The new CDC estimate of the infection fatality rate for COVID is .25% to .3%, putting it close to ordinary flu, which is .1% of infections. That means the survival rate of all people who get COVID-19 is about 99.8%, versus flu at about 99.9%
The infection mortality estimate includes all infections, both diagnosed and undiagnosed. Care should be taken to distinguish the infection mortality rate (IFR) from the case fatality rate (CFR,) which only includes tested, confirmed cases. COVID has a high number of asymptomatic and mild symptom cases, according to the WHO about 80%. The CFR of COVID is about 5%.
The revised CDC estimate follows a study from U Penn Medical Center which puts the infection fatality rate at .25%. In the early stages of the US response to the virus, infection was, mistakenly, sought to be as high as 6%.
The decline in the CDC’s infection mortality rate has generated a spate of “fake news” debunks attempting to show COVID is far deadlier than flu, such as National Geographic’s “How scientists know COVID-19 is way deadlier than the flu” which relies on an analysis by a graduate student at an Australian university, who describes himself as an “epidemiologist.”
Many believe that actual COVID deaths are vastly over-inflated, due to countless anecdotes of patients who may have died with COVID, but primarily of other causes.
Are “Lockdowns” and “Social Distancing” Necessary?
There is now no good reason to continue the “lockdowns” or any form of social distancing among healthy people. But the goal posts have been moved from “flattening the curve,” in order to not overwhelm the health care system, to the unnatural and impossible goal of stopping the spread all disease.
This is a recipe for the indefinite fascism of arbitrary quarantine and contact tracing. It is incredible that in America, authorities are seriously talking about recording who you have been with at any time, the mark of an oppressive and intrusive police state.
Shutdowns and social distancing are delaying the wall of herd immunity, which is the build up of natural resistance in a population.
That rate is much lower than the numbers used in the horrifying projections that shaped the government response to the epidemic.
According to the Centers for Disease Control and Prevention (CDC), the current “best estimate” for the fatality rate among Americans with COVID-19 symptoms is 0.4 percent. The CDC also estimates that 35 percent of people infected by the COVID-19 virus never develop symptoms. Those numbers imply that the virus kills less than 0.3 percent of people infected by it—far lower than the infection fatality rates (IFRs) assumed by the alarming projections that drove the initial government response to the epidemic, including broad business closure and stay-at-home orders.
The CDC offers the new estimates in its “COVID-19 Pandemic Planning Scenarios,” which are meant to guide hospital administrators in “assessing resource needs” and help policy makers “evaluate the potential effects of different community mitigation strategies.” It says “the planning scenarios are being used by mathematical modelers throughout the Federal government.”
The CDC’s five scenarios include one based on “a current best estimate about viral transmission and disease severity in the United States.” That scenario assumes a “basic reproduction number” of 2.5, meaning the average carrier can be expected to infect that number of people in a population with no immunity. It assumes an overall symptomatic case fatality rate (CFR) of 0.4 percent, roughly four times the estimated CFR for the seasonal flu. The CDC estimates that the CFR for COVID-19 falls to 0.05 percent among people younger than 50 and rises to 1.3 percent among people 65 and older. For people in the middle (ages 50–64), the estimated CFR is 0.2 percent.
That “best estimate” scenario also assumes that 35 percent of infections are asymptomatic, meaning the total number of infections is more than 50 percent larger than the number of symptomatic cases. It therefore implies that the IFR is between 0.2 percent and 0.3 percent. By contrast, the projections that the CDC made in March, which predicted that as many as 1.7 million Americans could die from COVID-19 without intervention, assumed an IFR of 0.8 percent. Around the same time, researchers at Imperial College produced a worst-case scenario in which 2.2 million Americans died, based on an IFR of 0.9 percent.
Such projections had a profound impact on policy makers in the United States and around the world. At the end of March, President Donald Trump, who has alternated between minimizing and exaggerating the threat posed by COVID-19, warned that the United States could see “up to 2.2 million deaths and maybe even beyond that” without aggressive control measures, including lockdowns.
