“Global Coronavirus “Plandemic” 2020 (NEWS UPDATES)

Publisher’s Note: Consider this page a “holding tank” for the latest relevant news on the “global coronavirus plandemic.” Folks are waking up. Good.

BOOM: The Hill’s RISING co-hosts – Sagar and Krystal – discover the Amazon “Virtual News Release.” Fake news, slickly packaged, and GIVEN to TeeVee news shows by Amazon’s PR department for programming the public. Hilariously grotesque.

BOOM: The Bill and Melinda Gates Foundation invested heavily in Apple, Amazon and Google last quarter, according to a May 26, 2020 Business Insider report. Like, $450 million in total investments.

BOOM: The CDC’s New ‘Best Estimate’ Implies a COVID-19 Infection Fatality Rate Below 0.3%

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.”    TOP ARTICLES2/5FEDERAL JUDGE: FLORIDA CAN’T BLOCK VOTING RIGHTS BECAUSE OF INABILITY TO PAY COURT FINES

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.

BOOM: The Center for Disease Control is conflating viral and antibody tests, according to a May 21st article the Atlantic. As we’ve suggested here for weeks, COVID “testing” is nonsense (literally non sense), as no novel coronavirus or disease uniquely identified as “COVID-19” has actually been identified. Moving forward, testing will be the rationale for opening up states from Lockdown, reviving businesses, reassembling schools and communities – this provides vital clues into a larger story.

CLICK:

The Centers for Disease Control and Prevention is conflating the results of two different types of coronavirus tests, distorting several important metrics and providing the country with an inaccurate picture of the state of the pandemic. We’ve learned that the CDC is making, at best, a debilitating mistake: combining test results that diagnose current coronavirus infections with test results that measure whether someone has ever had the virus. The upshot is that the government’s disease-fighting agency is overstating the country’s ability to test people who are sick with COVID-19. The agency confirmed to The Atlantic on Wednesday that it is mixing the results of viral and antibody tests, even though the two tests reveal different information and are used for different reasons.

This is not merely a technical error. States have set quantitative guidelines for reopening their economies based on these flawed data points.

Several states—including Pennsylvania, the site of one of the country’s largest outbreaks, as well as Texas, Georgia, and Vermont—are blending the data in the same way. Virginia likewise mixed viral and antibody test results until last week, but it reversed course and the governor apologized for the practice after it was covered by the Richmond Times-Dispatch and The Atlantic. Maine similarly separated its data on Wednesday; Vermont authorities claimed they didn’t even know they were doing this. 

The widespread use of the practice means that it remains difficult to know exactly how much the country’s ability to test people who are actively sick with COVID-19 has improved.

Read: There’s one big reason the U.S. economy can’t reopen

“You’ve got to be kidding me,” Ashish Jha, the K. T. Li Professor of Global Health at Harvard and the director of the Harvard Global Health Institute, told us when we described what the CDC was doing. “How could the CDC make that mistake? This is a mess.”

Viral tests, taken by nose swab or saliva sample, look for direct evidence of a coronavirus infection. They are considered the gold standard for diagnosing someone with COVID-19, the disease caused by the virus: State governments consider a positive viral test to be the only way to confirm a case of COVID-19. Antibody tests, by contrast, use blood samples to look for biological signals that a person has been exposed to the virus in the past.

A negative test result means something different for each test. If somebody tests negative on a viral test, a doctor can be relatively confident that they are not sick right now; if somebody tests negative on an antibody test, they have probably never been infected with or exposed to the coronavirus. (Or they may have been given a false result—antibody tests are notoriously less accurate on an individual level than viral tests.) The problem is that the CDC is clumping negative results from both tests together in its public reporting.

Read: Pools will test the limits of social distancing

Mixing the two tests makes it much harder to understand the meaning of positive tests, and it clouds important information about the U.S. response to the pandemic, Jha said. “The viral testing is to understand how many people are getting infected, while antibody testing is like looking in the rearview mirror. The two tests are totally different signals,” he told us. By combining the two types of results, the CDC has made them both “uninterpretable,” he said.

The public-radio station WLRN, in Miami, first reported that the CDC was mixing viral and antibody test results. Pennsylvania’s and Maine’s decisions to mix the two tests have not been previously reported.

Kristen Nordlund, a spokesperson for the CDC, told us that the inclusion of antibody data in Florida is one reason the CDC has reported hundreds of thousands more tests in Florida than the state government has. The agency hopes to separate the viral and antibody test results in the next few weeks, she said in an email.

But until the agency does so, its results will be suspect and difficult to interpret, says William Hanage, an epidemiology professor at Harvard. In addition to misleading the public about the state of affairs, the intermingling “makes the lives of actual epidemiologists tremendously more difficult.”

“Combining a test that is designed to detect current infection with a test that detects infection at some point in the past is just really confusing and muddies the water,” Hanage told us.

Read: Why the coronavirus is so confusing

The CDC stopped publishing anything resembling a complete database of daily test results on February 29. When it resumed publishing test data last week, a page of its website explaining its new COVID Data Tracker said that only viral tests were included in its figures. “These data represent only viral tests. Antibody tests are not currently captured in these data,” the page said as recently as May 18.

Yesterday, that language was changed. All reference to disaggregating the two different types of tests disappeared. “These data are compiled from a number of sources,” the new version read. The text strongly implied that both types of tests were included in the count, but did not explicitly say so.

