Back in early days of the COVID-19 pandemic, Dr. John Ioannidis wrote an article in March of 2020 questioning government statistics about the fatality rate associated with COVID-19. The backlash was swift and brutal and John’s reputation as one of the most influential scientists in the world took a beating.
Today, John makes his second appearance on STEM-Talk to discuss his extensive research into the COVID-19 pandemic as well as the public shaming he received in 2020 for questioning the World Health Organization’s prediction of a 3.4 percent fatality rate associated with COVID-19.
John also talks about his most recent peer-reviewed paper that looked at the age-stratified infection fatality rate of COVID-19 in the non-elderly population. The study found that the pre-vaccination fatality rate for those infected may have been as low as 0.03 percent for people under 60 years old, and 0.07 percent for people under 70, far below the World Health Organization’s prediction of a 3.4 percent fatality rate.
In today’s episode, John walks us through this paper, which was published in January, as well as what he describes as the U.S. government’s bungled response to COVID-19. He also discusses the importance of collecting reliable data in the future to guide disease modelers and governments before they make decisions of monumental significance like lockdowns. He goes on to share how he underestimated the power that politics and the media, or powers outside of science, can have on science.
Over the past two decades, John’s research has earned him a global reputation as a consummate physician and researcher, which contributed to The Atlantic describing John in 2010 as one of the most influential scientists alive. He is a professor of Medicine, Epidemiology and Population Health as well as a statistician and professor of biomedical data science at Stanford University.
Back in 2018 when we interviewed John on episode 77 of STEM-Talk, we talked to him about his 2005 paper questioning the reliability of most medical research. The paper, titled, “Why Most Published Research Findings Are False,” found that much of the medical science reported in peer-reviewed journals is flawed and cannot be replicated. The paper is the most citied article in the history of the journal PLoS Medicine and has been viewed more than 3 million times.
Show notes:
[00:03:16] Dawn opens the interview welcoming John back to STEM-Talk. his last appearance being in 2018. Dawn explains that when John last appeared on STEM-Talk in 2018, he was described by Atlantic Magazine as “one of the most influential scientists alive.” But in the intervening years, John became public enemy number one in 2020 after a paper he published questioning government statistics about COVID 19’s fatality rate. Dawn asks John if it’s fair to say that he has been on a rather rocky ride for the past few years.
[00:03:54] Dawn explains that John was trained at Harvard and Tufts universities in internal medicine and infectious disease, and asks John what led him to study infectious disease.
[00:04:54] Ken asks John about his initial thoughts in 2019 when he first heard the reports coming out of China about COVID-19.
[00:05:52] Ken explains that in March of 2020, John fell into some hot water for writing a piece questioning the 3.4 percent fatality rate associated with COVID-19. John found this number to be inflated and wrote that while COVID-19 was indeed a threat, it did not behave like the Spanish Flu or a pandemic that would lead to a 3.4 percent fatality rate. Ken asks John how he came to this conclusion.
[00:08:37] The article that John wrote in 2020 was titled “A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data.” John argued in his article that the data collected in the first three months of the pandemic was “utterly unreliable.” He went on to write that no one had a good way of knowing how many people ...
Episode 151: John Ioannidis talks about the bungled response to COVID-19 | STEM-Talk podcast - Listen or read transcript on Metacast
