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The scientific facts.

"The present paper about masks illustrates the degree to which governments, the mainstream media, and institutional propagandists can decide to operate in a science vacuum, or select only incomplete science that serves their interests. Such recklessness is also certainly the case with the current global lockdown of over 1 billion people, an unprecedented experiment in medical and political history."

[thewallwillfall.org]

lawrenceblair 8 July 3
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0

Imagine it is 1957. 116,000 people have died of the flu. The US population is 172 million – about half of what it is today,

Doctors today know that creating vaccines against the flu is like throwing darts at a target. Sometimes it works. Sometimes it doesn't work for many different reasons.

Enter politics. Some more “visible” physicians are now talking about “controlling the virus.” “Winning the battle against the virus.” “Preventing this from ever happening again.” Before today, no one ever made statements like that because responsible doctors knew that we don't have that kind of power yet over viruses and microbes. Medical researchers know this.

So in 1957, if we decided to shut down the nation to “flatten the curve” then, we would have experienced then, what is happening now: an extension of the duration of the virus, and an increase of the exposure of those with pre-existing conditions to the virus, and more deaths of that group, than if we did nothing.

And if we decided to mount a massive national test, instead of knowing only about those mortally ill, we would then know about those who are infected and asymptomatic. Would our response be to panic because we conclude that the infection is increasing? Or would we be aware that increasing the testing increased our awareness of the true numbers that we had faced all along?

The 1957 flu was incredibly deadly, and in equivalent numbers, fatalities would be around 220,000 – far higher than COVID-19 today. So the question is: Is our current political solution the right way handle an issue appropriately managed by the medical community in 1957?

Not so. If there were 172M people back then, that's about 1/2 of what we have today. So, that's 60,000 deaths in four months in 1957 numbers. If we have another 60,000 deaths in the next four months, that's 120,000 deaths in 8 months in 1957 numbers. That's 180,000 deaths pre year in 1957 numbers. That's way worse than H1N1. You really can't say, at this point, how bad or not so bad it might get. So, taking reasonable precautions to stop the spread the this virus seems perfectly reasonable. It certainly doesn't warrant the mass paranoid schizophrenia that seems to run rampant on the site.

@TyKC. Read it again. In 1957, 116,000 died of the flu. Look it up. If you want to compute the equivalent death rate with todays population, you take 330million times 116,000, divided by 172 million. That's 220,000 for an engineer like me. I don't know how you do math. The rate per million remains the same with my numbers, and it is higher than COVID-19. We did nothing then, and the flu passed in about 3 months. We are extending it now, so more people with pre-existing conditions are dying now than happens when there is a shorter duration.

@TimTuolomne That's correct, but you have to take the 180,000 and multiply by roughly 2 to get 2020 numbers. You are comparing 1957 numbers to 2020. So, 180000 (1957) X 2 = 360000 (2020). Last time I checked 220 < 360. I'm not saying your 220,000 is wrong. I'm just saying you have to compare apples to apples. BTW, what engineering school did you say you went to?

@TyKC. 1.Where do you get the 60,000 to derive your 180,000? 116,000 died of the flu in 1957. Look it up.
2. Most CDC and other data compares death rates per thousand or million, not time series, which you are using for some reason which I don't get.
3. FYI, engineers love math. If we weren't engineers, we would be mathematicians. What did you say YOUR math qualifications are?

@TimTuolomne Ok, so I'll do this slowly so even an engineer can understand it. First, lets do the problem in 2020 numbers. So, in four months there has been about 120,000 deaths due to Covid-19. This estimate is low, but we'll be conservative. You should like that. So, if the death rate continues the same, then in 12 months, we would have 120,000 x 3 = 360,000. If you wanted to be really really conservative, you could say 120,000 deaths in six months, so we would only multiply by 2. That would be 240,000. But still 240 > 220, being unrealistically conservative. You should like that even better. Now lets do the problem in 1957 numbers. First 172M/340M is roughly 1/2. So we had roughly 1/2 the population in 1957 than we do today. So, in 1957, we should have about 1/2 the number of cases, if the rates are the same. Ok, in four months, then 120,000/2 = 60,000. For a year, 60,000 x 3 = 180,000 in 1957 numbers. To get 2020 numbers, multiply by 2, since we have twice the population now than we had then, 2 x 180,000 = 360,000. Presto, it checks. And 220 < 360.

Your question: What did you say YOUR math qualifications are? My qualifications are that I do the math correctly. I don't compare 1957 numbers to 2020 numbers and then call someone an idiot for not agreeing with my incorrect conclusion.

@TyKC. I don't have any problems with your math. And I defy you to find anywhere in my responses that I called you an idiot. I am trying to respond to what you say and answer you specifically and clearly. Your attitude is disrespectful and a bit nasty, but that's OK.

Have you ever heard of flattening the CURVE? Being a curve, it is not a linear rate. Not the same at all points in time. Your math assumes it is the same and will be the same. It is not a time function. It is a death rate per thousand or million, and you are taking issue with my response, defined that way. Get it?

I'm pretty sure everyone else does.

