Did a Math Error Lead to the Never-Ending COVID-19 Lockdowns?

AP Photo/Paul Sancya

More and more voices are now speaking out on the lockdowns, widespread testing, and other policies associated with the COVID-19 response. Now a doctoral candidate in epidemiology from the University of Waterloo in Ontario, Canada, Ronald B.Brown, Ph.D., is questioning how mortality data was presented to Congress early in the pandemic.


This hearing was held on March 11, 2020, in front of the House Oversight Committee. During the questioning, Dr. Anthony Fauci asserted that the mortality rate for COVID-19 was 3%. He then extrapolated to claim that if you added in the mildly symptomatic cases, it would probably be about 1%, which is ten times more deadly than the seasonal flu.

Brown asserts that this testimony is what launched campaigns for social distancing, lockdowns, and shelter-in-place orders to varying degrees nationwide. He then goes on to explain that fatality rates are classified in two different ways, while mortality rates are a timebound calculation.

The CDC defines mortality rate as the frequency of deaths within a time period for a well-defined population. To calculate the mortality rate for women under 40 from breast cancer for 2019, you would divide the number of deaths from breast cancer for women under 40 by the total population of women under 40 during that specific calendar year.

A case fatality rate (CFR) is calculated as the number of people who die of a disease divided by the total number of confirmed cases of the disease during a period of time. This calculation is a measure of disease severity. Using the example above, the CFR for breast cancer in women under 40 would be divided by the number of confirmed breast cancer cases under 40.

By contrast, an infection fatality rate (IFR) is the number of deaths from an infection divided by the prevalence of that infection in the population. As we know, with COVID-19, there are a significant number of asymptomatic and mild infections. The number of infections is estimated based on representative samples of blood tests looking for an immune response. The CDC estimated that for COVID-19 the number of infections in the population was ten times the number of confirmed cases in July of 2020.

What the Media Won’t Tell You About the United States’ COVID-19 Case Fatality Rate

For COVID-19, all of these calculations are already a little muddy. The National Center of Health Statistics (NCHS) relaxed the guidelines for classifying a death as COVID-19. It did not require a confirmed test or any other medical documentation to use the COVID-19 ICD-10 code on the death certificate. The latest update encourages testing wherever possible, but still does not require a lab-confirmed test.

Combine the relaxed stance from the NCHS with the financial incentives provided to hospitals for COVID-19 care through the CARES Act, and it’s not a conspiracy theory to think death rates may be overstated. This assertion is reinforced by states like New York periodically reclassifying deaths after the fact.

Brown asserts that because these three statistics can vary widely, it is essential they are not confused. Yet he believes this may have happened with COVID-19 rates, which he explained in his testimony:

Previous to the Congressional hearing, a less severe estimation of coronavirus mortality appeared in a February 28, 2020, editorial released by NIAID and the Centers for Disease Control and Prevention (CDC). Published online in the New England Journal of Medicine (NEJM.org), the editorial stated:

“…the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza (which has a case fatality rate of approximately 0.1%).”

Almost as a parenthetical afterthought, the NEJM editorial inaccurately stated that 0.1% is the approximate case fatality rate of seasonal influenza. By contrast, the World Health Organization (WHO) reported that 0.1% or lower is the approximate influenza infection fatality rate, not the case fatality rate.


By definition, the CFR is always going to be higher than the IFR. The bar for inclusion in the population in the denominator for CFR is most often a laboratory-confirmed case of the infection. By contrast, the denominator in the IFR is estimated based on widespread population samples looking for antibodies. Brown explains the significance of the error:

In a comparative analysis with WHO and CDC documents, the coronavirus mortality rate of 2–3% that was adjusted to 1% in Congressional testimony is consistent with the coronavirus CFR of 1.8–3.4% (median 2.6%) reported by the CDC. Furthermore, the World Health Organization reported that the CFR of the H1N1 influenza virus is also 2–3%, similar to the unadjusted 2–3% CFR of the coronavirus reported in Congressional testimony, with no meaningful difference in mortality. As previously mentioned, the World Health Organization also reported that 0.1% is the IFR of seasonal influenza, not the CFR of seasonal influenza as reported in the NEJM editorial.

Fauci used the IFR to describe the seasonal flu and the H1N1 pandemic. He used the CFR to describe COVID-19. This error led him to estimate that COVID-19 would be ten times deadlier than the flu, which drove public policy. Brown also called out the classification error of referring to all positive tests as “cases.” This error is constant in the media. It is also misleading given recent reporting from The New York Times about the significant oversensitivity of the PCR test.

The New York Times Does Accidental Journalism on COVID-19 Testing

The estimation provided by Dr. Fauci was used to communicate with the public, by the media, and to determine public policy. It has also led to the word “cases” being redefined to mean people who are sick with COVID-19, as well as people who display no symptoms. A positive test puts you in that category even though the test is so sensitive that as often as 90% of the time it picks of virus incapable of causing transmission of infection.

Even worse, the media still does not delineate the risks to various segments of the population. They also don’t discuss where the majority of new cases are occurring. This metric is vital to assess health system resources. If the majority of cases are in people under 40, the risk to the system remains low.

So, thanks to a false comparison, we closed schools, colleges, and pretty much everything else. Many areas are still having reopening debates. Ultimately, the CDC placed the IFR for COVID-19 at 0.26 in June. That is a rate between two and three times the WHO estimate of IFR for the flu and is significantly concentrated in patients over 65 with preexisting conditions. This knowledge should have caused a significant shift in public policy nationwide, but we still have states and cities that have draconian restrictions.

The conflation of CFR and IFR at the outset of the pandemic was a mistake that likely cost billions of dollars and an untold number of lives when we consider increases in suicide and deaths from undetected diseases, which will lag. The public health apparatus owes the country an answer on this and an assurance it won’t happen again.


Editor’s Note: Want to support PJ Media so we can continue telling the truth about the China virus? Join PJ Media VIP TODAY and use the promo code WUHAN to get 25% off your VIP membership

Correction: An earlier version of this article listed Brown as a faculty member rather than a doctoral candidate.


Trending on PJ Media Videos

Join the conversation as a VIP Member