Facebook has flagged a meme claiming that the fatality rate for COVID-19 in the US is 0.017 percent as “False” and directs users of the social media platform to a “Fact Check” article from Lead Stories published on May 21 that describes the meme as a “hoax”. The author of the article, Gita Smith, asserts that the true mortality rate is much higher, at an estimated 1.3 percent.
The purpose of the meme was to suggest that the extreme lockdown measures that have been implemented by governments around the world, including most US states, are the result of “panic”, and that people might perceive the risk differently if represented in terms of the survival rate rather than the death rate. The meme presented numbers for a variety of countries, starting with a claimed survival rate for the US of “99.983%”.
The purpose of the Lead Stories article, by contrast, is to advocate the lockdown measures as being necessary to prevent an enormous number of deaths due to SARS-CoV-2 supposedly having a death rate more than ten times that of the flu.
So, the question at the heart of this controversy is whether the extreme measures are warranted or instead represent a mass panic and political overreaction that could easily be doing more harm than good.
The “Fact Check” article is correct to say that the fatality rate presented in the meme is not supported by the currently available data. However, far from presenting the public with the truth, Lead Stories itself grossly deceives its readers with numerous assertions that range from misleading to outright false. On a few counts, Lead Stories demonstrably lies, fabricating claims by misrepresenting its own cited sources.
A critical examination of the claims made by Lead Stories reveals that, if we objectively apply its own standard, the “Fact Check” article is certainly no less of a hoax than the meme. It is nothing more than political propaganda intended to serve the function of manufacturing consent for extreme authoritarian government policies that could easily be doing far more harm than good.
- The Crux of the Deception
- Falsely Characterizing Case and Death Numbers as “Confirmed”
- Confusing the Name of the Virus with the Name of the Disease
- Conflating Different Types of Mortality Estimates
- Lying about the Potential Number of COVID-19 Deaths
- How Lead Stories Mischaracterizes Its Own Primary Source
- How Lead Stories’ Primary Source Gets It Wrong, Too
- Is SARS-CoV-2 Ten Times Deadlier than Influenza?
- Are Extreme Lockdown Measures Doing More Harm Than Good?
In the lead paragraph of the Lead Stories article, Smith argues that if the meme’s fatality rate of 0.017 percent were true, given 1,610,000 “confirmed” COVID-19 cases in the US as of May 21, “the death toll would be about 27,370”; but instead there are “at least 95,000 confirmed deaths from COVID-19”. The implication is that therefore the claimed rate must be wrong.
Actually, a fatality rate of 0.017 percent, given 1,610,000 cases, would mean just 274 deaths. Smith neglected to move the decimal place in her calculation (0.017 divided by 100 to convert the percent to a decimal). The proper calculation, of course, makes the meme’s claimed rate appear even more hugely understated, given the logic of her argument, so this was obviously an honest mistake that worked in the meme’s favor.
Curiously, though, Smith did not do the math to directly compare the meme’s claimed fatality rate with the rate we get by dividing 95,000 into 1,610,000, which is an alarming 5.9 percent.
This, too, would obviously have made the meme’s rate seem even more outrageously understated. Instead, though, she cites an estimate from a University of Washington study that puts what she describes as the “mortality rate” at 1.3 percent.
She offers no explanation for why that number differs so greatly from the much higher one that her own reasoning demands we use for comparison.
There is an explanation, which we’ll come to, but first there are some other immediate problems with Smith’s argument that require our attention.
Smith does not cite a source for the case and death numbers she presents, but they are close to those provided by the website Worldometers, a widely cited source for this data. The numbers for May 21 were updated throughout the day. At 3:51 pm GMT, it was showing 1,596,838 cases and 95,058 deaths; and by 7:41 pm GMT, it was showing 1,614,578 cases and 96,055 deaths.
These are pretty close to the numbers Lead Stories presents. Smith may also have been looking at another source. But other sources reporting similar case and death totals, such as Johns Hopkins University and the New York Times, draw their numbers from the same underlying data reported by public health authorities.
Given this, we can reasonably conclude that by claiming the totals represent “confirmed” cases and deaths, Lead Stories is lying to its readers.
Here’s why: As Worldometers explains (with its bold emphasis), the case numbers are “reported total cumulative count of detected and laboratory (and sometimes, depending on the country reporting them and the criteria adopted at the time, also clinically) confirmed positive and sometimes—depending on the country reporting standards—also presumptive, suspect, or probable cases of detected infection.”
Crucially, Worldometers provides additional information about its methodology specifically regarding the US numbers: “As of April 14, 2020, CDC case counts and death counts include both confirmed and probable cases and deaths.” (Emphasis in original.)
The page goes on to say, “This change is a further example of one of the many reasons why the label ‘confirmed cases’ . . . is incorrect . . . .” (Emphasis in original.)
As of June 1, the CDC is reporting a total of 84,735 “COVID-19 Deaths” through the week ending May 30. It notes that “COVID-19 Deaths” means “Deaths with confirmed or presumed COVID-19, coded to ICD-10 code U07.1”. (Emphasis added.)
The Johns Hopkins webpage presenting the mapped data states, “Confirmed cases include presumptive positive cases and probable cases, in accordance with CDC guidelines as of April 14.” (The bold emphasis is theirs; the italic emphasis is mine. A presumptive positive case is one in which the individual has tested positive but the result is awaiting confirmation, whereas “probable” cases are those for which no lab-confirmation is obtained.)
That is, nonsensically, Johns Hopkins’ working definition of “confirmed” cases includes unconfirmed cases.
Additionally, Johns Hopkins’ data sources include the CDC and Worldometers, whose numbers, as already discussed, likewise include presumed along with confirmed cases.
The New York Times webpage presenting its mapped data provides more reasonable definitions distinguishing between “confirmed” and “probable” cases and deaths. But the Times explains that the numbers include “the total of confirmed and probable counts when they are available individually or combined. Otherwise only the confirmed count will be shown.” (Emphasis added.)
In sum, contrary to the false claim made by Lead Stories, the cumulative total numbers being reported by multiple different sources do not represent “confirmed” cases and deaths. Rather, the numbers also include individuals who are presumed to have been infected with SARS-CoV-2. Furthermore, just because someone dies after having been tested positive for the virus does not necessarily mean that the virus is what caused their death.
To be clear, these caveats do not necessarily mean that the reported numbers are inherently overestimated. It is also likely that some deaths go unreported.
However, in terms of the mortality rate, any underestimation in the number of deaths will be of lesser magnitude than the underestimation in the number of infections.
Hence, the fatality rate resulting from dividing the number of reported deaths into the number of reported cases—5.9 percent in this case—is inherently overestimated.
This is actually explained quite well by Lead Stories’ own primary source.
So, assuming that Smith read the University of Washington study, she must have known that the logic underlying her argument was fallacious, which would explain why she chose not to do the comparison that her own line of reasoning demanded that she make.
The Lead Stories deceptions, however, do not end there. We’re just getting started.
Another problem with the Lead Stories “Fact Check” is that it confuses the name of the virus with the name of the disease that the virus causes. This might appear a trivial criticism at first, but as you will see, conflating the two enables Lead Stories to perpetrate its key deception.
The article never refers to the name of the virus, which is “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2). Instead, the article refers only to the abbreviation for the name of the disease caused by the novel coronavirus that emerged in late 2019, which is “coronavirus disease” (COVID-19).
Therein lies the significance of the distinction for our purposes: not everyone who is infected with the virus gets the disease.
Frequently in the public discourse, the term “COVID-19” is used incorrectly to refer to the virus, even by public health officials who should know better.
For example, a quick search for the phrase “infection with COVID-19” on Google turns up a CDC frequently asked questions (FAQ) webpage for clinicians that mostly distinguishes properly between the name of the virus and the name of the disease. For example, one question asks, “Who is at risk for infection with the virus that causes COVID-19?” But in the answer to another question about patients with asthma, the CDC states, “Clinicians may be concerned that an asthma exacerbation is related to an underlying infection with COVID-19.” The CDC means “infection with SARS-CoV-2.”
The World Health Organization (WHO), which literally defined “COVID-19”, also uses the terms contrary to their official definitions, such as in this statement regarding the use of masks: “However, there is currently no evidence that wearing a mask (whether medical or other types) by healthy persons in the wider community setting, including universal community masking, can prevent them from infection with respiratory viruses, including COVID-19.”