@TyKC Your clarity in math reminds me of the Princess Bride where wesley say's, " truly you have a dizzying intellect". I found your math difficult to follow. What lawrence was saying is roughly this... 57 1,000 people died....extrapolating that to todays population at roughly twice as much...would mean that twice the population would yield twice the deaths or 232,000. What I think he is not saying that you try to point out is that we're not done with COVID yet, and it's only been 4-6 months. While his point was that most of the flu deaths in 57 occurred in 3-4 months.... I don't know that there is much to argue about (we won't know the real annual deaths until the year is up anyway). His point was more about the way we're handling it, and that our flattening the curve approach may be making things actually worse.

Whether the COVID epidemic turns out to be as bad, 1.5 times worse, twice as bad, he is saying that the 57 flu was HUGE (let's not quibble over relative severity unless we get an order of magnitude 👩 or more apart).

@CoachD1 I must say that you having difficulty with my math is alarming. It's pretty straight forward. The quality of math education in the US has definitely declined. The situation in 1957 is far different than it is today. Back then, by the time the disease reached the U.S, a vaccine had already been developed. You can read about it below if you care. Here's an excerpt from the article:

When the new flu strain hit the United States in September, just as Hilleman had predicted it would, the country was ready with a vaccine. The virus, dubbed the “Asian flu,” killed an estimated 70,000 to 116,000 Americans and one to four million people worldwide, but experts suggest it would have killed many more if not for the vaccine. BTW, the deaths accumulated over 1957 and 1958, not in 4-6 months as you claim.

That's not the case today. Basically, we Bungled the response to the virus. In other countries, who have been employing the same social distancing and mask wearing techniques as the CDC is recommending except on a National, not a local, scale, have been successful in controlling the virus. Their numbers are going down. Europe has opened travel to there except from the US and a few other countries. These are basic facts and are not hard to verify. Our numbers are going up, which is also not hard to verify. They are going up largely because of a lack of National leadership. But I expect that you will have just as difficult a time following this simple argument as you did following my simple math. So maybe you can explain to me why social distancing is fine for most every other country, but it won't work here. I'll wait.

[history.com]

@TyKC It seems that you have issues beyond what I care to get into. And before I continue I will admit that my use of math was actually the same as what I'm about to acuse you of..... used in combination with prose with interjected explanations. It switches the reader in and out of stream of concioussness flow. I am guilty of it, because it is also the way I back up my thoughts. However, mixing the two tends to lose a readers interest.

The beauty of math is it is inherently objective and inherently simple (although tensor calculus takes some real work). When you mix math and prose as you did in your earlier post, it wasn't that I couldn't follow it, it was simply that I didn't wish to because, like I said, unless we're talking about disparity that are off by orders of magnitude 10x, 100x, 1000x, then it is not really germain to the original post.

If we could simply agree that the 1957 flu was really bad with a death rate somewhere within the same realm of today's pandemic then we can move on with the actual content of the his point (instead of being stuck on whether 240 > 220 or 220 < 360).

You seem to be arguing for arguments sake and missing the message. . . That Tim thinks the 1957 epidemic was handled better than the current one. And I think, by your posts, that you at least believe that this one is and was being handled poorly (I'm not sure how you feel about how it was handled in 1957 - and don't really care).

I made zero mention of how I thought things were being handled in the US. I don't know how you're even thinking I either support or have an issue with social distancing. It just seems you have a chip on your shoulder, and want to make assumptions so you can continue to be upset and argue.

@CoachD1 Ok, we will just have to agree that we have very different perspectives on this. Keep in mind what started this discussion. A statement was made and I quote "the 1957 flu was incredibly deadly, and in equivalent numbers, fatalities would be around 220,000 – far higher than COVID-19 today." This is a grotesquely irresponsible statement. It suggests that we are acting irrationally by implementing practical mitigation measures and that the virus really is no worse than the common flu, which cannot, at this point, be determined. In fact, the numbers indicate just the opposite. The statement is at worst blatantly false and at best grotesquely misleading. All I did was to point this out. Trying to compare what happened in 1957 to what's happening today is an exercise in folly. It is simply not comparable. They had a vaccine back then and their attitudes and expectations were different. If we have to say that we are no better off than we were in 1957, then all the so called progress is all for not.

I joined this site because I was led to believe that I would escape the constant drivel of the liberal sites I used to frequent, that it would be a whole new experience. I've been sadly disappointed. The constant bias diarrhea, the continuous affront to common sense, the irrational blather that spews from this site is no better than on the left. This has to be fought, our country depends upon it. Such blather is ruining our country. Somebody has to care. Am is pissed? Yes. Do I carry a chip on my shoulder? Yes I do! If you choose to rationalize that, well it's not that important, because it isn't an order of magnitude greater, so be it. That's not my style.

@TyKC Irresponsible? In your expert opinion?

Geneticists who have analyzed the gene sequence of SARS COV2, which causes COVID-19, are confident of three facts:

1.There are no markers which are unavoidable when genes are spliced (engineered).
2. The forensic path clearly shows a very old gene sequence consistent with a path through bats found in China, but no where near Wuhan.
3. A much more recent forensic path in the gene sequence of the virus shows that it went through the pangolin before jumping to humans.
[virological.org]

The R-nought value of SARS COV2 is 5.7 in 2.5 days, which tells us that the entire globe was exposed to the virus long before the West was even aware of its existence in China.* And that means social distancing and masks were an attempt to prevent exposure that had already happened. [healthline.com]

Furthermore, 99% of those the virus kills are smokers and people of any age with serious pre-existing conditions. Its possible that the remaining 1% also have pre-existing conditions that are undiagnosed. This is why populations with high concentrations of smokers and illnesses, like Italy, have higher mortality rates than countries like Sweden. The numbers are still consistent partly because of the unusually long latency of SARS COV2. And partly because other things are still getting them.
See "Multiple Conditions Increase Risk." [informationisbeautiful.net]

So with a case base that could be 2000 times greater than reported right now, that makes the virus 2000 times less dangerous - far less than the common flu. SARS-COV2 is less dangerous to healthy people than driving. Not a very effective biological weapon.