One glaring problem with those worst-case scenarios was the counterfactual assumption that people would carry on as usual in the face of the pandemic—that they would not take voluntary precautions such as avoiding crowds, minimizing social contact, working from home, wearing masks, and paying extra attention to hygiene. The Imperial College projection was based on “the (unlikely) absence of any control measures or spontaneous changes in individual behaviour.” Similarly, the projection of as many as 2.2 million deaths in the United States cited by the White House was based on “no intervention”—not just no lockdowns, but no response of any kind.
Another problem with those projections, assuming that the CDC’s current “best estimate” is in the right ballpark, was that the IFRs they assumed were far too high. The difference between an IFR of 0.8 to 0.9 percent and an IFR of 0.2 to 0.3 percent, even in the completely unrealistic worst-case scenarios, is the difference between millions and hundreds of thousands of deaths—still a grim outcome, but not nearly as bad as the horrifying projections cited by politicians to justify the sweeping restrictions they imposed.
“The parameter values in each scenario will be updated and augmented over time, as we learn more about the epidemiology of COVID-19,” the CDC cautions. “New data on COVID-19 is available daily; information about its biological and epidemiological characteristics remain[s] limited, and uncertainty remains around nearly all parameter values.” But the CDC’s current best estimates are surely better grounded than the numbers it was using two months ago.
A recent review of 13 studies that calculated IFRs in various countries found a wide range of estimates, from 0.05 percent in Iceland to 1.3 percent in Northern Italy and among the passengers and crew of the Diamond Princess cruise ship. This month Stanford epidemiologist John Ioannidis, who has long been skeptical of high IFR estimates for COVID-19, looked specifically at published studies that sought to estimate the prevalence of infection by testing people for antibodies to the virus that causes the disease. He found that the IFRs implied by 12 studies ranged from 0.02 percent to 0.4 percent. My colleague Ron Bailey last week noted several recent antibody studies that implied considerably higher IFRs, ranging from 0.6 percent in Norway to more than 1 percent in Spain.
Methodological issues, including sample bias and the accuracy of the antibody tests, probably explain some of this variation. But it is also likely that actual IFRs vary from one place to another, both internationally and within countries. “It should be appreciated that IFR is not a fixed physical constant,” Ioannidis writes, “and it can vary substantially across locations, depending on the population structure, the case-mix of infected and deceased individuals and other, local factors.”
One important factor is the percentage of infections among people with serious preexisting medical conditions, who are especially likely to die from COVID-19. “The majority of deaths in most of the hard hit European countries have happened in nursing homes, and a large proportion of deaths in the US also seem to follow this pattern,” Ioannidis notes. “Locations with high burdens of nursing home deaths may have high IFR estimates, but the IFR would still be very low among non-elderly, non-debilitated people.”
That factor is one plausible explanation for the big difference between New York and Florida in both crude case fatality rates (reported deaths as a share of confirmed cases) and estimated IFRs. The current crude CFR for New York is nearly 8 percent, compared to 4.4 percent in Florida. Antibody tests suggest the IFR in New York is something like 0.6 percent, compared to 0.2 percent in the Miami area.
Given Florida’s high percentage of retirees, it was reasonable to expect that the state would see relatively high COVID-19 fatality rates. But Florida’s policy of separating elderly people with COVID-19 from other vulnerable people they might otherwise have infected seems to have saved many lives. New York, by contrast, had a policy of returning COVID-19 patients to nursing homes.
“Massive deaths of elderly individuals in nursing homes, nosocomial infections [contracted in hospitals], and overwhelmed hospitals may…explain the very high fatality seen in specific locations in Northern Italy and in New York and New Jersey,” Ioannidis says. “A very unfortunate decision of the governors in New York and New Jersey was to have COVID-19 patients sent to nursing homes. Moreover, some hospitals in New York City hotspots reached maximum capacity and perhaps could not offer optimal care. With large proportions of medical and paramedical personnel infected, it is possible that nosocomial infections increased the death toll.”