The meme dubbed a “hoax” by Lead Stories similarly refers to the “survival rate of COVID-19”, but it is evident that the creator intended to convey a survival rate of SARS-CoV-2 infection as opposed to limiting the denominator to just those who developed the disease. Smith tacitly acknowledges this by referring to how the meme subtracted a claimed fatality rate from 100 percent, with the latter representing the total number of people “believed to have caught the virus.”
Smith also confuses the terms in her Lead Stories article, stating that we must rely on estimates because we don’t know “the total number of people infected with COVID-19”.
The consequence of this misuse of names is that, when Smith refers to “COVID-19 cases”, readers could reasonably take her to mean cases of infection with SARS-CoV-2. And so when she presents “1.3 percent” as the “mortality rate of COVID-19 cases”, readers would have to be forgiven for interpreting this to mean that for every 77 people who become infected with the virus, one will die.
The rest of the article serves to further reinforce that false conclusion in the mind of readers.
Having reported a “mortality rate” of 1.3 percent to contrast with the meme’s claimed rate, Smith goes on to quote a Dr. Theo Vos as saying, “You really need to look at this by age, because there is an enormous age gradient for risk of death per infected case.” (Emphasis added.)
Subsequently, she paraphrases a Dr. Brian P. Monahan saying that, if everyone in the country became infected, given a population of 329 million, “a 1% mortality rate at that scale of infection would be between 700,000 and 1.5 million dead.” (Emphasis added.)
Both of these statements in their given context reinforce to the reader that when Lead Stories talks about the “mortality rate of COVID-19 cases”, it means the number of deaths from COVID-19 divided by the total number of people infected with SARS-CoV-2.
The problem with leading readers to that conclusion is that neither the 5.9 percent we get from the raw numbers nor the 1.3 percent estimate from the University of Washington study represent what is known as the “infection fatality rate” (IFR), which is an estimate of the true mortality rate of an infectious pathogen.
By contrast, the simple division of reported deaths into reported cases gives us what’s known as the “case fatality rate” (CFR). This term by itself can be confusing because it implies a meaning of “deaths divided by cases”. However, the CFR typically includes only reported cases in the denominator. Since an unknown proportion of cases of infection with SARS-CoV-2 go unreported, the CFR is inherently overestimated.
That’s why it’s so disingenuous for Lead Stories to suggest that simply looking at the reported numbers of cases and deaths demonstrates that the fatality rate claimed by the meme is wrong. Smith herself points out that we don’t know the true number of infections, and so the conclusion seems inescapable that she must have understood that her argument is fallacious.
As already noted, that the CFR inherently overestimates the mortality rate, even if deaths are undercounted, is also explained by the author of the University of Washington study, Anirban Basu, a professor in the university’s Department of Pharmacy.
As Basu explains, while there can also be underreporting of deaths in the numerator, which biases the CFR downward, the underreporting error “is smaller for deaths than for cases” since deaths “are much more visible events than infections”. Therefore, it is “self-evident” that “the errors in the denominator are larger than the errors in the numerator.” This is why the CFR is higher than the IFR, the latter of which includes not only reported cases in the denominator but aims to estimate the total number of estimated infections. Hence, the IFR is a more accurate measure of the mortality rate.
This brings us to the “1.3 percent” Lead Stories presents in lieu of the CFR. The impression left upon readers is that this represents an estimated IFR, a “scientific estimate” of the “mortality rate”. However, the deception here only grows because the “1.3 percent” is also not an estimate of the rate of deaths per infections.
We’ll come to that. But, first, there’s yet another deception that needs to be addressed.
Lead Stories argues that the idea that “Americans are irrationally panicking has no basis”; the meme is wrong to suggest that “a lockdown with stay-at-home rules” is “panic . . . out of control”; extreme measures are instead warranted due to the “mortality rate” of 1.3 percent.
“A 99% survival rate might sound promising”, Smith writes. “But when it’s scaled out to the rest of the country—all 329 million residents—a 1% death rate takes on a different meaning . . . .” It’s here that she paraphrases Dr. Monahan, who, she claims, “predicted early in the pandemic that a 1% mortality rate at that scale of infection would be between 700,000 and 1.5 million dead. He told CNBC TV on March 11, 2020, that death toll would roughly equal the population of Washington, D.C., on the low end, or the entire population of Hawaii on the high end.”
Conveniently, Lead Stories provides us with the link to the source, a CNBC article dated March 11. However, the CNBC article does not say what Smith claims it says, and Dr. Monahan did not say what she says he did.
First, Monahan, who is “the attending physician of Congress and the U.S. Supreme Court”, did not make his comments to CNBC. Rather, he “made the comments to Senate staff”.
Second, though, what Monahan said, as paraphrased by CNBC, was that “he expects 70 million to 150 million people in the United States will become infected with COVID-19”. (There again, incidentally, you can see how the media constantly confuse the name of the virus with the name of the disease.)
CNBC does not report him as saying that 700,000 and 1.5 million people would die. Nor, therefore, is there anything in the article about him equating those numbers to the populations of Washington, D.C., and the state of Hawaii, respectively. Evidently, this was Smith’s own contextualization.
The CNBC article does point out that “many experts and U.S. health officials” were saying that “the mortality rate is likely somewhere around 1%, making it at least 10 times as lethal as the flu.” But it does not say that Dr. Monahan was one of them. What Smith has done is to draw her own conclusion and misattributed her own thoughts to Dr. Monahan. Perhaps he did believe that the mortality rate was 1 percent and that his projected number of infections would mean as many deaths as Lead Stories claims he projected. But the source cited does not show that he projected up to 1.5 million deaths.
Furthermore, even if Dr. Monahan had said what Lead Stories claims he said, it would still be doing its readers a disservice by unquestioningly accepting the 1 percent as an accurate estimate of the mortality rate. But it is not.
This is because the “1 percent” figure propagated by health officials—including Dr. Anthony Fauci, Director of the National Institute for Allergy and Infectious Disease (NIAID) at the National Institutes of Health (NIH)—was derived from the reported numbers of deaths and cases and is therefore also inherently overestimated.
Fauci told members of Congress matter-of-factly on March 11, “The flu has a mortality of 0.1 percent. This has a mortality of ten times that.”
The problem with this statement is that he could not possibly have known that.
At the time, the only type of testing that was being done to detect cases was with reverse transcription polymerase chain reaction (RT-PCR) assays, which detect the presence of the virus’s RNA in the body. These tests indicate whether a person is currently infected. They do not show whether someone was infected in the past and has recovered. Consequently, they cannot by themselves be used to determine the infection rate in a population.
Another problem with citing reported cases from RT-PCR assays to estimate the mortality rate is that the numbers suffer from what’s known as an ascertainment bias. The testing that was being done was far from randomized. On the contrary, particularly given the shortage of test kits, it was typically only those with more serious symptoms who were being tested. Naturally, when the sample population is disproportionately those with more severe disease, there is going to be a higher rate of death than would be true for the general population.
And, naturally, given its purpose in advocating authoritarian government policies, Lead Stories does not explain that to its readers, but is instead content to lead them to conclude that SARS-CoV-2 is ten times deadlier than the flu and to leave it at that.
This brings us back to the “scientific estimate” putting the “mortality rate” at “1.3 percent”, which is more than ten times greater than the 0.1 percent we’re told is the mortality rate from the seasonal flu.
Contrary to Smith’s characterization, what the study estimated was not the infection fatality rate.
Most people who experience COVID-19 develop only mild to moderate symptoms, which are oftentimes indistinguishable from the symptoms of a cold or the flu. Severe disease is relatively rare, especially among people under the age of 65 who are healthy. The greatest proportion of deaths have occurred among elderly people with underlying medical conditions that have been identified as risk factors for severe COVID-19, including cardiovascular disease, diabetes, and obesity.
Smith’s primary source, the University of Washington study, highlights the fact that a significant proportion of individuals who become infected with SARS-CoV-2 never develop any symptoms, which is to say that they never get COVID-19.
Belying Lead Stories’ characterization of the “1.3 percent” figure, the headline of its hyperlinked source, a University of Washington press release from May 18, includes an important caveat lost upon Smith’s readers: “COVID-19: UW study reports ‘staggering’ death toll in US among those infected who show symptoms”. (Emphasis added.)
The press release summarizes Basu’s findings by saying, “The national rate of death among people infected with the novel coronavirus — SARS-CoV-2 — that causes COVID-19 and who show symptoms is 1.3%, the study found. The comparable rate of death for the seasonal flu is 0.1%.” (Emphasis added.)
That is, this figure was not arrived at by estimating the total number of infections and using that number in the denominator. It was rather arrived at by excluding the number of asymptomatic infections. Thus, it does not represent the SARS-CoV-2 mortality rate as falsely claimed by Lead Stories.
