One of the most contentious issues that has emerged in COVID-19 debate is the inaccuracy of statistical modeling of the infection. This has been exacerbated by a very contentious debate on how individuals in the public health community should frame disagreements about predictions are the COVID-19 epidemic.
Two recent articles have highlighted this debate. An editorial in the Wall Street Journal today called into question many of the projections different academics have made about the epidemic pandemic. In addition, a new paper by Stanford epidemiologist John Ioannidis suggests most models have overshot infection and death statistics in part by making faulty assumptions about virus reproduction rates and homogenous susceptibility.
The contentious debate about the accuracy of COVID-19 modelling has gotten to the point that it has been called “Medical tribalism,” where those scientists who chose different sides in the low and high end of predictions have attacked attack each other vociferously. There also seemed that political partisanship infiltrated the debate.
Errors in modelling have been common on both sides of the COVID-19 divide. A Massachusetts General Hospital model predicted more than 23,000 deaths within a month of Georgia reopening but the state had only 896. Neil Ferguson famously projected 2.2 million deaths from COVID-19 in the US. Ioannidis own estimate that as few as 20,000 people in the US will die from COVID-19 is tragically incorrect as we approach 125,000 deaths.
Those responsible for making decisions based on these models admit to the errors. Gov. Cuomo was asked last month about when fatalities and hospitalizations would meet state thresholds for reopening, and he responded: “All the early national experts, ‘Here’s my projection model.’ . . . They were all wrong. They were all wrong. . . . I understand that. We didn’t know what the social distancing would actually amount to. I get it, but we were all wrong.”
Ioannidis believes that the contentious debate has polarize the models and played into the incorrect results. “In the presence of strong groupthink and bandwagon effects, modelers may consciously fit their predictions to what is the dominant thinking and expectations—or they may be forced to do so,” Mr. Ioannidis writes. “Forecasts may be more likely to be published or disseminated, if they are more extreme.”
New infections may be expected as states reopen, and health officials will have to monitor and identify hot spots. The WSJ argues that “we all need to understand that even the most sophisticated models aren’t infallible, and the cost of new lockdowns is too great to sustain.”
Regardless of the political intrusion into this debate, if nothing else as the American people take on the daunting task of restarting their lives, they at least deserve an open, data based debate about the risks they are taking.