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8/29/20, Up to 90 percent of “positive” Covid tests are actually negative, NY Times review finds. Most carry insignificant amounts of virus and aren’t likely to be contagious.
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Aug. 17, 2020, “What if ‘Herd Immunity’ Is Closer Than Scientists Thought?" NY Times, by Apoorva Mandavilli, print edition, Section D, page 8
“Researchers are trying to figure out how many people in a community must be immune before the coronavirus fades.”
To achieve so-called herd immunity-the point at which the virus can no longer spread widely because there are not enough vulnerable humans-scientists have suggested that perhaps 70 percent of a given population must be immune, through vaccination or because they survived the infection.
In interviews with The New York Times, more than a dozen scientists said that the threshold is likely to be much lower: just 50 percent, perhaps even less. If that’s true, then it may be possible to turn back the coronavirus more quickly than once thought….
In parts of New York, London and Mumbai, for example, it is not inconceivable that there is already substantial immunity to the coronavirus, scientists said.
[Chart: As of 9/1/20, NYC 5 boros. Covid deaths, peaked in April, flat for 4 months. “Confirmed deaths,” 80%-89% of deaths had co-morbidities]
“I’m quite prepared to believe that there are pockets in New York City and London which have substantial immunity,”
said Bill Hanage, an epidemiologist at the Harvard T.H. Chan School of
Public Health. “What happens this winter will reflect that.”…
The initial calculations for the herd immunity threshold assumed that each community member had the same susceptibility to the virus and mixed randomly with everyone else in the community.
“That doesn’t happen in real life,” said Dr. Saad Omer, director of the Yale Institute for Global Health. “Herd immunity could vary from group to group, and subpopulation to subpopulation,” and even by postal codes, he said.
For example, a neighborhood of older people may have little contact with others but succumb to the virus quickly when they encounter it, whereas teenagers may bequeath the virus to dozens of contacts and yet stay healthy themselves. The virus moves slowly in suburban and rural areas, where people live far apart, but zips through cities and households thick with people.
Once such real-world variations in density and demographics are accounted for, the [percentage] estimates for herd immunity fall. Some researchers even suggested the figure may be in the range of 10 to 20 percent, but they were in the minority.
Assuming the virus ferrets out the most outgoing and most susceptible in the first wave, immunity following a wave of infection is distributed more efficiently than with a vaccination campaign that seeks to protect everyone, said Tom Britton, a mathematician at Stockholm University.
His model puts the threshold for herd immunity at 43 percent— that is, the virus cannot hang on in a community after that percentage of residents has been infected and recovered….
The virus may still flare up here and
there, even if its overall spread is stymied. It’s also unclear how long
someone who has recovered may be immune, and for how long.
Virus-Resistant Communities?…
Is it possible that some of these communities have herd immunity? In some clinics, up to 80 percent of people tested had antibodies to the virus. The highest prevalence was found among teenage boys.
But people at clinics are more likely to be showing symptoms and therefore more likely to be infected, said Wan Yang, an epidemiologist at Columbia University’s Mailman School of Public Health in New York. Random household surveys would probably find lower rates–but still well above the 21 percent average reported for New York City, she said.
Is it possible that some of these communities have herd immunity? In some clinics, up to 80 percent of people tested had antibodies to the virus. The highest prevalence was found among teenage boys.
But people at clinics are more likely to be showing symptoms and therefore more likely to be infected, said Wan Yang, an epidemiologist at Columbia University’s Mailman School of Public Health in New York. Random household surveys would probably find lower rates–but still well above the 21 percent average reported for New York City, she said.
Researchers in Mumbai conducted just such a random household survey, knocking on every fourth door — or, if it was locked, the fifth — and took blood for antibody testing. They found a startling disparity between the city’s poorest neighborhoods and its more affluent enclaves. Between 51 and 58 percent of residents in poor areas had antibodies, versus 11 to 17 percent elsewhere in the city. The lowest-income residents are packed tightly together, share toilets, and have little access to masks. “These factors contributed to a silent infection spread,” said Dr. Jayanthi Shastri, a microbiologist at Kasturba Hospital in Mumbai who led the work.