Doctors think no disease should be acceptable. And as medical doctors, they may be ignorant of the consequences of political decisions. Does COVID-19 kill people? Yes. The same people who doctors like Dr Fauci should expect to be taken out by the flu and pneumonia, etc, because of other infirmities, every year.

2,046,549 people died in the US last year of all causes except suicide. No special measures. 80,000 died of the flu in 2018, 116,000 in 1957 - when the population was 172 million, which makes it equivalent to 220,000 today, and we did not lockdown. Typically more than three times that die of tuberculosis and Hep A, than the flu every year. Headlines? Nope.

Around 128,000 are now listed here as dying of COVID-19 in the US, almost all of whom could have been listed as dying of their pre-existing conditions. Most would have been listed as something else in any other year. Flu cases are below the baseline this year, according to the CDC. Fishy? Yep.

Are the Chinese making the most of all of this? Of course they are. They are pretty good at strategic planning, but it doesn't mean they orchestrated this, sorry.

Creating a vaccine will be futile if it mutates, and this source asserts it has already happened. Do we want to destroy our nation waiting to find a vaccine that is effective?
[theblaze.com]

So how did this happen? The media in the US was about to go under for the last time as among the least trusted groups in history (below Congress) and desperately needed a sensational story. When Taiwan announced the virus, the media did their best to make it as bad as they possibly could, and the American public - particularly the dumber half of the IQ curve, was ready to play victim.

There is no threat. And China could not have done what our own media did. And the New York Times proudly reports that 87% of the media are Democrat.

Finally, the Constitution justifies draconian measures only if leadership can prove due diligence in establishing that there actually is a threat. As you can see here, that claim will fall to pieces in court, and the lawsuits against the governments are just beginning.

*cases = 5.7**(periods), Periods = days/2.5. Day zero is 17 November
First week, 131
Second week 17,096
Third week 2,235,367
Fourth week 292,279,519
Fifth week, (End of December 2019) 38,216,242,089
(The World is 7,783,000,000)

To #smallbusinessowners & those affected by #lockdown,
It wasn't #COVID19 which closed the #economy and your #business, it was #government and #Parliament by allowing the #CoronaVirus Act to pass. Fact. We see this from countries (busy or not) #Japan and #Sweden with no #lockdowns.
Yet Govt claim to help you in #furlough relief either unaware or aware it was originally them which caused you to be on it.
#smallbusinesscrisisnow #endthelockdown

“The government is good at one thing. It knows how to break your legs, and then hand you a crutch and say, “See, if it weren't for the government, you wouldn't be able to walk.” - Harry Browne.

@TimTuolomne 2,046,549 people died in the US last year of all causes except suicide. No special measures. 80,000 died of the flu in 2018, 116,000 in 1957 - when the population was 172 million, which makes it equivalent to 220,000 today, and we did not lockdown. Typically more than three times that die of tuberculosis and Hep A, than the flu every year. Headlines? Nope.

I don't know where you you got your data from, but it's crap! You should cite your sources once in awhile. Here are mine.

[cdc.gov].

@TyKC. My data is from the CDC also, and there are many ways to define the data set to come up with something different, as apparently have you. I don't engage in discussion with cheap-shot artists. We are done.

@TimTuolomne Well you didn't say where you got your data, so how was I supposed to know? My data (sourced) is from that same year and I didn't see those numbers in there. You need to make it a little easier on all of us when quoting statistics. You'll gain creditability. As far as your argument goes it is grotesquely irresponsible. The situations in 1957 and now are profoundly different. There is no justification for using some bizarre curve flattening technique in this case or any of the other comparisons you are trying to make. There's not enough credible information available for doing that. And attempting to do so virtually guarantees an incorrect conclusion. The logical connections for that are simply not there. I'm sure some people in the health industry can draw some tentative conclusions, but, for the most, part they are dealing with a whole lot of unknowns. All you seem to be doing is promoting some bizarre conspiracy theory with arguments that succumb under the weight of a plie of nonsense and a very little amount of scrutiny, regardless of how accurate or inaccurate the data is, drawing conclusions you have little right to claim. But all is not lost. I'm sure a slew of fans, particularly on this site, are ready willing and able to suck in this kind of blather with not an inkling of critical thought. But I won't be one of them.

As for me, it is probably best that I fade away into the dust. I joined this site because I thought I would be dealing with people who could at least make a sensible argument. Instead, I'm barraged on a daily basis with baseless conspiracy theories, grotesque misuse of basic logic, daily assaults upon common sense, alternative facts and undiscoverable citations or articles that spew indefensible nonsense. I don't expect people to be perfect. I make my share of mistakes, but its an epidemic on this site and others too. Over the years I've gained an appreciation for the importance of good information and the importance of a sound argument, both, I think, are critical to the survival of our democracy, sorry, republic. I stand in astonishment, bewilderment and dismay over the cesspool of misinformation that currently plagues our country and this site. It's a travesty.