But that’s not all. As it turns out, the press release is misleading, too, because it is not only asymptomatic cases that Basu excluded from his denominator.
It’s bad enough that Lead Stories so grossly misinforms its readers, including by falsely representing the information from own sources. But compounding the deception even further is its failure to critically analyze its primary source for the “mortality rate”. Had Lead Stories done real journalism as opposed to policy advocacy, it could have done the public a service by identifying a rather obvious and fatal flaw in the study.
In the study, which was published in Health Affairs on May 7, 2020, Basu acknowledges that “we do not know the actual number of individuals who are infected.” His purpose was to estimate “the IFR among symptomatic COVID-19 cases (IFR-S)”, which he points out is an inherent overestimate of the true IFR since it does not include asymptomatic cases in the denominator.
This raises the question: Why did Lead Stories characterize Basu’s estimate as the “mortality rate” of SARS-CoV-2 as though it was calculated including total estimated infections in the denominator, instead of informing readers that, in Basu’s own words, the “1.3 percent” estimate is “higher than the true overall IFR”?
This goes to the question of intent, and it is evident once again that Lead Stories’ purpose was not to educate the public, but to manipulate public opinion by contributing to the sense of fear and panic necessary to manufacture consent for authoritarian government policies.
To help quantify the proportion of asymptomatic cases, Basu discusses data from the cruise ship Diamond Princess, aboard which an outbreak occurred in February after departure from Hong Kong, resulting in the ship’s crew and passengers being placed under quarantine upon arrival in Japan. Noting that this data indicated an asymptomatic infection rate of 17.9 percent, Basu remarks, “Consequently, a reasonable estimate of the overall IFR would be about 20% lower than our estimated IFR-S.”
That would place the IFR at 1.04 percent.
However, we can deduce that this number, too, is inherently overestimated due to fallacies in Basu’s reasoning.
For one, Basu fails to relay important caveats about the Diamond Princess study from which he obtained the estimate, which was published in Eurosurvillance on March 12. Most of those aboard the ship, 83 percent, were tested for SARS-CoV-2. However, testing prioritized symptomatic and high-risk individuals. Therefore, those not tested included younger, healthy individuals who had no symptoms but who may nevertheless have been infected.
Additionally, the passengers and crew aboard the ship “do not constitute a random sample from the general population.” In fact, “most of the passengers were 60 years and older”, and elderly people are more likely to experience more symptoms.
In other words, there was ascertainment bias.
Both of those factors would bias their estimate downward, which is to say that “17.9 percent” should be considered a lower bound for the rate of asymptomatic cases of SARS-CoV-2 infection. Consequently, the implied IFR would also be lower than that suggested by Basu. (Note that he allows for it to be slightly conservative by rounding it to 20 percent, but this was an arbitrary allowance.)
Furthermore, Basu was relying on data for confirmed cases only, meaning that his denominator excluded all non-confirmed cases. To conduct his analysis, he examined the “40,835 confirmed cases and 1,620 confirmed deaths” in the US as of April 20, 2020.
Note that if you simply divide the 1,620 confirmed COVID-19 deaths into the 40,835 confirmed cases, the resulting CFR was 3.97 percent as of April 20.
The reason that his estimated IFR-S of 1.3 percent is lower than that is due to the assumption that estimates of CFR decrease over time. This phenomenon is due to a reduction over time in the errors in the numerator and the denominator, with the errors in the denominator, again, remaining “larger than the errors in the numerator” at any point in time. As testing capacity increases and researchers start to overcome ascertainment bias by conducting more widespread (and, ideally, randomized) testing among the general population, the underestimation of cases in the denominator declines faster than the decline in the underestimation of deaths in the numerator.
Because area-wide shortages of testing kits “could artificially raise the CFR after days of decline” and therefore bias the decay analysis, it was limited to counties where no such increases significantly occurred.
From there, Basu used a mathematical model to project the rate of decline in estimated CFR into the future. This is how he arrived at an estimated IFR-S of 1.3 percent.
As Basu acknowledged, including only lab-confirmed cases results in an overestimate of the fatality rate. Consequently, he suggested that, to estimate the IFR, his IFR-S should be revised downward by an estimated rate of asymptomatic infections, which he put at 20 percent.
This brings us to the major flaw in his analysis, which is that he falsely assumes that the proportion of cases who never develop any symptoms equals the proportion of cases that are not lab-confirmed.
That is, in addition to excluding those “who remain asymptomatic with infections” from his denominator, he also inherently excludes those who do develop symptoms but are never reported or otherwise never receive lab-confirmation of a COVID-19 diagnosis.
There is no basis for that assumption whatsoever. It is a fatal flaw in his estimate.
In fact, there is scientific evidence suggesting that symptomatic unreported cases comprise not just a large proportion of total cases but perhaps a large majority. A study published on the preprint server medRxiv, which publishes papers that have not yet completed the peer-review process so as to make potentially groundbreaking information more rapidly available, estimated that the number of reported cases of COVID-19 over a three week period in March represented a mere fraction of the total number of symptomatic cases.
Using data from the CDC’s “influenza-like illness” (ILI) outpatient surveillance, the authors observed “a surge of non-influenza ILI above the seasonal average” and showed that this was “correlated with COVID case counts across states. By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be less than 13%.”
In other words, the data suggest that fewer than 13 percent of people who had symptomatic SARS-CoV-2 infection and sought health care were counted as COVID-19 cases.
Furthermore, it is likely that a great number of people who’ve had mild to moderate COVID-19 symptoms have not sought health care. The study authors hypothesize that if only one-third of people infected with the virus sought care, “the ILI surge would correspond to more than 8.7 million new SARS-CoV-2 infections across the US during the three week period from March 8 to March 28.”
That’s not the only evidence indicating that Lead Stories’ primary source makes a fatal error in estimating the disease burden of COVID-19. Additionally, a second type of test has become available that can be used to detect individuals who were previously infected but have since recovered. Whereas RT-PCR assays are used to detect current infections, serological tests are designed to detect specific antibodies to SARS-CoV-2 in the blood, with a positive test indicating that the person had mounted an immune response during a prior infection and has already cleared the virus from their system.
Numerous studies using serological testing to estimate the true rate of infection have now been conducted, and they indicate that far greater numbers of people have been infected with SARS-CoV-2 than the numbers of lab-confirmed COVID-19 cases indicate.
One such study was conducted by researchers from Stanford University who estimated the prevalence of infection in Santa Clara County, California, which had the largest number of confirmed cases of any county in the northern part of the state. Their results indicated that 2.8 percent of residents in the county had already been infected by April 1, which would translate into 54,000 people who experienced infection. This contrasts with approximately 1,000 confirmed cases reported in the county at the time they conducted their survey.
In other words, their results indicated that the number of infections was 54 times greater than the number of lab-confirmed cases, indicating that only 2 percent of cases received lab-confirmation.
Assuming a three-week lag from the time of infection until the time of death, given 94 COVID-19 deaths, this further implied an infection fatality rate of 0.17 percent.
Does that figure look vaguely familiar? It’s almost the same as the fatality rate presented by the meme, except that the meme’s creator mistakenly calculated a “survival rate” using 0.017 percent (with an extra zero after the decimal place). This study was presumably the creator’s source, but whoever made the meme got it wrong by misplacing the decimal.
Note that this could easily have been a mistake rather than representing dishonest intent. Had the creator wished to perpetrate a hoax, arbitrarily invented numbers would have sufficed. Assuming a simple mistake is not unreasonable. Smith, after all, made a similar unintended error in her own math, as already noted, by failing to convert the fraction to a decimal. The evidence is much stronger that Lead Stories perpetrated a deliberate hoax, including with misrepresentations of cited sources that cannot so easily be explained as honest mistakes. At the very least, by its own standard, the Lead Stories “Fact Check” article is no less of a hoax than the meme.
That said, to correct for the meme’s error, the survival rate it presented was 99.983 percent. It should have said 99.83 percent—and the legitimate point that the meme was attempting to communicate remains.
That’s not to say that the results of the Stanford study are conclusive. There are reasonable criticisms of its methodology, as with any study, and its results cannot be generalized to the entire US population (as the meme also mistakenly does). One problem with the study, though, is that 0.17 percent could still be an overestimate because recovery from infection doesn’t necessarily mean that the person will have developed a detectable level of antibodies.