Most researchers are wary of concluding
that the hardest-hit neighborhoods of Brooklyn, or even those in
blighted areas of Mumbai, have reached herd immunity or will be spared
future outbreaks.
But models likZAe Dr. Britton’s hint that it’s not impossible. Other researchers have suggested...that herd immunity can be achieved at rates of immunity as low as 10 or 20 percent–and that entire countries may already have achieved that goal….
.......
.......
Imperfect Immunity
But what about immunity at levels lower than those needed for herd immunity?
“Definitely the disease would not spread as well if it gets back into New York,” said Joel Miller, a mathematical modeler at La Trobe University in Australia. “The same level of behavior change will have more effect on the disease now than it did four months ago.”
Thinking of a city or country as composed of subgroups, demarcated by age, race and level of social activity, might also help governments protect those with the least immunity.
That perspective also might help put a renewed focus on groups who require the higher levels of immunity, because of greater exposure levels and other inequities, including Black and Latino residents, said Dr. Manoj Jain, an infectious disease expert at Emory University. “That’s where this info is very useful,” he said.
The models also suggest a vaccination strategy: Rather than uniformly vaccinate all groups, governments could identify and immunize those most likely to be exposed in “superspreader” events.
Vaccination schemes for other pathogens have successfully exploited this approach. For example, when children were given the pneumococcal vaccine in the early 2000s, rates of bacterial pneumonia in the elderly rapidly dropped because of a “herd effect.”…
Back in Brooklyn, fewer than 1 percent of people tested at neighborhood clinics over the past eight weeks were infected with the virus. But there are still handfuls of cases.”…
Back in Brooklyn, fewer than 1 percent of people tested at neighborhood clinics over the past eight weeks were infected with the virus. But there are still handfuls of cases.”…
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“A version of this article appears in print on Aug. 18, 2020, Section D, Page 8 of the New York edition with the headline: Herd Immunity Is Hard to Decipher.”
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Added: “A disastrously flawed Coronavirus model changed government policy overnight and shut down the world for months:”
May 15, 2020, “The Lie That Launched a Thousand Lockdowns,” Dan O’Donnell, WISN1130, Milwaukee, Wisc.
“One can pinpoint the exact moment when life in the United States changed, possibly forever. It was the night of March 11th, 2020 and the NBA suspended its season. At about the same time, arguably the world’s most famous actor made a shocking announcement.
Within days, states would begin to issue orders restricting the number of people who could gather together. Shortly after that, they began closing schools.
[Image: 3/16/20, Trump announces 30 day virus plan, CNN, 3/17/20. UK study released on 3/16/20 predicting more than a million US deaths has not been published in a peer-reviewed journal.] “On the guidelines of the task force, the new modeling conducted by Dr. Birx, and our consultation with governors, we’ve made the decision to further toughen the guidelines and blunt the infection now,” President Trump announced. “We’d much rather be ahead of the curve than behind it, and that’s what we are. Therefore, my administration is recommending that all Americans, including the young and healthy, work to engage in schooling from home when possible. Avoid gathering in groups of more than 10 people. Avoid discretionary travel. And avoid eating and drinking at bars, restaurants, and public food courts.”
What, precisely did President Trump mean when he referenced “new modeling” conducted by Dr. Deborah Birx, the White House’s Coronavirus Response Coordinator?
She explained later in that same press conference on March 16th.
“We have been working on models, day and night, around the globe, to really predict, because some countries are in a very early stage, like the United States,” she said. “We’ve been working with groups in the United Kingdom.
“So, we had new information coming out from a model, and what had the biggest impact in the model is social distancing, small groups, not going in public in large groups.”
That model was from the Imperial College and its chief researcher Neil Ferguson. It is hard to stress enough just how influential it was in both the U.S. and U.K., where it turned lockdown skeptics like Prime Minister Johnson and President Trump into believers overnight.
And almost overnight, states across America began implementing total shutdowns of restaurants, bars, retail stores, and every business they deemed “non-essential.”
Unfortunately, the Imperial College model on which they all relied was staggeringly, disastrously wrong.
