The Perils of Predicting COVID-19
Predicting the future is hard. Predicting the future of COVID-19 is a case in point.
“Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window. ” – Peter F. Drucker
On March 16, 2020 Neil Ferguson, an esteemed epidemiologist at Imperial College London, confidently predicted that if the U.K. did nothing, more than 500,000 U.K. citizens would die from coronavirus, and that even with some mitigation efforts it “would still result in about 250,000 deaths and completely overwhelm intensive care in the NHS.” In an abrupt about-face on March 26 (10 days later) in parliamentary testimony he revised his estimate to “less than 20,000” and stated that the U.K. should have sufficient intensive care units to handle it. All of his statistics are derived from computer modeling.
Poor Mr. Ferguson, if only he’d read Peter Drucker! The media and politicians pounced on the initial estimates and forged full steam ahead with frightening headlines, multi-trillion dollar spending packages, recriminations and pious pronouncements, only to say “Oops” 10 days later. Except that many never said oops or even acknowledged the revised forecast.
In truth forecasts of what’s ahead with COVID-19 are all over the map. There are two problems:
- We don’t have the data we need regarding the status-quo.
- Even if we did, we don’t have reliable models to predict the future.
Other than that, we’re in great shape!
True, we need to act decisively in times of crisis. But, as the adage says, many unscrupulous politicians, journalists and hacks in general won’t let this crisis go to waste. And the pandemic is the biggest crisis faced in my nearly 72 years. I fear that it will be used by some to take our money and our freedoms. As professionals who know the hazards of predicting the future we owe to ourselves and our posterity to be watchful. Don’t let fear cause you to suspend skepticism. Question the numbers. What are the assumptions that underlie the predictions? How are the models validated? What data were used? What are the shortcomings? What are the possible biases of the researchers? These are just a few of the crucial questions you need to ask when you read those scary headlines.
Fancy computer models and impressive statistics are no substitute for common sense. In fact, red flags should fly when you see them used. So when a Harvard researcher predicts the death of millions, take it with a grain of salt. Take a deep breath, and ask a lot of questions.
* To date (April 20, 2020) there have been 16,060 coronavirus deaths in the U.K., a tragedy for sure, but no where near the initial predicted number. The 20,000 deaths prediction may be close.
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