In fact, depending on the virus and the individual, antibodies are neither always sufficient nor even necessary for the development of immunity. A study published at medRxiv on April 20 reported that, among 175 recovered COVID-19 patients, about 30 percent generated very low levels of SARS-CoV-2-specific antibodies, and ten patients (5.7 percent) had no detectable antibodies, suggesting that other immune responses were responsible for their recovery. Older patients were more likely to induce higher levels of antibodies than younger patients.
One thing, though, is certain: the Stanford researchers’ findings are a further strong indication that adjusting Basu’s estimated IFR-S downward by a mere 20 percent based on the estimated rate of asymptomatic cases aboard the Diamond Princess is not at all “a reasonable estimate of the overall IFR”.
If we assume that the rate of non-lab-confirmed infections with SARS-CoV-2 identified in the Stanford study is generalizable to the rest of the US, it means that Basu’s estimated IFR-S of 1.3 percent would need to be adjusted downward by 98 percent, which would give us an infection fatality rate of just 0.026 percent, or just one death per 3,846 infections.
The findings of the Santa Clara County study are bolstered by a similar study using immunological assays to determine the infection rate in Los Angeles County. What researchers found there was that the number of infections was 44 times greater than the number of confirmed cases, with the same rate of 98 percent of cases that were not lab-confirmed.
According to a New York Times report on April 23, serological testing in New York City had similarly indicated that as many as 2.7 million residents, one-fifth of the entire population, had already been infected. As of April 23, New York City was reporting 138,435 confirmed and probable cases. That means the infection rate was at least 20 times greater than the number of confirmed cases.
So, while the Stanford researchers’ findings are not generalizable, they do give us a scientific estimate of the infection fatality rate for one particular hard-hit county, which does serve as an indication of just how enormously Basu may have overestimated and which, in turn, further illustrates why Lead Stories’ fearmongering claim that the “mortality rate” is 1.3 percent isn’t just misleading but an outright lie.
To put a final nail in the coffin of Lead Stories’ demonstrably false claim that the mortality rate for people experiencing SARS-CoV-2 infection is 1.3 percent, consider that the CDC itself has recently published “COVID-19 Pandemic Planning Scenarios” providing “a current best estimate” that the overall symptomatic infection fatality rate is 0.4 percent.
Stratified by age, that works out to 1.3 percent for those aged sixty-five and older, 0.2 percent for those between the ages of fifty and sixty-five, and 0.05 percent in people under age fifty.
Notice that Lead Stories’ claimed overall crude “mortality rate” of 1.3 percent for the entire population happens to be equal to the CDC’s age-stratified estimate just for the highest risk group, and the CDC’s overall estimate is 69 percent lower.
Now, keep in mind that this is before accounting for asymptomatic infections. Conveniently, the CDC estimates that for us, too, putting the number at 35 percent.
The CDC doesn’t do the math for us to provide us with an estimated IFR based on its IFR-S adjusted downward by its estimated rate of asymptomatic infection. However, we can calculate it: if there are 1,000 infections, and if 35 percent are asymptomatic, then there are 650 symptomatic cases. If we multiply 650 by the symptomatic infection fatality rate of 0.4 percent, the result is 2.6 deaths per 1,000 total infections.
Thus, the CDC’s own “best estimate” of the overall infection fatality rate is 0.26 percent.
That’s 80 percent lower than the “mortality rate” Lead Stories uses to try to persuade people that extreme lockdown measures are absolutely necessary and not panic-driven.
To put it another way, according to the CDC’s estimate, the overall survival rate for SARS-CoV-2 infection is 99.74 percent.
We can also do age-adjusted estimates by applying the estimated overall asymptomatic infection rate of 35 percent, with the caveat that older people may are more likely to experience symptoms than younger people, and therefore the following numbers will overestimate the survival rate for the elderly and underestimate it for the younger group:
For those aged sixty-five and up, the survival rate is 99.15 percent.
For those aged ages fifty to sixty-four, the survival rate increases to 99.87 percent.
For those under age fifty, the survival rate increases to 99.97.
So, yes, the overall 99.983 percent survival rate presented by the meme is, as Facebook suggests, probably “overoptimistic”. But it’s still closer to the CDC’s own quietly released estimate than the 98.7 percent survival rate presented by Lead Stories in order to support the argument that extreme lockdown measures are absolutely necessary.
Yet if you share the link to the Lead Stories article on Facebook, Facebook will not flag it as “False” for presenting an overly pessimistic mortality rate or tell you that it’s a “hoax” for engaging in fearmongering by misrepresenting its own sources.
The logical corollary is that Facebook has no problem at all with misinformation, provided that it serves the right political agenda, which is to say that it’s fine as long as it serves to manufacture consent for existing government policies.
To reinforce that message that the extreme lockdown measures are necessary and appropriate, the Lead Stories “Fact Check” dramatically ends with a quote from the University of Washington press release intended to show that SARS-CoV-2 is far deadlier than influenza: “The COVID-19 death rate, the study adds, means that if the same number of people in the U.S. are infected by the end of the year as were infected with the influenza virus—roughly 35.5 million in 2018-2019—then nearly 500,000 people will die of COVID-19.”
This demonstrates how the university’s own press release also misleads the public about the contents of the study. While the release says that there were an estimated 35.5 million total influenza infections that season, as Basu points out in his paper, that number, too, includes only symptomatic infections.
The press release could have bolstered its case by saying so because, failing to have done so, readers might think it’s an invalid comparison: you can’t compare the IFR of flu with the IFR-S of COVID-19! So why not report this accurately?
We’re left to wonder. But consider that the undisclosed information also means that the claim we constantly hear from public health officials and the media that the death rate from influenza is 0.1 percent is wrong. Since this is calculated by including only symptomatic infections in the denominator, the true mortality rate is lower.
Basu gives us some idea of how much lower by citing a study estimating that 16 percent of influenza cases are completely asymptomatic. The CDC estimates that for the 2018 – 2019 flu season, there were 34,157 deaths and 35,520,883 symptomatic illnesses, which is an IFR-S of 0.096 percent (which rounds to the familiar 0.1 percent). Accounting for the 16 percent of asymptomatic infections, which was estimated in a 2016 study published in Epidemiology, the infection fatality rate for influenza would be 0.083 percent.
However, another 2016 study, published in the CDC’s journal Emerging Infectious Diseases, provides a range of 5.2 percent to 35.5 percent for influenza cases that are totally asymptomatic and an additional 25.4 percent to 61.8 percent for cases that are subclinical, meaning that they are so mild they do not meet the criteria for acute respiratory or influenza-like illness. So the true fatality rate for influenza could be considerably lower than even the 0.083 percent. If we add the averages of both these ranges together, we get another 64 percent of cases that should apparently be included in the denominator.
That gives us a fatality rate for influenza of just 0.035 percent.
It must also be understood that the CDC’s flu deaths estimates are calculated using mathematical models that may also greatly exaggerate the numbers, but that’s a whole other story. (You can read more about it here.) To put it into some perspective, though, consider that the number of deaths annually attributed to influenza on death certificates is little more than 1,000.
Long story short, the 0.1 percent that we’re told is the fatality rate of influenza is actually the estimated symptomatic infection fatality rate, so it’s valid to compare it to the CDC’s estimated overall IFR-S of 0.4 percent for SARS-CoV-2. This means that, if we accept the CDC’s estimates as reasonably accurate, SARS-CoV-2 is four times deadlier than influenza for those who develop symptoms, not ten times deadlier as claimed by Anthony Fauci or thirteen times deadlier as claimed by Lead Stories.
If we account for asymptomatic infections using the 16 percent rate for influenza presented by Basu, then SARS-CoV-2 is about three times deadlier than the flu was in 2018 – 2019, according to CDC data.
We can get SARs-CoV-2 to be ten times deadlier than the flu if we assume an asymptomatic infection rate for influenza of about 73 percent.
In other words, if SARS-CoV-2 is truly ten times as deadly as influenza, as Anthony Fauci has claimed and as we are routinely told by the media, then either the flu is far less deadly than they claim or SARS-CoV-2 is far less deadly than they claim.
They can’t have it both ways. No matter how you look at it, the conclusion is inescapable that we are being lied to.
Coming back to the press release, an IFR-S of 1.3 percent would imply that if 35.5 million people were infected with SARS-CoV-2, there would be 461,500 deaths from COVID-19. That’s far greater than the maximum death toll from the flu for any given season as estimated by the CDC, which is about 61,000.
While Lead Stories leaves it at that, the press release took it even further. Basu argues that SARS-CoV-2 is “more infectious” than influenza and therefore that a “conservative estimate” is that 20 percent of the US population will have become infected by the end of this year even with “the current trends in social distancing and health care supply continuing”. Given his estimations, and “accounting for those infected who will recover asymptomatically”, that means that the COVID-19 death toll will be “between 350,000 and 1.2 million”.