Almost immediately after it was released, Ferguson’s model came under fire from Oxford University epidemiologist Sunetra Gupta, whose own modeling suggested that Coronavirus had been spreading in the U.K. far earlier than anyone suspected and that in most cases, it was either asymptomatic or so mild that it was confused with the common cold.
“The Oxford study is based on a what is known as a ‘susceptibility-infected-recovered model’ of Covid-19, built up from case and death reports from the UK and Italy,” The Financial Times reported. “The researchers made what they regard as the most plausible assumptions about the behaviour of the virus.
“The modelling brings back into focus ‘herd immunity’, the idea that the virus will stop spreading when enough people have become resistant to it because they have already been infected.
“If the findings are confirmed by testing, then the current restrictions could be removed much sooner than ministers have indicated.”
In other words, the [Neil] Ferguson model was dramatically flawed.
“I am surprised that there has been such unqualified acceptance of the Imperial model,” Gupta said.
Ferguson himself, it seems, agreed with this conclusion, because as soon as Gupta’s research was released, he walked back his doomsday predictions.
“I should admit, we’ve always been sensitive in the analysis in the modeling to a variety of levels or values to those quantities [of people who had the virus but didn’t know it],” Ferguson told members of Parliament.
“What we’ve been seeing, though, in Europe in the last week or two is a rate of growth of the epidemic which was faster than we expected from early data in China. And so we are revising our quotes, our central best estimate of the reproduction…something more, a little bit above of the order of three or a little bit above rather than about 2.5.
“The current values are still within the wide range of values which…we should have been looking at previously.”
In other words, he was wrong. Spectacularly wrong. His revised projection indicated that far fewer people than initially predicted would die, and most of those who would were already so sick with underlying health problems that they would have died within a year anyway even if they never contracted Coronavirus.
On March 26th, Dr. Birx relayed this sad reality.
“I’m sure many of you saw the recent report out of the UK about them adjusting completely their needs,” she said. “This is really quite important. If you remember, that was the report that said there would be 500,000 deaths in the UK and 2.2 million deaths in the United States. They’ve adjusted that number in the UK to 20,000. So, half a million to 20,000. We’re looking into this in great detail to understand that adjustment.”
This was the first inkling Dr. Birx or any of the government’s top disease specialists had that the models on which they had relied were off by orders of magnitude. [But knowing this, one day later, March 27, Trump still signed a $2.2 trillion virus bailout bill which had been based on the now grossly inaccurate prediction of 1-2 million US deaths]
“We are about five times the size of Italy,” she [Birx] added. “So if we were Italy and you did all those divisions, Italy should have close to 400,000 deaths. They’re not close to achieving that. So these are the kinds of things we’re trying to understand. Models are models. We’re adapting now to the — there’s enough data now of the real experience with the Coronavirus on the ground to really make these predictions much more sound [in the future].”
In addition, that model predicted that since Sweden did not follow the lead of the U.S. and U.K. in immediately locking down, it would have 40,000 Coronavirus deaths by May 1st. It actually recorded about 2,800.
Why was Ferguson’s modeling so flawed? He admitted that he based it on computer coding developed in 2007 to model influenza pandemics rather than Coronavirus outbreaks like SARS in 2002 or COVID-19 now.
Instead of immediately releasing that code so that it could be peer-reviewed, though, he kept it hidden for six weeks and even then only released a heavily edited version.
Sadly, this was just the latest in a long line of catastrophic mistakes from Ferguson. As National Review noted:
“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.”…
[Ed. note: In 2011 a UK peer reviewed study found that slaughtering millions of healthy animals hadn’t been necessary. But, the
Queen was so impressed with Ferguson and his 2001 computer models that
called for slaughter of millions of healthy animals that in 2002 she awarded him with an OBE, Order of the British Empire. Image, 4/12/2020, “The full horrifying scale of the 2001 foot and mouth outbreak told by the Welsh farmers in the middle of it,“ walesonline.co.uk]
(continuing): “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 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.
So frequently and so epically did Ferguson overestimate death tolls that his colleagues mockingly call him “The Master of Disaster.”