Ioannidis also notes that “New York City has an extremely busy, congested public transport system that may have exposed large segments of the population to high infectious load in close contact transmission and, thus, perhaps more severe disease.” More speculatively, he notes the possibility that New York happened to be hit by a “more aggressive” variety of the virus, a hypothesis that “needs further verification.”
If you focus on hard-hit areas such as New York and New Jersey, an IFR between 0.2 and 0.3 percent, as suggested by the CDC’s current best estimate, seems improbably low. “While most of these numbers are reasonable, the mortality rates shade far too low,” University of Washington biologist Carl Bergstrom told CNN. “Estimates of the numbers infected in places like NYC are way out of line with these estimates.”
But the CDC’s estimate looks more reasonable when compared to the results of antibody studies in Miami-Dade County, Santa Clara County, Los Angeles County, and Boise, Idaho—places that so far have had markedly different experiences with COVID-19. We need to consider the likelihood that these divergent results reflect not just methodological issues but actual differences in the epidemic’s impact—differences that can help inform the policies for dealing with it.[End of Reason article]
Although it is frequently compared in the media to the Spanish Flu of 1918, in global deaths per capita it is nowhere near, and more similar to the 1957 and 1968 flu pandemics, which few in the general population even knew about and never prompted talk of masks or lockkdowns.
In 1957 and 1968, the US experienced two pandemics which claimed more lives per capita, also of mostly elderly and immune compromised people, than the present declared coronavirus pandemic. The viruses involved in these pandemics also had higher case mortality rates than the present revised estimate for COVID.
During the 1957 and 1968 pandemics, life went on as normal for the vast majority of the population.
Critics of the present approach, such as epidemiologist Dr. Knut Wittkowski, argue that social distancing measures such as closing schools delays the process of herd immunity, and allows the virus time to mutate into more deadly strains, making social distancing wildly counter-productive.
When figures from the database Worldometer are compared, it can be seen that there is little correlation between per capita deaths in a country and lockdown status, or lack of it.
During the 1957 and 1968 pandemics, not only was “social distancing” not a topic. Just months after the close of the 1968 – 1969 flu season, people flocked to one of the largest mass gatherings in US history, Woodstock.
(Reuters) – Three children in New York have died from a rare inflammatory syndrome believed to be linked to the novel coronavirus, Governor Andrew Cuomo said on Saturday, a development that may augur a pandemic risk for the very young.
But one phenomenon which is fairly well understood by vaccine researchers is hyper-immune response. This has been seen in experiments with previous coronavirus vaccine attempts, none of which succeeded. Robert F. Kennedy Jr.’s Children’s Health Defense, a “pro-safe vaccination group,” writes:
Why are the world’s top vaccine promoters, like Paul Offit and Peter Hotez, frantically warning us about the unique and frightening dangers inherent in developing a coronavirus vaccine?
“Scientists first attempted to develop coronavirus vaccines after China’s 2002 SARS-CoV outbreak. Teams of US & foreign scientists vaccinated animals with the four most promising vaccines. At first, the experiment seemed successful as all the animals developed a robust antibody response to coronavirus. However, when the scientists exposed the vaccinated animals to the wild virus, the results were horrifying. Vaccinated animals suffered hyper-immune responses including inflammation throughout their bodies, especially in their lungs. Researchers had seen this same “enhanced immune response” during human testing of the failed RSV vaccine tests in the 1960s. Two children died.”
The newly identified syndrome appears to be the result of a child’s immune system’s going into overdrive after a COVID-19 infection. However, it’s still too soon to pin all of the cases on the coronavirus. Some patients have tested negative.
Adding to the connection between prior vaccination and vulnerability to COVID or more severe cases of it in children, Dr. Anthony Fauci and a Defense Department study agree that a phenomenon known as “virus interference” may wreak havoc on the immune system’s ability to respond to the COVID virus. Virus interference may take place as a result of prior flu shot vaccination.
“I must warn that there is a possibility of negative consequences where certain vaccines can actually enhance the negative effect of the infection,” he told lawmakers.”
