households of any capital city in the world, and that pattern is repeated all over this sparsely-poulated country. In the summers, a vast number of people leave towns and use their summer-places in the countryside - another tradition that's been going-on for the last couple of centuries. In the summer of 2020 I was in Stockholm, and would often be the only person eating in a restaurant that would normally be packed with people.
All of these factors helped Sweden to keep down what would otherwise have been even more catastrophic numbers of dead. Which isn't to say that the number of people who did die - often without any medical care whatsoever in the case of many elderly people in homes that were supposed to be somehow 'protected' from the pandemic - is not a massive tragedy, that Swedes themselves hardly talk about any longer. Because it really was both a callous loss of life, and a traumatic triumph of selfishness over common welfare.
Saved some of you a click hopefully
and the only legitimate source to ask about the need for copyrights is Disney.
It always cuts both ways. Just because they're against the position you think is obviously correct doesn't mean that you can dismiss a dissenting position. Every story has another side to it. (Well, except this one :) )
If I have to put both the average CEO and the average epidemiologist on a spectrum of "more interested in my well-being" to "more interested in my pocketbook", I know where I'd put each one.
PS: For people who wonder, the H factor-style ranking mechanisms for researchers means that your rank is higher (which tends to translate to more interesting work and/or more money) if you're heavily quoted by other researchers.
Of course, the easiest way to be heavily quoted, unless you're already a highly established scientist, is by being contrarian/provocative, so that other people feel like they need to debunk your writings.
The issue is that they'll fail to appropriately consider the broader long-term effects of lockdowns. Most of the advice and policy also wasn't scientifically based in that the effectiveness wasn't well researched, so H-factor scores wouldn't play much of a role.
That is definitely one possibility. The other possibility being that a group of contrarian epidemiologists would get power and prestige by claiming that the first group is biased. This has happened in France, with Raoult growing a cult-like following.
> Most of the advice and policy also wasn't scientifically based in that the effectiveness wasn't well researched, so H-factor scores wouldn't play much of a role.
Well, in that case, H-factor-style scores wouldn't play much of a role because this was not research but policy.
I seem to remember that the effectiveness of lockdowns on diseases has been pretty much proved way before covid. Of course, their effect on economy may not have been studied quite as well :)
"The right to swing my fist ends where the other man’s nose begins."
* https://quoteinvestigator.com/2011/10/15/liberty-fist-nose/
Towards an individual those things may be fine, but we each live in a society, and how your actions effect others could be a factor in how much each of them may be exercised.
The current government policy is that most everyone should be continually working around 40 hours a week, enforced by a financial treadmill created by continual monetary stimulus. So this noble idea that people could have individually chosen to self isolate or freely associate is fallacious with our current setup. Without "lockdowns" (aka closing businesses), most people would have been forced to keep going into work for business as usual, just as they do for other communicable diseases until the symptoms cannot be ignored.
Centralized control begets more centralized control to address its own failings. Focusing on surface issues while ignoring their underlying causes just makes you end up being a tool for entrenched interests wanting authoritarian policies that benefit them.
I think I know where the road of 'personal freedom, free enterprise, property rights, limited government, and sound money' leads, and I'm also usually quite happy to dismiss anyone shilling for it out of hand.
Just because his offered solution wasn't great doesn't mean his critique was wrong.
Not in an absolute sense: property rights are obviously very important to how society functions. But "the US needs stronger property rights" is in general a harmful position. US limitations on property rights are largely to tax the ultra-rich and to limit exploitation and concentration of wealth. For example, tenants' rights are pretty much directly opposed to the landlord's property rights, and tenants' rights do not (IMHO) need weakening in most places in the US, but instead need strengthening.
The same holds to some extent for the other values, but maybe only in context. (Personal freedom to do what? Not to get an abortion...)
Also, we're talking about a website with a front-page article arguing that reparations for slavery + Jim Crow + other racist discrimination are bad, because slavery is "like winning the lottery" for modern American black people. After all, without the system that kidnapped and enslaved their ancestors and then discriminated against their grandparents and parents and still discriminates against them today -- such that they have less income, less savings, worse life expectancy, triple maternal mortality etc -- they probably would have been born in Africa!
You don't need to be defending these guys.
Not saying they're ultimately wrong, but it's good to know what their biases are in order to put things in context.
... but if they were, it wouldn't be the first time. These guys are also global warming denialists, covid deniers, and they even vociferously defend third world sweatshops.
Lmfao, overreaction much? I suspect Mr. Tucker has never had the misfortune to live in an actually authoritarian society.
Before Salk's vaccine was discovered and widely used (~'54) there was polio hysteria, public places were avoided, "unclean" Italians were shunned, as were cats. It was a ridiculous overreaction. Too soon for anyone to be fooled again.
Strangely, none of that is mentioned in an article about US reaction to a pandemic just three years later.
