Dr. Jay Bhattacharya Emerges Top Candidate to Lead NIH

Dr. Jay Bhattacharya Emerges Top Candidate to Lead NIH

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.