0

I was surprised to read that using a respirator was almost as useless as these cloth and paper masks.

Most countries have a National mask wearing requirement. They even have people standing outside public places that hand out masks to those who might need one. They've even installed mask vending machines all over the place. Of course, what do they know? Their number of cases is decreasing exponentially, while ours is increasing exponentially. The Governor of Texas just issued a mandatory wear mask requirement in his State. What an idiot. He obviously didn't read this solid science that supports not wearing a mask.

@TyKC. Look at Sweden.

@TimTuolomne Ok, I did.

[businessinsider.com]

0

Interesting post. Click on the link and it comes up "page cannot be found". And I suspect neither can the science that supports this claim.

Just below that, is a search window. Enter “ masks” and it will bring you right to it.

I reposted it with a link that works, although I don't know why I bother informing you as you believe it is a pack of lies before reading it.

@lawrenceblair So, all the doctors and Nurses who use protective masks to prevent them from getting the disease are all idiots too?

@TyKC I wouldn't call them idiots; I would call them people that refuse to check out the facts and think for themselves. I am not the sort to jump to conclusions without checking the facts, nor am I a person who automatically believes everything I am told by supposed experts with an agenda. There is another type of personality I am not; I am not the sort who virtue signals while following the crowd as is a problem that seems to be afflicting the majority of those in our nation. Now there are those who consider me a fool or an idiot for choosing to think for myself instead of following the crowd off the cliff into the oblivion of slavery but, hey, it's a free country, they are free to have their opinion.

@TyKC, They work with all kinds of pathogens, not just the common cold, which COVID-19 is.

@TyKC, Odd. It came up for me. Here it is. Your credibility with me is nearing zero.

Masks don’t work – a review of science relevant to Covid-19 social policy
vanessa beeley / 2 weeks ago

By Denis Rancourt, PhD

Masks and respirators do not work.

There have been extensive randomized controlled trial (RCT) studies, and meta-analysis reviews of RCT studies, which all show that masks and respirators do not work to prevent respiratory influenza-like illnesses, or respiratory illnesses believed to be transmitted by droplets and aerosol particles.

Furthermore, the relevant known physics and biology, which I review, are such that masks and respirators should not work. It would be a paradox if masks and respirators worked, given what we know about viral respiratory diseases: The main transmission path is long-residence-time aerosol particles (< 2.5 μm), which are too fine to be blocked, and the minimum-infective dose is smaller than one aerosol particle.

The present paper about masks illustrates the degree to which governments, the mainstream media, and institutional propagandists can decide to operate in a science vacuum, or select only incomplete science that serves their interests. Such recklessness is also certainly the case with the current global lockdown of over 1 billion people, an unprecedented experiment in medical and political history.

Review of the Medical Literature
Here are key anchor points to the extensive scientific literature that establishes that wearing surgical masks and respirators (e.g., “N95&rdquo😉 does not reduce the risk of contracting a verified illness:

Jacobs, J. L. et al. (2009) “Use of surgical face masks to reduce the incidence of the common cold among health care workers in Japan: A randomized controlled trial,” American Journal of Infection Control, Volume 37, Issue 5, 417 – 419. [ncbi.nlm.nih.gov]

N95-masked health-care workers (HCW) were significantly more likely to experience headaches. Face mask use in HCW was not demonstrated to provide benefit in terms of cold symptoms or getting colds.

Cowling, B. et al. (2010) “Face masks to prevent transmission of influenza virus: A systematic review,” Epidemiology and Infection, 138(4), 449-456. [cambridge.org] review/64D368496EBDE0AFCC6639CCC9D8BC05

None of the studies reviewed showed a benefit from wearing a mask, in either HCW or community members in households 😎. See summary Tables 1 and 2 therein.

bin-Reza et al. (2012) “The use of masks and respirators to prevent transmission of influenza: a systematic review of the scientific evidence,” Influenza and Other Respiratory Viruses 6(4), 257–267. [onlinelibrary.wiley.com]

“There were 17 eligible studies. … None of the studies established a conclusive relationship between mask/respirator use and protection against influenza infection.”

Smith, J.D. et al. (2016) “Effectiveness of N95 respirators versus surgical masks in protecting health care workers from acute respiratory infection: a systematic review and meta-analysis,” CMAJ Mar 2016 [cmaj.ca]

“We identified six clinical studies … . In the meta-analysis of the clinical studies, we found no significant difference between N95 respirators and surgical masks in associated risk of (a) laboratory-confirmed respiratory infection, 🍺 influenza-like illness, or ☕ reported work-place absenteeism.”

Offeddu, V. et al. (2017) “Effectiveness of Masks and Respirators Against Respiratory Infections in Healthcare Workers: A Systematic Review and Meta-Analysis,” Clinical Infectious Diseases, Volume 65, Issue 11, 1 December 2017, Pages 1934–1942, [academic.oup.com]

“Self-reported assessment of clinical outcomes was prone to bias. Evidence of a protective effect of masks or respirators against verified respiratory infection (VRI) was not statistically significant”; as per Fig. 2c therein:

mask

Radonovich, L.J. et al. (2019) “N95 Respirators vs Medical Masks for Preventing Influenza Among Health Care Personnel: A Randomized Clinical Trial,” JAMA. 2019; 322(9): 824–833. [jamanetwork.com]

“Among 2862 randomized participants, 2371 completed the study and accounted for 5180 HCW-seasons. … Among outpatient health care personnel, N95 respirators vs medical masks as worn by participants in this trial resulted in no significant difference in the incidence of laboratory-confirmed influenza.”