In other words, Basu projects that even with current lockdown measures remaining in place for the rest of the year, there will be at least 350,000 deaths and possibly over a million.
This is difficult to reconcile with the observed epidemic curve indicating that it has already peaked. The trend has been a general decline in daily new cases and daily deaths since April. The worst three weeks were from April 5 through April 25, during which there were 44,467 deaths, according to the CDC’s provisional counts. For the three weeks prior, as daily case counts were increasing, the total was 12,913 deaths. For each the three weeks after, from April 26 through May 16, the numbers declined, with 25,354 deaths in total.
That’s a 43 percent decline from the worst three weeks compared to the three weeks after.
To reach 350,000 by year’s end would mean there would have to be 267,175 deaths from May 17 forward. That’s about seven and a half more months, so about 35,623 additional deaths per month to reach Basu’s most “conservative” projected toll.
If things were to go on as he suggests, we’d have to see about 10,269 deaths for the week ending May 23. Yet the CDC reports just 1,688 deaths that week. This will likely increase some as additional data comes in, but it’s improbable that it will increase by 508 percent. The number of deaths for the week ending May 30 is 222.
The observable trend simply does not fit with even Basu’s most conservative projection of the death toll.
If he thinks the present epidemic wave will subside and then there will be a massively deadly second wave later in the year, he doesn’t say so—much less offer any explanation as to why this would happen if the lockdown measures continue and are effective.
After presenting his projected death toll of possibly over a million deaths, the press release quotes Basu saying, “This is a staggering number, which can only be brought down with sound public health measures.”
But what additional measures is he talking about? His estimate assumes the continuation of lockdowns, with the term “social distancing” being used euphemistically to mean indiscriminate quarantine of entire populations, and with “stay-at-home” orders in many states that have limited people’s freedom to only going out for things that they need in order to survive, like buying food and supplies or getting outdoor exercise.
The endgame of these lockdown measures has from the very beginning been mass vaccination. Politicians implementing these measures have no exit strategy apart from the development of pharmaceutical treatments and faith in vaccine technology. Presumably, this is also what Basu—coming from a school of pharmacy—has in mind, too.
There is a need, in the mind of mass vaccination proponents, to manufacture consent for any future coronavirus vaccine, too—and to the extent that’s not feasible, to mandate its use. Never mind that no vaccine even exists yet that has been demonstrated through randomized, placebo-controlled trials to be both safe and effective.
Basu’s projections play into that perceived need well by helping to create the necessary fear.
It’s tempting to conclude that the decline in reported cases and deaths in the US is due to the extreme lockdown measures. Indeed, we have been hearing the argument that if the measures are lifted, there will be a resurgence in cases and deaths.
We were told that the measures were necessary to “flatten the curve”, meaning to slow the rate of transmission enough to avoid the health care system from being overwhelmed. But Sweden managed to flatten the curve without ordering people to stay at home and thereby shutting down its economy. It accomplished the goal rather by issuing sensible social distancing and hygienic guidelines to slow the spread, which members of the public, recognizing what was in their own best interests, voluntarily complied with.
Lockdown advocates frequently point out that Sweden’s rate of deaths per capita is higher than its Scandinavian neighbors who locked down. But it is also lower than other European countries that did lock down: the UK, Spain, France, and Belgium.
Additionally, public health authorities in Sweden’s neighbor Norway have published a report showing that the rate of transmission had already fallen so much by the time lockdown measures were implemented on March 12 that the subsequent decline in daily numbers can’t be attributed to the measures. The director of Norway’s Public Health Institute, Camilla Stotlenberg, candidly stated, “Our assessment now, and I find that there is a broad consensus in relation to the reopening, was that one could probably achieve the same effect—and avoid part of the unfortunate repercussions—by not closing. But, instead, staying open with precautions to stop the spread.” (Emphasis added.)
Part of the explanation for the observed trends is that people’s behavior naturally changes when they understand that a potentially deadly virus is circulating. They take sensible precautions, like avoiding touching their face with potentially contaminated hands, washing their hands more frequently, isolating themselves or loved ones whom they deem to be at higher risk, and distancing themselves more from others when they go out. People don’t need to be ordered to do these things because they instinctively recognize that it’s in their own best interest to avoid becoming infected and, if they do, to avoid spreading the infection to their loved ones.
Another possible part of the explanation is the seasonal nature of transmission for viruses that cause respiratory illnesses. We all know that the flu is seasonal, as are, to one extent or another, common human coronaviruses that are a common cause of the common cold. The data indicate that the transmissibility of SARS-CoV-2 is also affected by the weather. Factors like ultraviolet (UV) radiation from the sun and humidity can affect the transmissibility of viruses. People’s behavior changes, too, during warmer months. Vitamin D deficiency is known to be associated with increased risk of respiratory viral illnesses like the flu, which is one reason why it helps for people to get out in the sunshine more. Another reason it helps to get outdoors more is that a higher dose and longer duration of exposure is a risk factor for more severe disease—as opposed to being indoors together all the time, whether due to the weather or due to “stay-at-home” orders.
There is an opportunity cost of avoiding low dose, short duration exposures while out in public since such exposures could enable young healthy people who are at very low risk to acquire immunity while experiencing a milder course of disease or no symptoms at all.
This brings us to a third explanation for the observed trends: natural herd immunity. It is the nature of viral epidemics to come and go in waves, with rapid increases in cases and deaths near the start followed by a natural decline as it peters out. This is because the number of people who are susceptible to infection is inversely proportional to the number of people who’ve recovered and acquired immunity.
So, while SARS-CoV-2 may be more contagious than influenza, this is largely if not wholly a function of it spreading through an immunologically naïve population. As the epidemic wave rolls on and an increasing number of survivors gain immunity, the rate of transmission decreases.
Essentially, we are being told that we must remain in lockdown and thereby prevent the development of population immunity in order to give pharmaceutical corporations time to rush a vaccine to market.
One thing lockdown proponents fail to consider with respect to their criticisms of Sweden is that, by not locking down, Sweden will have developed a greater measure of population immunity. Consequently, it will have already seen the worst of things, and if viral transmission turns out to be at least partly seasonal in nature, any second wave come fall will likely be less severe for Sweden—whereas it will hit harder in the lockdown countries. Sweden also does not have to struggle with how to lift extreme measures without causing a deadly resurgence. The lockdown countries, on the other hand, face a choice of either perpetuating the measures indefinitely and thereby causing incalculable harm to their economies or to lift the measures and risk a second epidemic wave.
In fact, the infamous Imperial College London model published in March that was so influential in policymakers’ decision-making projected precisely this outcome. This was the model credited with causing the UK to change course from sensible social distancing measures to extreme economic shutdown. The model projected that, once five months of lockdown measures are lifted, the resulting second wave would be worse than what would have occurred had the UK simply kept on for three months with a policy of isolating cases, quarantining family members of infected individuals, and social distancing to protect the elderly.
The Imperial College model, incidentally, was based on data from China obtained using RT-PCR assays and assumed an estimated overall infection fatality rate of 0.9 percent—more than five times higher than that estimated by Stanford researchers based on immunoassay testing and more than three times higher than that indicated by the CDC’s best estimate.
Another thing lockdown proponents fail to account for is the economic costs of these measures. The authors of the Imperial College paper acknowledged that they “do not consider the ethical or economic implications” of the lockdown policy that they advocated. Quoting Ira Longini, a modeler at the University of Florida, Science magazine points out, “The economic fallout isn’t something epidemic models address, Longini says—but that may have to change. ‘We should probably hook up with some economic modelers and try to factor that in,’ he says.”
“Probably”?! What this means is that lockdown measures have been implemented without any risk-benefit analysis being done to assess whether they will cause a net benefit or net harm.
And the economic costs will not be just in terms of dollars, lost productivity, and a reduction in standard of living. There will also be an incalculable long-term cost in health and lives.
Poverty, for example, is associated with poorer health and increased mortality, so is driving millions of people toward financial ruin really such a wise idea? The long-term as well as the short-term negative consequences must be factored into the equation.
In that regard, we must also consider the COVID-19 death toll not just in terms of lives lost, but in terms of years of life lost and quality years of life lost. If lockdown measures mean extending the life of an elderly person by another year or two but the “collateral damage” means the loss of another life due to increased infant mortality associated with increased poverty, are the measures worth it?