The CDC’s data have also become more favorable over the past several days. On Monday, a page on the agency’s website reported that 10.2 million viral tests had been conducted nationwide since the pandemic began, with 15 percent of them—or about 1.5 million—coming back positive. But yesterday, after the CDC changed its terms, it said on the same page that 10.8 million tests of any type had been conducted nationwide. Yet its positive rate had dropped by a percent. On the same day it expanded its terms, the CDC added 630,205 new tests, but it added only 52,429 positive results.

This is what concerns Jha. Because antibody tests are meant to be used on the general population, not just symptomatic people, they will, in most cases, have a lower percent-positive rate than viral tests. So blending viral and antibody tests “will drive down your positive rate in a very dramatic way,” he said.

The absence of clear national guidelines has led to widespread confusion about how testing data should be reported. Pennsylvania reports negative viral and antibody tests in the same metric, a state spokesperson confirmed to us on Wednesday. The state has one of the country’s worst outbreaks, with more than 67,000 positive cases. But it has also slowly improved its testing performance, testing about 8,000 people in a day. Yet right now it is impossible to know how to interpret any of its accumulated results.

Read: Should you get an antibody test?

Texas, where the rate of new COVID-19 infections has stubbornly refused to fall, is one of the most worrying states (along with Georgia). The Texas Observer first reportedlast week that the state was lumping its viral and antibody results together. On Tuesday, Governor Greg Abbott denied that the state was blending the results, but the Dallas Observer reports that it is still doing so.

While the number of tests per day has increased in Texas, climbing to more than 20,000, the combined results mean that the testing data are essentially uninterpretable. It is impossible to know the true percentage of positive viral tests in Texas. It is impossible to know how many of the 718,000 negative results were not meant to diagnose a sick person. The state did not return a request for comment, nor has it produced data describing its antibody or viral results separately. (Some states, following guidelines from the Council of State and Territorial Epidemiologists, report antibody-test positives as “probable” COVID-19 cases without including them in their confirmed totals.)

Georgia is in a similar situation. It has also seen its COVID-19 infections plateau amid a surge in testing. Like Texas, it reported more than 20,000 new results on Wednesday, the majority of them negative. But because, according to The Macon Telegraph, it is also blending its viral and antibody results together, its true percent-positive rate is impossible to know. (The governor’s office did not return a request for comment.)

These results damage the public’s ability to understand what is happening in any one state. On a national scale, they call the strength of America’s response to the coronavirus into question. The number of tests conducted nationwide each day has more than doubled in the past month, rising from about 147,000 a month ago to more than 413,000 on Wednesday, according to the COVID Tracking Project at The Atlantic, which compiles data reported by state and territorial governments. In the past week, the daily number of tests has grown by about 90,000.

At the same time, the portion of tests coming back positive has plummeted, from a seven-day average of 10 percent at the month’s start to 6 percent on Wednesday.

“The numbers have outstripped what I was expecting,” Jha said. “My sense is people are really surprised that we’ve moved as much as we have in such a short time period. I think we all expected a move and we all expected improvement, but the pace and size of that improvement has been a big surprise.”

The intermingling of viral and antibody tests suggests that some of those gains might be illusory. If even a third of the country’s gain in testing has come by expanding antibody tests, not viral tests, then its ability to detect an outbreak is much smaller than it seems. There is no way to ascertain how much of the recent increase in testing is from antibody tests until the most populous states in the country—among them Texas, Georgia, and Pennsylvania—show their residents everything in the data.

BOOM: Dead Coronavirus Particles Muddy the Outcome of Test Results
By Yoojung Lee and Jason GaleApril 30, 2020, 4:01 AM EDT 

Nucleic acid tests can’t distinguish live from dead virus Infectiousness depends on duration of viable virus shedding

[Subscription only on Bloomberg]——https://www.livescience.com/coronavirus-reinfections-were-false-positives.html

More than 260 COVID-19 patients in South Korea tested positive for the coronavirus after having recovered, raising alarm that the virus might be capable of “reactivating” or infecting people more than once. But infectious disease experts now say both are unlikely.
Rather, the method used to detect the coronavirus, called polymerase chain reaction (PCR), cannot distinguish between genetic material (RNA or DNA) from infectious virus and the “dead” virus fragments that can linger in the body long after a person recovers, Dr. Oh Myoung-don, a Seoul National University Hospital doctor, said at a news briefing Thursday (April 30), according to The Korea Herald. 
These tests “are very simple,” said Carol Shoshkes Reiss, a professor of Biology and Neural Science at New York University, who was not involved in the testing. “Although somebody can recover and no longer be infectious, they may still have these little fragments of [inactive] viral RNA which turn out positive on those tests.”
That’s because once the virus has been vanquished, there is “all this garbage of broken-down cells that needs to be cleaned up,” Reiss told Live Science, referring to the cellular corpses that were killed by the virus. Within that garbage are the fragmented remains of now non-infectious viral particles. 
To determine whether or not someone is harboring infectious virus or has been reinfected with the virus, a completely different type of test would be needed, one that is not typically performed, Reiss said. Instead of testing the virus as it is, lab technicians would have to culture it, or place that virus in a lab dish under ideal conditions and see if it was capable of growing. …——http://m.koreaherald.com/view.php?ud=20200429000724
… He went on to explain that in PCR tests, or polymerase chain reaction tests, used for COVID-19 diagnosis, genetic materials of the virus amplify during testing, whether it is from a live virus or just from fragments of dead virus cells that can take months to clear from recovered patients.
The PCR tests cannot distinguish whether the virus is alive or dead, he added, and this can lead to false positives. 
“PCR testing that amplifies genetics of the virus is used in Korea to test COVID-19, and relapse cases are due to technical limits of the PCR testing.” …