Long, Y. et al. (2020) “Effectiveness of N95 respirators versus surgical masks against influenza: A systematic review and meta-analysis,” J Evid Based Med. 2020; 1- 9. [onlinelibrary.wiley.com]

“A total of six RCTs involving 9,171 participants were included. There were no statistically significant differences in preventing laboratory-confirmed influenza, laboratory-confirmed respiratory viral infections, laboratory-confirmed respiratory infection, and influenza-like illness using N95 respirators and surgical masks. Meta-analysis indicated a protective effect of N95 respirators against laboratory-confirmed bacterial colonization (RR = 0.58, 95% CI 0.43-0.78). The use of N95 respirators compared with surgical masks is not associated with a lower risk of laboratory-confirmed influenza.”

Conclusion Regarding That Masks Do Not Work
No RCT study with verified outcome shows a benefit for HCW or community members in households to wearing a mask or respirator. There is no such study. There are no exceptions.

Likewise, no study exists that shows a benefit from a broad policy to wear masks in public (more on this below).

Furthermore, if there were any benefit to wearing a mask, because of the blocking power against droplets and aerosol particles, then there should be more benefit from wearing a respirator (N95) compared to a surgical mask, yet several large meta-analyses, and all the RCT, prove that there is no such relative benefit.

Masks and respirators do not work.

Precautionary Principle Turned on Its Head with Masks
In light of the medical research, therefore, it is difficult to understand why public-health authorities are not consistently adamant about this established scientific result, since the distributed psychological, economic, and environmental harm from a broad recommendation to wear masks is significant, not to mention the unknown potential harm from concentration and distribution of pathogens on and from used masks. In this case, public authorities would be turning the precautionary principle on its head (see below).

Physics and Biology of Viral Respiratory Disease and of Why Masks Do Not Work
In order to understand why masks cannot possibly work, we must review established knowledge about viral respiratory diseases, the mechanism of seasonal variation of excess deaths from pneumonia and influenza, the aerosol mechanism of infectious disease transmission, the physics and chemistry of aerosols, and the mechanism of the so-called minimum-infective-dose.

In addition to pandemics that can occur anytime, in the temperate latitudes there is an extra burden of respiratory-disease mortality that is seasonal, and that is caused by viruses. For example, see the review of influenza by Paules and Subbarao (2017). This has been known for a long time, and the seasonal pattern is exceedingly regular. (Publisher’s note: All links to source references to studies here forward are found at the end of this article.)

For example, see Figure 1 of Viboud (2010), which has “Weekly time series of the ratio of deaths from pneumonia and influenza to all deaths, based on the 122 cities surveillance in the US (blue line). The red line represents the expected baseline ratio in the absence of influenza activity,” here:

The seasonality of the phenomenon was largely not understood until a decade ago. Until recently, it was debated whether the pattern arose primarily because of seasonal change in virulence of the pathogens, or because of seasonal change in susceptibility of the host (such as from dry air causing tissue irritation, or diminished daylight causing vitamin deficiency or hormonal stress). For example, see Dowell (2001).

In a landmark study, Shaman et al. (2010) showed that the seasonal pattern of extra respiratory-disease mortality can be explained quantitatively on the sole basis of absolute humidity, and its direct controlling impact on transmission of airborne pathogens.

Lowen et al. (2007) demonstrated the phenomenon of humidity-dependent airborne-virus virulence in actual disease transmission between guinea pigs, and discussed potential underlying mechanisms for the measured controlling effect of humidity.

The underlying mechanism is that the pathogen-laden aerosol particles or droplets are neutralized within a half-life that monotonically and significantly decreases with increasing ambient humidity. This is based on the seminal work of Harper (1961). Harper experimentally showed that viral-pathogen-carrying droplets were inactivated within shorter and shorter times, as ambient humidity was increased.

Harper argued that the viruses themselves were made inoperative by the humidity (“viable decay&rdquo😉, however, he admitted that the effect could be from humidity-enhanced physical removal or sedimentation of the droplets (“physical loss&rdquo😉: “Aerosol viabilities reported in this paper are based on the ratio of virus titre to radioactive count in suspension and cloud samples, and can be criticized on the ground that test and tracer materials were not physically identical.”

The latter (“physical loss&rdquo😉 seems more plausible to me, since humidity would have a universal physical effect of causing particle/droplet growth and sedimentation, and all tested viral pathogens have essentially the same humidity-driven “decay.” Furthermore, it is difficult to understand how a virion (of all virus types) in a droplet would be molecularly or structurally attacked or damaged by an increase in ambient humidity. A “virion” is the complete, infective form of a virus outside a host cell, with a core of RNA or DNA and a capsid. The actual mechanism of such humidity-driven intra-droplet “viable decay” of a virion has not been explained or studied.

In any case, the explanation and model of Shaman et al. (2010) is not dependent on the particular mechanism of the humidity-driven decay of virions in aerosol/droplets. Shaman’s quantitatively demonstrated model of seasonal regional viral epidemiology is valid for either mechanism (or combination of mechanisms), whether “viable decay” or “physical loss.”

