Topic: Mathematical Modeling

4 chapters across the catalog

COVID Retrospective
Episode 1439 59:26 - 1:01:58

1439: COVID Retrospective

Global Death Rate, Harvard Infection Projections

The WHO reported a global death rate of 3.4% for COVID-19, noting it was deadlier than the seasonal flu. A Harvard expert provided a projection that 40% to 70% of the world's adult population could eventually be infected. The expert noted that many cases might be asymptomatic, which would lower the overall fatality rate among the total infected population.

Get Boris!
Episode 1417 1:35:56 - 1:39:12

1417: Get Boris!

2007 CBC Report, Flu Death Modeling

A 2007 investigative report by CBC's Kelly Crowe revealed that official flu death statistics in Canada are based on mathematical models rather than actual body counts. The report found that deaths from heart and lung disease are often categorized as "possible flu deaths" to encourage vaccine uptake. The hosts use this to illustrate long-standing issues with the veracity of public health data.

Imbleachment
Episode 1237 2:37:24 - 2:40:59

1237: Imbleachment

Criticism of the Imperial College COVID-19 Model

Dr. Giesecke criticizes the Imperial College report that led to Boris Johnson's "180-degree turn" and the UK lockdown. He points out that the paper was never peer-reviewed and relied on flawed assumptions that have since been heavily criticized. The hosts conclude that the world has been misled by "bullshit" mathematical models rather than actual science.

Booby-Trap
Episode 1222 18:14 - 22:50

1222: Booby-Trap

Harvard Expert Predicts Massive Global COVID-19 Infection Rates

Dr. Marc Lipsitch of Harvard University provided a startling projection to CBS News, estimating that 40% to 70% of the world's adult population could be infected by the coronavirus. The hosts perform calculations based on these figures, suggesting such a scenario would result in millions of deaths in the United States. They express skepticism regarding the accuracy of these mathematical models compared to seasonal flu data.