The UN has estimated that the global economic harm will cause such an increase in poverty that it would “represent a reversal of approximately a decade in the world’s progress in reducing poverty.”
A separate UN policy brief on the estimated impact on children points out that governments have implemented “mitigation measures that may inadvertently do more harm than good.” But the damage will not be equally distributed. It is the poorest among us who will be disproportionately harmed.
The paper states that “Economic hardship experienced by families as a result of the global downturn could result in hundreds of thousands of additional child deaths in 2020, reversing the last 2 to 3 years of progress in reducing infant mortality within a single year.” (Emphasis added.)
The authors of the paper regarded this as “likely an under-estimate”. Malnutrition will increase. Children’s health will suffer. Those who do survive early childhood could face a reduced life expectancy as a result.
These are just a few considerations that lockdown advocates seem unable or unwilling to weigh against any potential benefits of shutting down the economy. There are countless others. But just looking at the estimated cost in terms of infant mortality alone, it is clear that the risks from economic shutdowns are too great to justify any potential benefit.
As Stanford scientists have pointed out, in a study separate from the one in Santa Clara County, the available data on SARS-CoV-2 suggest that “the vast majority of infections are either asymptomatic or mildly symptomatic and thus do not come to medical attention. These data also suggest that the infection fatality rate may be close to that of a severe influenza (<0.2%) when the health system does not collapse and when massive nosocomial infections and nursing home spread are averted.” (Emphasis added.)
Nosocomial infections are those that occur in the hospital setting. Hospitals have been major sources of transmission during outbreaks. As Tom Jefferson and Carl Heneghan of the Centre for Evidence-Based Medicine put it, “An older person admitted to hospital runs the risk of never seeing the light of day again. This is probably the clearest message coming from Italy.”
They also pointed out that “the price of lockdown to society and economic paralysis is likely to be paid for generations to come.” The situation, they wrote, “boils down to this: is economic meltdown a price worth paying to halt or delay what is already amongst us?”
Furthermore, lockdown measures have utterly failed to prevent deaths among those at highest risk: elderly people living in nursing care homes.
According to a New York Times report on May 11, one-third of all COVID-19 deaths in the US have occurred in care home residents or workers.
According to data from the Kaiser Family Foundation (KFF) current as of May 28, at least 43 percent of all deaths in the US have occurred in long-term care facilities. There are thirty-nine states that have reported such deaths. Among those states, there are twenty-six in which at least half of all deaths have occurred in care homes. The Foundation also features a map showing what mitigation policies were implemented in each state. Among the twenty-six states, only two—Nebraska and Utah—never issued executive “stay-at-home” orders. The other twenty-four states where 50 percent or more of all deaths occurred in elderly care homes were lockdown states.
A huge proportion of deaths in the US have occurred in New York City, where the count of confirmed and probable deaths is 21,602 as of June 1. That’s over 20 percent of all reported deaths in the US. It didn’t help that on March 25, the New York state government issued an order mandating nursing homes to take in patients discharged from hospitals who were confirmed or presumed to have COVID-19. The order wasn’t reversed until May 10, more than a month later and after the epidemic and death rate had already peaked in April.
Coming back to the second study by Stanford researchers, they continued by noting that “high infection fatality rates are seen when hospitals are overrun, and there are massive death loads from nosocomial-infected hospitalized patients and nursing home residents (e.g., in New York).” By contrast, “the vast majority of the population may be reassured that their risks are very low.” (Emphasis added.)
Quantifying that risk, they pointed to data “set explicitly to include the locations with the highest numbers of deaths” from European countries and the US, which showed that people under the age of 65 without underlying medical conditions identified as risk factors for severe disease have accounted for only 0.7 percent to 2.6 percent of all COVID-19 deaths. People aged 40 or under “have almost no risk at all of dying”, and females may have two to three times lower risk than males. While news stories have contributed to the sense of panic with stories about young people dying, the data show that “for healthy non-elderly people, the risk of dying from COVID-19 this season has been infinitesimally small.”
Consequently, “the loss of quality-adjusted life-years from COVID-19 may be much smaller than a crude reading of the number of deaths might suggest”, whereas “long-term lockdowns may have major adverse consequences for health (suicides, domestic violence, worsening mental health, cardiovasulcar disease, loss of health insurance from unemployment, and famine, to name a few) and society at large.”
“Strategies focused specifically on protecting high-risk elderly individuals”, they conclude, “should be considered in managing the epidemic.”
Sensible mitigation policies would have focused on protecting those at highest risk while allowing the rest of the population to continue making a living and go on with their lives in a socially responsible manner, which would have the consequence of also enabling the development of the population immunity required to reduce transmission, thereby ultimately reducing risk to frail elderly people so that they, too, could finally come out of isolation and enjoy the remaining time they have left with us.
The extreme and authoritarian lockdown measures implemented instead have essentially achieved the opposite.
The Lead Stories so-called “Fact Check” cited by Facebook to flag the meme post is an illuminating case study of how major media serve the function of manufacturing consent for government policies by deceiving the public. This is not an isolated case. It is typical and routine. Hypocritical self-proclaimed “fact check” sites fulfill this function by issuing flak selectively at sources critical of government policies while themselves frequently deceiving the public in support of those same policies.
For example, the website FactCheck.org has claimed that the mercury used in flu shots and formerly in numerous other vaccines on the CDC’s routine childhood schedule is harmless and that science has proven that vaccines don’t cause autism. The website’s source for these assertions was the CDC’s website and a 2004 review by the Institute of Medicine (IOM). The CDC in turn also cites several observational studies and the IOM review.
Yet the IOM in fact acknowledged that the mercury-based preservative thimerosol is a “known neurotoxin” that “accumulates in the brain” and “can injure the nervous system”.
The IOM also acknowledged that the hypothesis that vaccines can contribute to the development of autism in susceptible children cannot be excluded by observational studies and, moreover, that none of the studies included in their review were actually designed to test that hypothesis.
In February 2019, Congressman Adam B. Schiff sent letters to the CEOs of Facebook, Google, and Amazon, essentially calling on these companies to help the government censor any information about vaccines that wasn’t in line with the goals of public vaccine policy. Ostensibly, the purpose was to combat “misinformation” about vaccines, but his criteria applied only to what he termed “anti-vaccine” information and were inclusive of any information, no matter truthful and well-grounded in science, that might lead parents to “decline to follow the recommended vaccination schedule.” Schiff himself blatantly lied about vaccine safety in the letter by asserting that there is “no evidence to suggest that vaccines cause life-threatening or disabling diseases”.
In keeping with that perceived duty, Facebook has, for example, flagged a post saying that vaccines can cause encephalopathy as “False”, citing an article by Health Feedback. (Encephalopathy encompasses any type of brain damage, disorder or disease, including encephalitis, or brain inflammation.)
Yet the vaccine manufacturer Merck in its bestselling medical textbook the Merck Manual states explicitly that “Encephalitis can occur as a secondary immunologic complication of certain viral infections or vaccinations.”
And the US government, under its Vaccine Injury Compensation Program, which along with legal immunity for vaccine manufacturers serves to shift the financial burden for injuries away from the pharmaceutical companies and onto the taxpaying consumers, lists encephalopathy and encephalitis as compensable vaccine injuries.
In a famous case, a girl named Hannah Poling developmentally regressed into diagnosed autism after receiving nine vaccine doses at once at 19 months of age. The government acknowledged the vaccinations “significantly aggravated an underlying mitochondrial disorder, which predisposed her to deficits in cellular energy metabolism, and manifested as a regressive encephalopathy with features of autism spectrum disorder.”
Another Facebook “Fact Check” cites an AFP article and a Lead Stories article to flag a video as false for reporting that the World Health Organization’s chief scientist had been caught lying to the public about vaccine safety. Both “Fact Check” articles denied that the WHO chief scientist had lied.
Yet neither of those articles bothered to explain how the report was untrue when the scientist had in fact claimed in a WHO video published on YouTube that “robust vaccine safety systems” exist in countries around the globe that enable scientists working closely with the WHO to ensure that vaccines are administered “without risks” only to admit a few days later to her colleagues in a WHO meeting that “we cannot overemphasize the fact that we really don’t have very good safety monitoring systems in many countries” and that the risk of serious adverse events being discovered only after a vaccine is on the market is “always there”.
I could cite other examples, but it would be superfluous. It is absolutely clear to any free-thinking individual that “misinformation” is being used euphemistically so as to encompass any information, no matter how truthful, that does not suit a given political agenda. Neither Facebook nor its “Fact Check” partners are particularly concerned with educating the public and telling the truth. What they are more concerned with is manipulating public opinion through censorship and political propaganda.