The breakthrough achieved by Shaman et al. is not merely some academic point. Rather, it has profound health-policy implications, which have been entirely ignored or overlooked in the current coronavirus pandemic.

In particular, Shaman’s work necessarily implies that, rather than being a fixed number (dependent solely on the spatial-temporal structure of social interactions in a completely susceptible population, and on the viral strain), the epidemic’s basic reproduction number (R0) is highly or predominantly dependent on ambient absolute humidity.

For a definition of R0, see HealthKnowlege-UK (2020): R0 is “the average number of secondary infections produced by a typical case of an infection in a population where everyone is susceptible.” The average R0 for influenza is said to be 1.28 (1.19–1.37); see the comprehensive review by Biggerstaff et al. (2014).

In fact, Shaman et al. showed that R0 must be understood to seasonally vary between humid-summer values of just larger than “1” and dry-winter values typically as large as “4” (for example, see their Table 2). In other words, the seasonal infectious viral respiratory diseases that plague temperate latitudes every year go from being intrinsically mildly contagious to virulently contagious, due simply to the bio-physical mode of transmission controlled by atmospheric humidity, irrespective of any other consideration.

Therefore, all the epidemiological mathematical modeling of the benefits of mediating policies (such as social distancing), which assumes humidity-independent R0 values, has a large likelihood of being of little value, on this basis alone. For studies about modeling and regarding mediation effects on the effective reproduction number, see Coburn (2009) and Tracht (2010).

To put it simply, the “second wave” of an epidemic is not a consequence of human sin regarding mask wearing and hand shaking. Rather, the “second wave” is an inescapable consequence of an air-dryness-driven many-fold increase in disease contagiousness, in a population that has not yet attained immunity.

If my view of the mechanism is correct (i.e., “physical loss&rdquo😉, then Shaman’s work further necessarily implies that the dryness-driven high transmissibility (large R0) arises from small aerosol particles fluidly suspended in the air; as opposed to large droplets that are quickly gravitationally removed from the air.

Such small aerosol particles fluidly suspended in air, of biological origin, are of every variety and are everywhere, including down to virion-sizes (Despres, 2012). It is not entirely unlikely that viruses can thereby be physically transported over inter-continental distances (e.g., Hammond, 1989).

More to the point, indoor airborne virus concentrations have been shown to exist (in day-care facilities, health centers, and on-board airplanes) primarily as aerosol particles of diameters smaller than 2.5 μm, such as in the work of Yang et al. (2011):

“Half of the 16 samples were positive, and their total virus −3 concentrations ranged from 5800 to 37 000 genome copies m . On average, 64 per cent of the viral genome copies were associated with fine particles smaller than 2.5 μm, which can remain suspended for hours. Modeling of virus concentrations indoors suggested a source strength of 1.6 ± 1.2 × 105 genome copies m−3 air h−1 and a deposition flux onto surfaces of 13 ± 7 genome copies m−2 h−1 by Brownian motion. Over one hour, the inhalation dose was estimated to be 30 ± 18 median tissue culture infectious dose (TCID50), adequate to induce infection. These results provide quantitative support for the idea that the aerosol route could be an important mode of influenza transmission.”

Such small particles (< 2.5 μm) are part of air fluidity, are not subject to gravitational sedimentation, and would not be stopped by long-range inertial impact. This means that the slightest (even momentary) facial misfit of a mask or respirator renders the design filtration norm of the mask or respirator entirely irrelevant. In any case, the filtration material itself of N95 (average pore size ~0.3−0.5 μm) does not block virion penetration, not to mention surgical masks. For example, see Balazy et al. (2006).

Mask stoppage efficiency and host inhalation are only half of the equation, however, because the minimal infective dose (MID) must also be considered. For example, if a large number of pathogen-laden particles must be delivered to the lung within a certain time for the illness to take hold, then partial blocking by any mask or cloth can be enough to make a significant difference.

On the other hand, if the MID is amply surpassed by the virions carried in a single aerosol particle able to evade mask-capture, then the mask is of no practical utility, which is the case.

Yezli and Otter (2011), in their review of the MID, point out relevant features:

Most respiratory viruses are as infective in humans as in tissue culture having optimal laboratory susceptibility
It is believed that a single virion can be enough to induce illness in the host
The 50-percent probability MID (“TCID50&rdquo has variably been found to be in the range 100−1000 virions
There are typically 10 to 3rd power − 10 to 7th power virions per aerolized influenza droplet with diameter 1 μm − 10 μm
The 50-percent probability MID easily fits into a single (one) aerolized droplet
For further background:
A classic description of dose-response assessment is provided by Haas (1993).
Zwart et al. (2009) provided the first laboratory proof, in a virus-insect system, that the action of a single virion can be sufficient to cause disease.
Baccam et al. (2006) calculated from empirical data that, with influenza A in humans,“we estimate that after a delay of ~6 h, infected cells begin producing influenza virus and continue to do so for ~5 h. The average lifetime of infected cells is ~11 h, and the half-life of free infectious virus is ~3 h. We calculated the [in-body] basic reproductive number, R0, which indicated that a single infected cell could produce ~22 new productive infections.”
Brooke et al. (2013) showed that, contrary to prior modeling assumptions, although not all influenza-A-infected cells in the human body produce infectious progeny (virions), nonetheless, 90 percent of infected cell are significantly impacted, rather than simply surviving unharmed.