As to be expected, rapidly deployed media “debunks” that seem aimed at steering the herd away from anything but mandatory vaccines say that virus interference has not been proven. But that does not mean there is no evidence for it. The same “debunks” seem peculiarly uninterested in calling for more extensive studies which would give us more information.
One of the most active “debunk” sites is Factcheck.org, a project of the Annenberg Foundation. In its “debunk” of the plain words of the Defense Department study, and never mentioning Fauci, the article “No Evidence That Flu Shot Increases Risk of COVID-19” quotes a Dr. Sharon Rikin as saying:
“In medicine, we are always weighing the risks and benefits of treatments. In this case, we know that the flu vaccine is safe and effective to reduce illness and death among children and adults every year,”
But Dr. Rikin’s credibility is easily impeached by the common knowledge that adverse side effects from flu shots are the number one source of pay-outs to persons injured by vaccines by the National Vaccine Injury Compensation Program. Many people would be surprised to know such a program even exists. Vaccine injuries may be rare, but they are common enough that a massive government program has been created to compensate for them.
As of October 2019, $4.2 Billion has been awarded from the program. It is worthy of note that the amounts awarded from the program have steadily increased, as the number of vaccinations required for schoolchildren has gone up.
Annual awards, National Vaccine Injury Program (source)
Yes it was un-American. Yes it was unprecedented. But we were told we had no choice. We had to “flatten the curve” of an exponentially growing threat, if Dr. Anthony Fauci were to be believed.
Now the curve is “flattened,” and the nation’s health care systems never came close to being overwhelmed, even in New York. In New York City, the hospital ship Mercy sat in New York Harbor, before sailing away with a grand total of 22 of its thousand beds being used.
Incredibly, the numbers warning of up to 2 million dead in the US turned out to be bogus, made up by disgraced scientist Neil Ferguson of Imperial College, who has now been called by colleagues one of the “most wrong” scientists in the world.
From early fears, based on limited data, of a 2% to possibly 6% mortality rate, which would be up to 60 times deadlier than the common flu, the prediction that the true mortality rate would follow a steady downward trend has proven correct.
A UPMC doctor on Thursday made a case the death rate for people infected with the new coronavirus may be as low as 0.25% — far lower than the mortality rates of 2-4% or even higher cited in the early days of the pandemic.
Dr. Donald Yealy based it partly on studies of levels of coronavirus antibodies detected in people in New York and California, and partly on COVID-19 deaths in the Pittsburgh region. The studies found that 5-20% of people had been exposed to the coronavirus, with many noticing only mild illness or none at all, he said.
“We’ve learned that way more people, far, far more people have actually been exposed to the infection without any knowledge of it. That makes the overall death rate much lower,” said Yealy, who is UPMC’s chair of emergency medicine. “Many people just didn’t feel sick at all and recovered without difficulty.”
Yealy went on to offer a hypothetical scenario of 3% of Allegheny County residents being exposed — a conservative number compared to the findings of the New York and California studies.
That would mean about 36,000 people in Allegheny have been exposed to the coronavirus. With 94 COVID-19 deaths in the county as of Thursday, it would mean 0.25 percent of people exposed to the coronavirus had died, he said.
“There is a big difference between 0.25% mortality and 7%,” Yealy said.
Yealy has been one of the main public voices of UPMC during the coronavirus pandemic. He spoke Thursday during a 40-minute online discussion with reporters.
Another speaker, Dr. Rachel Sackrowitz, the chief medical officer for UPMC’s intensive care units, said 234 COVID-19 patients have recovered and been discharged from UPMC hospitals. “This is very good news. It means people are getting better and we’re all on the right track together.”
Yealy said only 2% percent of the UPMC system’s 5,500 beds are occupied by COVID-19 patients and the number of new COVID-19 patients is declining.
He cited that figure in explaining UPMC’s plans to quickly increase its volume of the non-emergency surgeries that were largely banned to conserve beds and supplies for COVID-19 patients. The ban is now being eased as the volume of COVID-19 patients falls short of worst-case predictions.
Yealy said he can’t predict if there will be a second wave, but said “What I suspect is COVID-19 will be a part of our experience treating patients for an extended [period of] months to maybe years.”