In the case of the COVID-19 “survival rate” meme, the Lead Stories article Facebook cites is correct to point out that the claimed fatality rate is unsupported by available scientific evidence. But the political agenda is apparent in how Lead Stories goes so far in its effort to advocate lockdowns as to grossly deceive its readers into thinking that for every 77 people who become infected with SARS-CoV-2, one will die. This in turn creates the fear necessary to manufacture consent for authoritarian policies that are unsupported by scientific evidence and could easily be causing far more harm than good.
[Correction appended June 2, 2020: As originally published, this article stated that an increase in deaths from 1,688 to 10,269 would be an 84 percent increase. Percent increase is calculated by subtracting the original number from the final number, dividing that by the original number, then multiplying by 100 to convert to a percentage. I mistakenly divided by the final number. The correct number is 508 percent. Thanks to Franklin for pointing out the error in the comments. Corrections appended, Jun 3, 2020: As originally published, I misattributed to to Lead Stories the claim that 70 million to 150 million people in the US would die from COVID-19 and accused Lead Stories on those grounds of fabricating a death toll two orders of magnitude higher than that implied by numbers presented in the CNBC article. This was a careless misreading and was not what Smith wrote. My only excuse is mental exhaustion. The error has been corrected and the remaining criticism in that section clarified. Additionally, I had written that Lead Stories leads readers to believe that for every 130 people infected, one will die, but what the cited mortality rate leads readers to believe is that for every 77 people infected, one will die. Thanks to Paul for bringing both of these errors to my attention. Correction appended June 23, 2020: As originally published, this article stated, “In other words, their results indicated that the number of infections was 54 times greater than the number of lab-confirmed cases, indicating that only 98 percent of cases received lab-confirmation.” It rather indicated that only 2 percent of cases received lab confirmation. The 98 percent was the proportion of infections for which there was no lab confirmation.]
 Gita Smith, “Fact Check: U.S. Survival Rate From COVID-19 Is NOT 99.983 Percent”, Lead Stories, May 21, 2020, accessed May 25, 2020, https://leadstories.com/hoax-alert/2020/05/fact-check-u.s.-survival-rate-from-covid-19-Is-not-99.8-percent.html.
To view a whole page screenshot of the article as it appeared to me on May 25, click here: https://www.jeremyrhammond.com/wp-content/uploads/2020/05/200521-lead-stories-archived.pdf.
The Facebook post sharing the meme is located here: https://www.facebook.com/photo.php?fbid=10221780214430846&set=a.1713746239755&type=3.
 Archived versions of the page can be viewed here: http://web.archive.org/web/*/https://www.worldometers.info/coronavirus/country/us/.
 “Changes in United States Data following the new CDC guidelines on ‘Case’ and ‘Death’ definition”, Worldometers, accessed May 25, 2020, https://www.worldometers.info/coronavirus/us-data/.
 Centers for Disease Control and Prevention, “Provisional Death Counts for Coronavirus Disease (COVID-19)”, CDC.gov, updated May 22, 2020, accessed May 25, 2020, https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm.
 Johns Hopkins University, “COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)”, Coronavirus Resource Center, accessed May 26, 2020, https://coronavirus.jhu.edu/map.html.
 “Coronavirus in the U.S.: Latest Map and Case Count”, New York Times, updated May 26, 2020, accessed May 26, 2020, https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.
 World Health Organization, “Naming the coronavirus disease (COVID-19) and the virus that causes it”, WHO.int, accessed May 25, 2020, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.
 William C. Shiel, Jr., “Medical Definition of Disease”, MedicineNet, accessed May 25, 2020, https://www.medicinenet.com/script/main/art.asp?articlekey=3011.
 Centers for Disease Control and Prevention, “Clinical Questions about COVID-19: Questions and Answers”, CDC.gov, updated May 12, 2020, accessed May 25, 2020, https://www.cdc.gov/coronavirus/2019-ncov/hcp/faq.html.
 WHO, “Naming the coronavirus disease (COVID-19).
World Health Organization, “Advice on the use of masks in the context of COVID-19”, WHO.int, April 6, 2020, https://www.who.int/publications-detail/advice-on-the-use-of-masks-in-the-community-during-home-care-and-in-healthcare-settings-in-the-context-of-the-novel-coronavirus-(2019-ncov)-outbreak.
 Smith describes the study as being done by “researchers”, plural, and there were others involved in research and reviewing his paper, but Basu’s is the only name in the byline so I will refer to him as the sole author.
 Gita Smith, “Fact Check: U.S. Survival Rate From COVID-19 Is NOT 99.983 Percent”, Lead Stories, May 21, 2020, https://leadstories.com/hoax-alert/2020/05/fact-check-u.s.-survival-rate-from-covid-19-Is-not-99.8-percent.html.
 Dr. Anthony Fauci, Testimony to the House Oversight and Reform Committee Hearing on Coronavirus Response, C-SPAN, March 11, 2020, https://www.c-span.org/video/?470224-1/dr-fauci-warns-congress-coronavirus-outbreak-worse&start=5863.
 Jake Ellison, “COVID-19: UW study reports ‘staggering’ death toll in US among those infected who show symptoms”, UW News, May 18, 2020, https://www.washington.edu/news/2020/05/18/covid-19-uw-study-reports-staggering-death-rate-in-us-among-those-infected-who-show-symptoms/.
 To calculate this: .013 – (.013 x .2) = .0104
 Kenji Mizumoto et al., “Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020”, Eurosurveillance, March 12, 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078829/.
 Basu. He was evidently able to parse confirmed from non-confirmed cases by using county-level data.
 Justin D Silverman, Nathaniel Hupert, and Alex D Washburne, “Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States”, medRxiv, April 26, 2020, https://doi.org/10.1101/2020.04.01.20050542.
 Jeremy R. Hammond, “Paul Offit Unwittingly Exposes Scientific Fraud of FDA’s Vaccine Licensure”, JeremyRHammond.com, July 26, 2019, https://www.jeremyrhammond.com/2019/07/26/paul-offit-unwittingly-exposes-scientific-fraud-of-fdas-vaccine-licensure/.
 Fan Wu et al., “Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications”, medRxiv, April 20, 2020, https://doi.org/10.1101/2020.03.30.20047365.
 To calculate this: (0.013 – (0.013 x 0.98) = 0.00026). But, again, since the national counts include both confirmed and probable cases and the CDC has decided to include both under a single diagnostic code, it’s seemingly impossible to parse the numbers and estimate what percentage of total cases are confirmed versus presumed. So, if, on the other hand, we assume Basu’s numbers do include probable as well as confirmed cases, then it implies his estimate would need to be adjusted downward by 87 percent—not a mere 20 percent—to arrive at an IFR of 0.17 percent.
 Neeraj Sood et al., “Seroprevalence of SARS-CoV-2–Specific Antibodies Among Adults in Los Angeles County, California, on April 10-11, 2020”, JAMA, May 18, 2020, https://doi.org/10.1001/jama.2020.8279.
 J. David Goodman and Michael Rothfeld, “1 in 5 New Yorkers May Have Had Covid-19, Antibody Tests Suggest”, New York Times, April 23, 2020, https://www.nytimes.com/2020/04/23/nyregion/coronavirus-antibodies-test-ny.html.
New York City, “Coronavirus Disease 2019 (COVID-19) Daily Data Summary”, NYC Health, April 22, 2020, archived April 22, 2020, accessed June 2, 2020, http://web.archive.org/web/20200422212122/https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-daily-data-summary-04222020-1.pdf. The city parsed the data for confirmed and probable deaths, but not for cases.
 Centers for Disease Control and Prevention, “COVID-19 Pandemic Planning Scenarios”, CDC.gov, reviewed May 20, 2020, accessed May 27, 2020, https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html.
 This assumes a consistent rate of asymptomatic infection across these age groups. This probably underestimates the survival rates for younger people since they are likely to have a higher rate of asymptomatic infection, whereas elderly people likely have a lower rate.
 The difference between survival rate of 98.7 percent we get from Lead Stories’ claimed death rate and the one we get from the CDC’s estimate is 1.04 percent, whereas the difference between the meme’s and the CDC’s is 0.243 percent. The Stanford study’s estimated IFR of 0.17 gets us even closer yet, with a 0.09 percent difference from the CDC’s estimate. I say the estimate was quietly released because I am unaware of any accompanying press release from the CDC and have yet to see any mainstream media outlet reporting about it.