In late March [2020], Dr. Birx recognized what British scientists did years ago: That Neil Ferguson was a hack. America needed its own modeling.
She turned to the [Bill Gates funded] University of Washington’s Institute for Health Metrics and Evaluation (IHME), which presented a model showing numbers similar to Ferguson’s: If no lockdowns were implemented, 1.5 to 2.2 million Americans would die.
If instead states kept their shelter-in-place orders going [indefinitely], though, only 240,000 people would die.
The Imperial College model was fatally flawed, but this new model was far more accurate. President Trump accepted it and on March 29th announced that “15 Days to Flatten the Curve” would be extended to the end of April.
“What we’ve been seeing, though, in Europe in the last week or two is a rate of growth of the epidemic which was faster than we expected from early data in China. And so we are revising our quotes, our central best estimate of the reproduction…something more, a little bit above of the order of three or a little bit above rather than about 2.5.
“The current values are still within the wide range of values which…we should have been looking at previously.”
In other words, he was wrong. Spectacularly wrong. His revised projection indicated that far fewer people than initially predicted would die, and most of those who would were already so sick with underlying health problems that they would have died within a year anyway even if they never contracted Coronavirus.
On March 26th, Dr. Birx relayed this sad reality.
“I’m sure many of you saw the recent report out of the UK about them adjusting completely their needs,” she said. “This is really quite important. If you remember, that was the report that said there would be 500,000 deaths in the UK and 2.2 million deaths in the United States. They’ve adjusted that number in the UK to 20,000. So, half a million to 20,000. We’re looking into this in great detail to understand that adjustment.”
This was the first inkling Dr. Birx or any of the government’s top disease specialists had that the models on which they had relied were off by orders of magnitude. [But knowing this, one day later, March 27, Trump still signed a $2.2 trillion virus bailout bill which had been based on the now grossly inaccurate prediction of 1-2 million US deaths]
“We are about five times the size of Italy,” she [Birx] added. “So if we were Italy and you did all those divisions, Italy should have close to 400,000 deaths. They’re not close to achieving that. So these are the kinds of things we’re trying to understand. Models are models. We’re adapting now to the — there’s enough data now of the real experience with the Coronavirus on the ground to really make these predictions much more sound [in the future].”
In addition, that model predicted that since Sweden did not follow the lead of the U.S. and U.K. in immediately locking down, it would have 40,000 Coronavirus deaths by May 1st. It actually recorded about 2,800.
Why was Ferguson’s modeling so flawed? He admitted that he based it on computer coding developed in 2007 to model influenza pandemics rather than Coronavirus outbreaks like SARS in 2002 or COVID-19 now.
Instead of immediately releasing that code so that it could be peer-reviewed, though, he kept it hidden for six weeks and even then only released a heavily edited version.
Sadly, this was just the latest in a long line of catastrophic mistakes from Ferguson. As National Review noted:
“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.”…
[Ed. note: In 2011 a UK peer reviewed study found that slaughtering millions of healthy animals hadn’t been necessary. But, the
Queen was so impressed with Ferguson and his 2001 computer models that
called for slaughter of millions of healthy animals that in 2002 she awarded him with an OBE, Order of the British Empire. Image, 4/12/2020, “The full horrifying scale of the 2001 foot and mouth outbreak told by the Welsh farmers in the middle of it,“ walesonline.co.uk] (continuing): “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 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.
So frequently and so epically did Ferguson overestimate death tolls that his colleagues mockingly call him “The Master of Disaster.”
In late March [2020], Dr. Birx recognized what British scientists did years ago: That Neil Ferguson was a hack. America needed its own modeling.
She turned to the [Bill Gates funded] University of Washington’s Institute for Health Metrics and Evaluation (IHME), which presented a model showing numbers similar to Ferguson’s: If no lockdowns were implemented, 1.5 to 2.2 million Americans would die.
If instead states kept their shelter-in-place orders going [indefinitely], though, only 240,000 people would die.
The Imperial College model was fatally flawed, but this new model was far more accurate. President Trump accepted it and on March 29th announced that “15 Days to Flatten the Curve” would be extended to the end of April.
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