Thursday, June 27, 2024

UNITED STATES RULES AGAINST JAY BATTACHARYA

To me, Jay Battacharya is one of the biggest heroes to have emerged from the Pandemic. And he is so because of a very simple idea.

In the early days of the COVID epidemic (I'm talking in February, 2020 to April 2020) while the media was lying that COVID killed 3 and 4 percent of people who had it, Jay Battacharya quietly did the research to provide a real denominator for the numerator.

You see,  you can only claim COVID was killing 3% or 4% of people IF YOU HIDE THE NUMBER OF PEOPLE WHO ACTUALLY HAD CONTRACTED COVID AND SURVIVED.

And Jay Battacharya, a Professor of Epidemiolgy knew that, so he went out and got the number.

Read the following.

This is the man the Supreme Court said needed to be censored by the Government.

We don't really have a Constitution anymore, people.

Prior to spring 2020, Jay Bhattacharya was a well-respected but little-known epidemiologist and Stanford Medical School professor. But when the COVID pandemic broke out that March, Dr. Bhattacharya was thrust into a leadership role as coauthor of the groundbreaking Santa Clara Study, one of the first comprehensive looks at how the disease spread and impacted populations, and as one of the principals behind the Great Barrington Declaration, one of the first public declarations questioning the lockdown policies then being instituted worldwide. His public interrogation of these policies made him a target of public health officials in the US and abroad—including Dr. Anthony Fauci of the CDC and Dr. Francis Collins at the National Institutes of Health in Washington, DC—and placed him in a media spotlight. In this interview, Dr. Bhattacharya reflects on those battles, what we learned, and how we might better manage future pandemics.

COVID-19 antibody seroprevalence in Santa Clara County, California

 

Abstract

Background: Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

Methods: On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

Results: The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

Conclusion: The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

In a study published Friday, the researchers, many of whom hailed from Stanford University, noted that the results suggest that Covid-19 could be far more widespread than the official counts suggest.

Specifically, they estimate that between 2.5% and 4.2% of people in Santa Clara County may have antibodies. (The range is a result of different models used to extrapolate the test results to a representative population.)

“These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50 (to) 85-fold more than the number of confirmed cases,” the authors wrote.

Antibody tests look for signs that a patient’s immune system has had a response after being infected with the virus. Such tests are by no means perfect indicators that a person has truly been exposed, and studies show that there are varying levels of quality. Some of the tests are providing false reassurance, while others are offering false answers. As scientists have stressed, having a positive antibody result does not mean the person is immune to the virus. 

But such tests can be helpful to researchers looking to get a more accurate sense of how widespread the virus is. 

TRANSCRIPT ONLY

Dr. Bhattacharya returns to discuss the results of a study testing for COVID-19 in Santa Clara County, California, and one currently underway in partnership with Major League Baseball. We also discuss some signs of hope and specifics about how the economy can be restarted safely and efficiently.

Tuesday, April 21, 2020 

Peter Robinson: A month ago I interviewed my friend, Dr. Jay Bhattacharya, a Professor of Medicine here at Stanford who holds both an MD and a Doctorate in Economics. That interview went viral. More than a million people have viewed it on the internet, clips appeared on news shows of every kind, and Jay tells me that even his mother down in Los Angeles called to say she had seen it. In that interview you may recall, in that interview, Dr. Bhattacharya said that he would be testing on COVID-19 here in Santa Clara county and back with us now to talk about the results, Dr. Jay Bhattacharya. Jay, thanks for making the time and welcome everybody to this special plague time addition of Uncommon Knowledge with Peter Robinson.

Jay Bhattacharya: Thanks, Peter. Nice to be here.

Peter Robinson: Jay, the results. I'm quoting from your study which is available today through :. Have I got that right? All right. It's available through: and I'm quoting. Quote "the population-weighted prevalence of antibodies" "among those who tested here in Santa Clara County, "was 2.81%. "This implies that the infection is much more widespread "than indicated by the number of confirmed cases." Close quote. And I read this study carefully for the bit that a layman had the best chance of understanding. You've got to explain this to us, Jay. What briefly the results and then tell us how you conducted this study.

Jay Bhattacharya: Why don't we start with that first just so everybody has the idea of what we did. We drew a sample of people from Santa Clara county. Basically using a Facebook targeted ad strategy. So if you got a Facebook ad from us we were basically inviting you to come do a quick finger-prick blood test to see if you have antibodies present. We looked at basically 3,200 people in this drive-through testing facilities that we set up on the fly.

Peter Robinson: We're recording this on Friday. This is earlier this week or late last week?

Jay Bhattacharya: About two weeks ago I think.

Peter Robinson: Two weeks ago, all right.

Jay Bhattacharya: And so yeah, they took the finger prick, and then we just looked to see if the finger prick test showed evidence of antibodies to COVID-19. Now, why is that important? Because those antibodies imply very strongly that you had COVID-19 previously. Some sort of infection with SARS-CoV-2 virus which is the technical way to put it. Now 2.8, actually the numbers run, but depending on your assumptions that we've made, somewhere between 2.8 and 4%, which doesn't sound like a lot. That means somewhere between 96 and 98% of people haven't got--

Peter Robinson: Sorry, I have to ask laymen questions here. That sample of 3,000 and some odd people, you designed the Facebook ads to make that sample as random or as representative of the entire county as possible? Is that, you're trying to get a result that implies something for the larger population, but it's the larger population of what? Of the county?

Jay Bhattacharya: County, right. Although that strategy worked very well to some extent, we also had to make some adjustments because a lot of the richer parts of the county, they respond to the ads in higher rate than the poor parts of the county, but we know how many people live in each of these rich and poor parts of town. So, anyone who showed up from a poor part of the county, we counted essentially more than people who showed up from poorer county so that when we did the final estimate it represents the entire county, rich and poor alike at the right proportions as opposed to over counting the rich. So then basically with that number, with that procedure, we figured out about somewhere between 2.8 and 4% of Santa Clara County has had evidence of COVID infection. So what does that mean? First thing, right around the time when we were doing this study, there had been about 1,000 cases of COVID found of COVID infection. Active SARS-CoV-2 infection found within the county.

Peter Robinson: Right.

Jay Bhattacharya: So there's about two million people in the county. If 4% have it, evidence of infection, that means that there's about 85 times more people who've had it per person that actually identified having it.

Peter Robinson: That's the critical finding.

Jay Bhattacharya: Yeah and if it's on the low end it'd be 2.8 it'd be 50 times. So for every single person that the healthcare system in Santa Clara County has identified as having the virus actively in them, there are 50 people out there who had it, and that never showed up with a test of positive test. The COVID infection is substantially more common in the population than we realized prior to the study.

So, that's what Jay Battacharya was censored for.

A Stanford Epidemiologist was censored by the authority of the Supreme Court, for making the results of his Scientific study known to the public. 

Seriously, WHAT THE FUCK IS THIS SHIT?

It's time to pack it all in.


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