 Centers for Disease Control and Prevention, “Estimated Influenza Illnesses, Medical visits, Hospitalizations, and Deaths in the United States — 2018–2019 influenza season”, CDC.gov, reviewed January 8, 2020, accessed May 26, 2020, https://www.cdc.gov/flu/about/burden/2018-2019.html.
 Nancy Leung et al., “The fraction of influenza virus infections that are asymptomatic: a systematic review and meta-analysis”, Epidemiology, November 1, 2016, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586318/.
 Jeremy R. Hammond, “How the CDC Uses Fear Marketing to Increase Demand for Flu Vaccines”, Foreign Policy Journal, June 19, 2018, https://www.foreignpolicyjournal.com/2018/06/19/how-the-cdc-uses-fear-marketing-to-increase-demand-for-flu-vaccines/.
 Tom Jefferson et al., “Why have three long-running Cochrane Reviews on influenza vaccines been stabilized?” Cochrane Community, January 29, 2018, https://community.cochrane.org/news/why-have-three-long-running-cochrane-reviews-influenza-vaccines-been-stabilised.
 If the IFR for SARS-CoV-2 of 0.26 percent were ten times that of influenza, then the IFR of influenza would be 0.026 percent. If the IFR-S of flu is 0.096 percent, this gives us: (0.00096 – (0.00096x) = 0.00026). Therefore, (x = 0.729) or an asymptomatic infection rate of about 73 percent.
 Worldometers, accessed May 27, 2020.
 Centers for Disease Control and Prevention, “Provisional Death Counts for Coronavirus Disease (COVID-19)”, CDC.gov, updated May 22, 2020, accessed May 26, 2020, https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm.
 Jeremy R. Hammond, “SARS-CoV-2 Response: Imperial College Model and Lockdown Endgame”, JeremyRHammond.com, April 17, 2020, https://www.jeremyrhammond.com/2020/04/17/sars-cov-2-response-imperial-college-model-and-lockdown-endgame/.
 Tanya Albert Henry, “How to ready patients now so they’ll get a COVID-19 vaccine later”, American Medical Association, May 27, 2020, https://www.ama-assn.org/delivering-care/public-health/how-ready-patients-now-so-they-ll-get-covid-19-vaccine-later.
Dorit R. Reiss and Arthur L. Caplan, “Considerations in Mandating a New Covid-19 Vaccine in the USA for Children and Adults”, Journal of Law and the Biosciences, May 8, 2020, https://doi.org/10.1093/jlb/lsaa025.
 Knut M. Wittkowski, “The first three months of the COVID-19 epidemic: Epidemiological evidence for two separate strains of SARS-CoV-2 viruses spreading and implications for prevention strategies”, medRxiv, April 29, 2020, https://doi.org/10.1101/2020.03.28.20036715.
 Worldometers, accessed May 27, 2020.
 Fraser Nelson, “Norway health chief: lockdown was not needed to tame Covid”, The Spectator, May 27, 2020, https://www.spectator.co.uk/article/norway-health-chief-lockdown-was-not-needed-to-tame-covid.
Folkehelseinstituttet, “COVID-19-EPIDEMIEN: Kunnskap, situasjon, prognose, risiko og respons i Norge etter uke 18”, May 5, 2020, https://www.fhi.no/contentassets/c9e459cd7cc24991810a0d28d7803bd0/notat-om-risiko-og-respons-2020-05-05.pdf.
 Ran Xu et al., “The Modest Impact of Weather and Air Pollution on COVID-19 Transmission”, Harvard University, May 23, 2020, https://projects.iq.harvard.edu/files/covid19/files/weather_and_covid-19_preprint.pdf.
 I’m using the term “rate of transmission” for simplicity, but technically I’m referring to what’s called the reproduction number, or R0 (R-“naught”), which is a measure of how many additional people will be infected for each infected case. So, for example, an R value of 2 means that each infected person will on average spread the virus to two additional people. Since those two additional people will also on average spread it to two more people, the nature of viral transmission is exponential growth in the number of infections during the early stage of the epidemic, but the R0 declines over time for the reason explained. The
 Hammond, “SARS-CoV-2 Response”.
 Neil M Ferguson et al., “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand”, Imperial College London, March 16, 2020, https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf.
 Martin Enserink and Kai Kupferschmidt, “Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies”, Science, March 25, 2020, https://www.sciencemag.org/news/2020/03/mathematics-life-and-death-how-disease-models-shape-national-shutdowns-and-other.
 The media constantly refer to the harmful consequences of extreme lockdown measures as “collateral damage” of “COVID-19”—as though it was the virus and not the politicians responsible for the damage. See Google search results for examples: https://www.google.com/search?q=collateral+damage+of+covid
 Andy Summer, Chris Hoy, and Eduardo Ortiz-Juarez, “Estimates of the impact of COVID-19 on global poverty”, United Nations University World Institute for Development Economics Research, April 2020, https://doi.org/10.35188/UNU-WIDER/2020/800-9.
 “Policy Brief: The Impact of COVID-19 on children”, United Nations Sustainable Development Group, April 2020, https://unsdg.un.org/resources/policy-brief-impact-covid-19-children.
 John P.A. Ioannidis, Cathrine Axfors, and Dispina G. Contopoulos-Ioannidis, “Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters”, medRxiv, May 5, 2020, https://doi.org/10.1101/2020.04.05.20054361.
 Tom Jefferson and Carl Heneghan, “COVID-19 – The Tipping Point”, Centre for Evidence-Based Medicine, April 8, 2020, https://www.cebm.net/covid-19/covid-19-the-tipping-point/.
 Karen Yourish, “One-Third of All U.S. Coronavirus Deaths Are Nursing Home Residents or Workers”, New York Times, May 11, 2020, https://www.nytimes.com/interactive/2020/05/09/us/coronavirus-cases-nursing-homes-us.html.
 Kaiser Family Foundation, “State Data and Policy Actions to Address Coronavirus”, KFF.org, updated June 1, 2020, accessed June 2, 2020, https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/.
 City of New York, “COVID019: Data”, NYC Health, updated June 1, 2020, accessed June 2, 2020, https://www1.nyc.gov/site/doh/covid/covid-19-data.page.
 Worldometers, accessed May 27, 2020.
 Anna Wilde Mathews, “New York Sent Recovering Coronavirus Patients to Nursing Homes: ‘It Was a Fatal Error’”, Wall Street Journal, May 14, 2020, https://www.wsj.com/articles/new-york-sent-recovering-coronavirus-patients-to-nursing-homes-it-was-a-fatal-error-11589470773.
 Ioannidis et al.
 For more on how the media serve this function, see: Edward S. Herman and Noam Chomsky, Manufacturing Consent: The Political Economy of the Mass Media (Pantheon, 1982).
 Jeremy R. Hammond, “FactCheck.org, Following CDC’s Example, Lies about Vaccine Safety”, JeremyRHammond.com, December 24, 2018, https://www.jeremyrhammond.com/2018/12/24/factcheck-org-following-cdcs-example-lies-about-vaccine-safety/.
 Jeremy R. Hammond, “How the Media Lie about Why Parents Don’t Vaccinate”, JeremyRHammond.com, October 17, 2019, https://www.jeremyrhammond.com/2019/10/17/how-the-media-lie-about-why-parents-dont-vaccinate/.
 Adam B. Schiff, letter to Mark Zuckerberg, Chairman and Chief Executive Officer of Facebook Inc., dated February 14, 2019, https://childrenshealthdefense.org/wp-content/uploads/Adam-Schiff-Letter.pdf. Schiff sent identical letters to the CEOs of Google and Amazon. For a detailed analysis illustrating the deceitful nature of Schiff’s letters, including how Schiff himself lied about vaccine safety, see: “Children’s Health Defense Sends Letters to Facebook, Amazon and Google”, Children’s Health Defense, March 4, 2019, https://childrenshealthdefense.org/news/childrens-health-defense-sends-letters-to-facebook-amazon-and-google/. I authored these rebuttal letters on behalf of Children’s Health Defense.
 Jeremy R. Hammond, “Facebook ‘Fact-Checker’ Misinforms Users about Vaccine Safety”, JeremyRHammond.com, June 17, 2019, https://www.jeremyrhammond.com/2019/06/17/facebook-fact-checker-misinforms-users-about-vaccine-safety/.
 Jeremy R. Hammond, “Fact Check: WHO Scientist Caught Lying to Public about Vaccine Safety”, JeremyRHammond.com, February 11, 2020, https://www.jeremyrhammond.com/2020/02/11/fact-check-who-scientist-caught-lying-to-public-about-vaccine-safety/.