* Covid killed far more people. One estimate for global deaths for the 1957 flu is 1.1 million—around the number of deaths from COVID-19 in the U.S. alone
* COVID-19 hospitalized more people. Apparently, the 1957 flu did not overburden hospitals. Even with efforts to “flatten the curve,” COVID-19 did.
* COVID-19 is a longer illness. While some people recover in 3-5 days (the article’s estimate for the 1957 flu), 5 days is the minimum isolation period and longer recoveries (not even factoring in Long COVID) are common.
* The vaccine was available earlier in the pandemic’s course, at least in the U.S. According to a Smithsonian article [0], a vaccine researcher was able to convince companies to start manufacturing a vaccine before it became widespread in the U.S.
It also ignores the fact that despite not locking down in 1957, the U.S. went into a recession anyway.
[0]: https://www.smithsonianmag.com/smithsonian-institution/unite...
It's a bit early to be calling it over given its hospitalising millions world wide every week, killings 100s of thousands and disabling millions still. It's a lot worsen than Flu by the numbers so far and it's still successfully raging.
I agree it's not over, but saying it's raging is not correct either. In the U.S. excess deaths are close to zero [0] and hospitalizations are also low [1]. Other countries largely seem to be seeing similar patterns, although I have not looked at their numbers as closely. We could see another wave, of course, but currently we are in a lull. For what it's worth, I think there are still precautions that make sense because they are are effective and relatively low-cost, like masking in crowded indoor spaces and testing before and after attending large events.
[0]: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm [1]: https://ourworldindata.org/grapher/current-covid-hospitaliza...
Elsewhere, the case has been made that the vaccine was developed and deployed rapidly before the virus took full hold, and halted the pandemic in its tracks.
https://daily.jstor.org/how-america-brought-the-1957-influen...
Thanks for digging up that bit of info.
https://en.wikipedia.org/wiki/American_Institute_for_Economi...
3 Years ago I was actually on the other side too. Wow what a journey.
It's not a liberal vs conservative thing per se. There are plenty of liberals and progressives who get basic science wrong. Liberals are more likely to reject nuclear power or GMO food for reasons that are not scientifically justifiable, for example [1]. Liberals have historically also been more likely to believe in medical nonsense like healing crystals and homeopathy and anti-vaccination.
The things liberals are more anti-science on though are also less likely to be the kind of things that require national or international solutions and so attract fewer think tanks.
Hence there is a better correlation between libertarian/conservative think tanks and scientifically unsound positions than there is between liberal/progressive think tanks and such positions.
[1] There is a decent argument against GMO food in places where food is abundant and cheap so avoiding GMO doesn't mean famine, but it has nothing to do with the science of GMO itself. It is that GMO gives food companies much more ability to customize and tweak foods in ways that are great for business but bad for public health. We already know when it comes to money vs health they go for money, which is a big contributor to rising obesity. They can still accomplish many of the same things without GMO that they would use GMO for but it is slower and more expensive which reduces the amount of harm they can do and gives more time to get regulations to catch up.
That being said, what happened in 1957-58 doesn't really apply here. First, population density wasn't nearly what it was today. It was the land of single-family homes and automobiles, not high-rises, dense air travel and public transit.
Secondly, the lockdowns were necessary to prevent the healthcare system from collapsing. Rate-limiting the illness with lockdowns and other NPIs allowed hospitals to remain functional and adapt to the crisis. In Italy at the onset of the pandemic, the Italian healthcare system dealt with collapse and rationing of care, with physicians choosing which people to treat and who to leave to die.
I don't know about society, but this was the straw the broke the camels back for me. I have not been the same since, I don't think I ever will be. The first lockdown in my province was when things really started to go wrong for me mental health wise in a major way and it only got worse as they continued.
What's really been maddening is that people get angry when I mention the affects it all had on me, they assume I'm taking a political stance first and foremost, they tell me it wasn't that bad, and they try to tell me I didn't experience what I experienced or do what I did.
I used to think things could get better, but I really don't see a happy ending for myself anymore.
I know many similar people, formerly of all different political, philosophical and religious persuasions. My rough estimation is that this voting bloc is between 20 and 35% of western society.
To those who disagree: although we are products of the same society, we have simply a fundamentally different view of liberty, happiness and risk tolerance.
I hope for your sake you aren't. If you are though you have all of my sympathy, someone like me looking back is not a good thing.
Ideologues are always saying that anybody who doesn't agree with them must be brainwashed. They can't imagine that could actually choose to hold a different point of view from their own. Well let me tell you it ain't necessarily the case.
US government overthrew Iran democratic government -> "I know the US is so evil!"
US government secretly injecting US citizens with radioactive material -> "My gosh, what's wrong with these people?"