All of this to say that: if anything gets through (and it always does, irrespective of the mask), then you are going to be infected. Masks cannot possibly work. It is not surprising, therefore, that no bias-free study has ever found a benefit from wearing a mask or respirator in this application.

Therefore, the studies that show partial stopping power of masks, or that show that masks can capture many large droplets produced by a sneezing or coughing mask-wearer, in light of the above-described features of the problem, are irrelevant. For example, such studies as these: Leung (2020), Davies (2013), Lai (2012), and Sande (2008).

Why There Can Never Be an Empirical Test of a Nation-Wide Mask-Wearing Policy
As mentioned above, no study exists that shows a benefit from a broad policy to wear masks in public. There is good reason for this. It would be impossible to obtain unambiguous and bias-free results [because]:

Any benefit from mask-wearing would have to be a small effect, since undetected in controlled experiments, which would be swamped by the larger effects, notably the large effect from changing atmospheric humidity.
Mask compliance and mask adjustment habits would be unknown.
Mask-wearing is associated (correlated) with several other health behaviors; see Wada (2012).
The results would not be transferable, because of differing cultural habits.
Compliance is achieved by fear, and individuals can habituate to fear-based propaganda, and can have disparate basic responses.
Monitoring and compliance measurement are near-impossible, and subject to large errors.
Self-reporting (such as in surveys) is notoriously biased, because individuals have the self-interested belief that their efforts are useful.
Progression of the epidemic is not verified with reliable tests on large population samples, and generally relies on non-representative hospital visits or admissions.
Several different pathogens (viruses and strains of viruses) causing respiratory illness generally act together, in the same population and/or in individuals, and are not resolved, while having different epidemiological characteristics.

Unknown Aspects of Mask Wearing
Many potential harms may arise from broad public policies to wear masks, and the following unanswered questions arise:

Do used and loaded masks become sources of enhanced transmission, for the wearer and others?
Do masks become collectors and retainers of pathogens that the mask wearer would otherwise avoid when breathing without a mask?
Are large droplets captured by a mask atomized or aerolized into breathable components? Can virions escape an evaporating droplet stuck to a mask fiber?
What are the dangers of bacterial growth on a used and loaded mask?
How do pathogen-laden droplets interact with environmental dust and aerosols captured on the mask?
What are long-term health effects on HCW, such as headaches, arising from impeded breathing?
Are there negative social consequences to a masked society?
Are there negative psychological consequences to wearing a mask, as a fear-based behavioral modification?
What are the environmental consequences of mask manufacturing and disposal?
Do the masks shed fibers or substances that are harmful when inhaled?

Conclusion
By making mask-wearing recommendations and policies for the general public, or by expressly condoning the practice, governments have both ignored the scientific evidence and done the opposite of following the precautionary principle.

In an absence of knowledge, governments should not make policies that have a hypothetical potential to cause harm. The government has an onus barrier before it instigates a broad social-engineering intervention, or allows corporations to exploit fear-based sentiments.

Furthermore, individuals should know that there is no known benefit arising from wearing a mask in a viral respiratory illness epidemic, and that scientific studies have shown that any benefit must be residually small, compared to other and determinative factors.

Otherwise, what is the point of publicly funded science?

The present paper about masks illustrates the degree to which governments, the mainstream media, and institutional propagandists can decide to operate in a science vacuum, or select only incomplete science that serves their interests. Such recklessness is also certainly the case with the current global lockdown of over 1 billion people, an unprecedented experiment in medical and political history.

Denis G. Rancourt is a researcher at the Ontario Civil Liberties Association (OCLA.ca) and is formerly a tenured professor at the University of Ottawa, Canada. This paper was originally published at Rancourt’s account on ResearchGate.net. As of June 5, 2020, this paper was removed from his profile by its administrators at Researchgate.net/profile/D_Rancourt. At Rancourt’s blog ActivistTeacher.blogspot.com, he recounts the notification and responses he received from ResearchGate.net and states, “This is censorship of my scientific work like I have never experienced before.”

The original April 2020 white paper in .pdf format is available here, complete with charts that have not been reprinted in the Reader print or web versions.

Endnotes:
Baccam, P. et al. (2006) “Kinetics of Influenza A Virus Infection in Humans”, Journal of Virology Jul 2006, 80 (15) 7590-7599; DOI: 10.1128/JVI.01623-05 [jvi.asm.org]

Balazy et al. (2006) “Do N95 respirators provide 95% protection level against airborne viruses, and how adequate are surgical masks?”, American Journal of Infection Control, Volume 34, Issue 2, March 2006, Pages 51-57. doi:10.1016/j.ajic.2005.08.018 [citeseerx.ist.psu.edu]

Biggerstaff, M. et al. (2014) “Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature”, BMC Infect Dis 14, 480 (2014). [doi.org]

Brooke, C. B. et al. (2013) “Most Influenza A Virions Fail To Express at Least One Essential Viral Protein”, Journal of Virology Feb 2013, 87 (6) 3155-3162; DOI: 10.1128/JVI.02284-12 [jvi.asm.org]

Coburn, B. J. et al. (2009) “Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1)”, BMC Med 7, 30. [doi.org]

Davies, A. et al. (2013) “Testing the Efficacy of Homemade Masks: Would They Protect in an Influenza Pandemic?”, Disaster Medicine and Public Health Preparedness, Available on CJO 2013 doi:10.1017/dmp.2013.43 [journals.cambridge.org]