Yealy was asked whether people should worry about COVID-19 more than the regular flu. He said people should be “worried differently,” pointing out that both take their heaviest toll on the elderly, especially nursing home residents, and people weakened by other medical conditions.
Yealy said he “would not think of it as more or less, just two different illnesses that share some features, but have some distinct differences.”
Sackrowitz said she expects COVID-19 will be part of the ongoing “disease burden” affecting Americans and, as with the flu, doctors will find treatments.
The scientist who authored the entire premise of the extraordinary and unprecedented crash of the world economy is called one of the “most wrong” scientists in the world, by other scientists. Imperial College’s Neil Ferguson’s Report 9 is the work cited by Dr. Anthony Fauci as making it necessary to lockdown the nation, and to start “contact tracing.” One of Ferguson’s early wrong models led to a horrific and unnecessary open slaughter of millions of sheep.
Neil Ferguson is the British academic who created the infamous Imperial College model that warned Boris Johnson that, without an immediate lockdown, the coronavirus would cause 500,000 deaths and swamp the National Health Service.
Johnson’s government promptly abandoned its Sweden-like “social distancing” approach, and Ferguson’s model also influenced the U.S. to make lockdown moves with its shocking prediction of over two million Americans dead.
Johan Giesecke, the former chief scientist for the European Center for Disease Control and Prevention, has called Ferguson’s model “the most influential scientific paper” in memory. He also says it was, sadly, “one of the most wrong.”
With all of his influence, it’s not surprising British media are making a great deal about Ferguson being forced to resign from the government’s virus advisory board yesterday after revelations he had violated lockdown rules he had championed in order to conduct an affair with a married woman. Ferguson admits he made an “error of judgement and took the wrong course of action.”
Ferguson’s hypocritical violation of his beloved lockdown was the least of his errors in judgment. His incompetence and insistence on doomsday models is far worse.
Elon Musk calls Ferguson an “utter tool” who does “absurdly fake science.” Jay Schnitzer, an expert in vascular biology and a former scientific direct of the Sidney Kimmel Cancer Center in San Diego, tells me: “I’m normally reluctant to say this about a scientist, but he dances on the edge of being a publicity-seeking charlatan.”
Indeed, Ferguson’s Imperial College model has been proven wildly inaccurate. To cite just one example, it saw Sweden paying a huge price for no lockdown, with 40,000 COVID deaths by May 1, and 100,000 by June. Sweden now has 2,854 deaths and peaked two weeks ago. As Fraser Nelson, editor of Britain’s Spectator, notes: “Imperial College’s model is wrong by an order of magnitude.”
Indeed, Ferguson has been wrong so often that some of his fellow modelers call him “The Master of Disaster.”
Ferguson was behind the disputed research that sparked the mass culling of eleven million sheep and cattle during the 2001 outbreak of foot-and-mouth disease. Charlotte Reid, a farmer’s neighbor, recalls: “I remember that appalling time. Sheep were left starving in fields near us. Then came the open air slaughter. The poor animals were panic stricken. It was one of the worst things I’ve witnessed. And all based on a model — if’s but’s and maybe’s.”
In 2002, Ferguson predicted that, by 2080, up to 150,000 people could die from exposure to BSE (mad cow disease) in beef. In the U.K., there were only 177 deaths from BSE.
In 2005, Ferguson predicted that up to 150 million people could be killed from bird flu. In the end, only 282 people died worldwide from the disease between 2003 and 2009.
In 2009, a government estimate, based on Ferguson’s advice, said a “reasonable worst-case scenario” was that the swine flu would lead to 65,000 British deaths. In the end, swine flu killed 457 people in the U.K.
Last March, Ferguson admitted that his Imperial College model of the COVID-19 disease was based on undocumented, 13-year-old computer code that was intended to be used for a feared influenza pandemic, rather than a coronavirus. Ferguson declined to release his original code so other scientists could check his results. He only released a heavily revised set of code last week, after a six-week delay.
So the real scandal is: Why did anyone ever listen to this guy?