US government lies about reasons for Iraq War -> "One of the great crimes of the century"
Then someone suggests, that just maybe, the lockdowns weren't based on science and that we should be concerned about the government overstepping and the comments are suddenly -> "Wow, did you forget your tinfoil hat?" or "Why don't you believe in science?" or "Think of the children"
It's really weird. The US gets rightfully condemned for wrongdoings, but then suddenly any suggestion that they did it again is met with violent opposition.
Not really. The HN user base has been taken over by big-corporation/big-government shills for years now.
Were there even places that gave you a citation for being out "uneccessarily"? Every single "lockdown" I'm familiar with in the US was voluntary. There was a period where "non-essential" businesses were forced or encouraged to not operate, but even that still isn't a lockdown, and nobody was forced to stay in their home.
Many around my parts were being outright belligerent asshats when all we had was asking nicely that people distance and wear masks. A substantial subset of the population did the exact opposite, and caused all sorts of trouble being antagonistic even to people just trying to buy groceries.
Then the lockdowns happened and it was like a light switch. I rarely saw the same kind of troublemakers acting out that way in public. Apparently once the US starts gutting its own economy to deal with an invisible threat, people start taking it more seriously. Until then it was like they thought it was all fake news, running around mocking the gullible masked suckers.
But why they didn't end far sooner, like maybe once food supply chains were recovering and our shelves weren't empty anymore, is totally beyond my comprehension. It evolved from "flattening the curve" to "prevent as many deaths as possible at all costs", which IMNSHO is where it went all wrong.
Do what you want, everything has tradeoff and risks. But don’t pretend science is normative until it becomes inconvenient.
And it's not like nothing good came out of lockdowns either, particularly the much greater willingness of employers to embrace remote work.
There was like the initial 6 weeks to stop the spread.
I still went to the grocery store, not locked down, i still went to the park, not locked down.
What lockdowns? please be specific.
When people say lockdown, they mean:
"I was forced to wear a mask when i went to the grocery store and i felt like a loser"
Thats the whole issue.
Call it what you like. You're arguing semantics when you say it wasn't a lock-down, but whatever you call it, it harmed people socially and psychologically. Humans aren't meant to be socially isolated.
The differences were highly variable. I have family in rural areas whose lives changed not one iota unless they had to travel for business to more urban areas. I have more urban people in my circle who were locked into their residence almost all the time, working and school from home. And then, there are the grocery store, food delivery, and health workers, who were working until they dropped, short staffed, in a sea of anxiety, many getting Covid multiple times.
There was no single experience of lockdown status even within just the USA, let alone across countries.
Plenty of it I'm sure, but why does that matter? Local policies effected my life even if they weren't federal policies.
Well guys, we weren't physically barred in our homes and starved so we weren't locked down. Brilliant.
Looking back at this thread it seems every post disagreeing with the ridiculous statement that there were no lockdowns has been downvoted. Amazing.
Don't confuse correlation with causality.
You had politicians abusing the covid pandemic with accusations of being a made-up conspiracy and in the process enticing hatred towards health officials who enacted standard public health policies, to the point that hardline militants even attacked them for suggesting that people should wash their hands.
After over a year of this blend of propaganda permeating society, of course you'll have the most vulnerable segment of society, overly represented by the undereducated, building up an uneducated grudge against society in general and in particular against "the system", whatever they were told to believe that might be.
It gave belligerent jackasses as stage upon which to perform. I suspect the grudge was long there, and the chip sat so long on their shoulder prior to the pandemic that it left a stain on many of their shirts. We should not cater to them.
It burned up goodwill and trust for experts and politicians (who generally didn't have much to spare in the first place.)
Again, just an excuse. Throwing the baby out with the bathwater because people that know far more about the subject than you do got a few things wrong, well, that's just being immature and/or supporting a narrative. We should not cater to them, either.
But at least we can agree that closing the schools was a bad idea.
So Covid was only ten times deadlier. But certainly we should have had the same response. Those "libertarian" organizations are getting less and less veiled about their hatred of humanity. Sometimes I wonder if they are some extra-terrestrial cockroaches in human shells out of Philip K. Dick story out to wipe out all humans.
Note that the above is understating things because the 1% Covid CFR number is dilluted by a lot of later covid cases when people were vaccinated and the later less potent virus strains were out. In the beginning with the original strain and the deadly delta strain and no vaccines Covid was much deadlier than that 1% number.
1. https://en.wikipedia.org/wiki/1957%E2%80%931958_influenza_pa... 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451339/
I still think on a numbers perspective that the lockdowns saved more lives than not locking down would have - but it does not help the cause of "keeping that tool in the public health toolbox" to not acknowledge the externalities of its face - its not a completely benign policy without externalities - its just significantly less bad than the alternatives.
I think we should study this more, to try to form a consensus when a lockdown is appropriate. I think a COVID scale illness is, but when does that cease to be true? half of one? 3/4? who knows, we've not spent much time thinking about it yet.