Despres, V. R. et al. (2012) “Primary biological aerosol particles in the atmosphere: a review”, Tellus B: Chemical and Physical Meteorology, 64:1, 15598, DOI: 10.3402/tellusb.v64i0.15598 [doi.org]

Dowell, S. F. (2001) “Seasonal variation in host susceptibility and cycles of certain infectious diseases”, Emerg Infect Dis. 2001;7(3):369–374. doi:10.3201/eid0703.010301 [ncbi.nlm.nih.gov]

Hammond, G. W. et al. (1989) “Impact of Atmospheric Dispersion and Transport of Viral Aerosols on the Epidemiology of Influenza”, Reviews of Infectious Diseases, Volume 11, Issue 3, May 1989, Pages 494–497, [doi.org]

Haas, C.N. et al. (1993) “Risk Assessment of Virus in Drinking Water”, Risk Analysis, 13: 545-552. doi:10.1111/j.1539-6924.1993.tb00013.x [doi.org]

HealthKnowlege-UK (2020) “Charter 1a – Epidemiology: Epidemic theory (effective & basic reproduction numbers, epidemic thresholds) & techniques for analysis of infectious disease data (construction & use of epidemic curves, generation numbers, exceptional reporting & identification of significant clusters)”, HealthKnowledge.org.uk, accessed on 2020-04-10. [healthknowledge.org.uk] epidemiology/epidemic-theory

Lai, A. C. K. et al. (2012) “Effectiveness of facemasks to reduce exposure hazards for airborne infections among general populations”, J. R. Soc. Interface. 9938–948 [doi.org]

Leung, N.H.L. et al. (2020) “Respiratory virus shedding in exhaled breath and efficacy of face masks”, Nature Medicine (2020). [doi.org]

Lowen, A. C. et al. (2007) “Influenza Virus Transmission Is Dependent on Relative Humidity and Temperature”, PLoS Pathog 3(10): e151. [doi.org]

Paules, C. and Subbarao, S. (2017) “Influenza”, Lancet, Seminar| Volume 390, ISSUE 10095, P697-708, August 12, 2017. [dx.doi.org]

Sande, van der, M. et al. (2008) “Professional and Home-Made Face Masks Reduce Exposure to Respiratory Infections among the General Population”, PLoS ONE 3(7): e2618. doi:10.1371/journal.pone.0002618 [doi.org]

Shaman, J. et al. (2010) “Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States”, PLoS Biol 8(2): e1000316. [doi.org]

Tracht, S. M. et al. (2010) “Mathematical Modeling of the Effectiveness of Facemasks in Reducing the Spread of Novel Influenza A (H1N1)”, PLoS ONE 5(2): e9018. doi:10.1371/journal.pone.0009018 [doi.org]

Viboud C. et al. (2010) “Preliminary Estimates of Mortality and Years of Life Lost Associated with the 2009 A/H1N1 Pandemic in the US and Comparison with Past Influenza Seasons”, PLoS Curr. 2010; 2:RRN1153. Published 2010 Mar 20. doi:10.1371/currents.rrn1153 [ncbi.nlm.nih.gov]

Wada, K. et al. (2012) “Wearing face masks in public during the influenza season may reflect other positive hygiene practices in Japan”, BMC Public Health 12, 1065 (2012). [doi.org]

Yang, W. et al. (2011) “Concentrations and size distributions of airborne influenza A viruses measured indoors at a health centre, a day-care centre and on aeroplanes”, Journal of the Royal Society, Interface. 2011 Aug;8(61):1176-1184. DOI: 10.1098/rsif.2010.0686. [royalsocietypublishing.org]

Yezli, S., Otter, J.A. (2011) “Minimum Infective Dose of the Major Human Respiratory and Enteric Viruses Transmitted Through Food and the Environment”, Food Environ Virol 3, 1–30. [doi.org]

Zwart, M. P. et al. (2009) “An experimental test of the independent action hypothesis in virus– insect pathosystems”, Proc. R. Soc. B. 2762233–2242 [doi.org]

COVID-19: “Excess deaths” or State-sponsored “homicide”?

Throughout the course of the COVID Operation, we have seen that COVID “attribution” data has been unreliable and manipulated. World Health Organization (WHO) and Centers for Disease Control (CDC) changed Cause of Death guidelines. Financial incentives are part of the manipulated equation, unvalidated tests are used, “presumptive COVID “cases” count as “COVID cases”, distinctions between death WITH COVID-19 and deaths BY/FROM COVID-19 are not being made. Importantly, comorbidities such as age, cardiovascular health etc. are largely negated as the spotlight burns on COVID-19. All of these biases corrupt the data.

@TimTuolomne There are always outliers in medical studies. Surely you don't expect me to view one medical article and think it's the end all be all. This source seems of dubious merit. Who is Denis Rancourt, PhD? One author? Very unusual to have only one author on a journal ready medical study whether it's in a journal or not. Why couldn't he get others to join him who held the same view? Makes it a lot more credible. I've attached a study that says just the opposite. And it has numerous authors. If there is one thing I've learned, anybody can put a PhD behind their name, cite all kinds of sources that promote their agenda and ignore the rest. It's not that hard to do. If there is more than one author, it's a little more creditable. There seems to be a lot more studies that say masks and the like work than say they don't. I'll take the scientific route thank you very much. BTW, I take my zero creditability with you as a compliment.

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