On March 4, I had the distinct honor of presenting my research on cascading expert failure at UW-Superior (you can find the ungated version here). A recording of the talk is available on my YouTube channel. I extend my gratitude to Dr. Joshua K. Bedi for hosting and to the Wisconsin Institute for Citizenship and Civil Dialogue for sponsoring this event.
In my presentation, I explored how the decision made early in the pandemic to limit COVID-19 testing to hospital patients resulted in a phenomenon I term “cascading expert failure.” Regrettably, I did not fully conclude this narrative during my talk, so I will address that oversight here.
By confining testing to hospitals, we inadvertently introduced an upward bias in the data, which distorted the perception of COVID-19’s lethality. This skewed information was then integrated into prominent models, including the notorious Imperial College London model, which projected staggering death tolls in the millions over subsequent months. These projections contributed significantly to the justification of lockdown measures. Despite a growing body of evidence suggesting that the virus was less deadly than initially feared—and that lockdowns may have exacerbated its spread—these misguided policies continued unabated.
