The Two Mistakes That Led to a National Lockdown

May 13, 2020 Updated: May 13, 2020

News Analysis

Michael Bloomberg’s doomed campaign for the 2020 Democratic presidential nomination was only days away from crashing when he wrote his strategy on March 1 for fighting the CCP virus.

“As president, I’ll trust the science and let the experts do their jobs,” Bloomberg wrote on Twitter. Three days later, he quit the race.

Bloomberg’s declaration of faith in science was repeatedly echoed by other candidates, elected and bureaucratic officials, medical and scientific experts, and influential journalists like Fox News’ Chris Wallace, who declared on March 27, “All I can say is trust the science, trust the science.”


President Donald Trump did just that on Jan. 31, by restricting travel into the United States for those who had been in China in the prior 14 days. The CCP virus—also known as the novel coronavirusfirst became public the month before in Wuhan, China.

Trump did so again on March 13, when he declared a national coronavirus emergency, and on March 16 when he issued national guidelines instructing all Americans to avoid groups of more than 10 people and to remain at home whenever possible for 15 days.

“If everyone makes this change or these critical changes and sacrifices now, we will rally together as one nation, and we will defeat the virus, and we’re going to have a big celebration all together,” Trump declared during his daily coronavirus media briefing.

Trump’s experts—most notably Dr. Anthony Fauci, long-time director of the U.S. National Institute of Allergy and Infectious Diseases, and Dr. Deborah Birx, the White House coronavirus response coordinator—declared the measures would “flatten the curve” of new CCP virus cases enough to prevent the health care system from being overwhelmed and thus buy time for effective treatments and a vaccine to be developed.

By March 30, virtually the entire nation was under lockdown for at least another month or until further notice. By the end of April, unemployment was the highest since the Great Depression in the 1930s. More than 33 million Americans are now unemployed.

To date, more than 83,000 Americans have died, most being 65 years or older, or suffering an underlying condition such as heart disease, chronic obstructive pulmonary disease, or diabetes.

Trump unveiled a phased-in relaxation of the lockdown in mid-April, but not without heated criticism from experts such as the Mayo Clinic, which reported April 17 that “new projections suggest social distancing may need to continue through 2022.”

Such predictions sparked thousands of citizen protests demanding to be allowed to return to work, church, beaches, restaurants, and other aspects of everyday life.

Flawed Models

How did America get to this point? Two critical CCP virus response mistakes stand out: relying too heavily on flawed statistical models and failing to target resources primarily to protect the most vulnerable Americans.

At the heart of the first error, two models have most influenced policymakers and media reporting: the Imperial College in London model from Great Britain and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.

Trump and other officials in the White House and leaders of both parties in Congress have repeatedly warned that as many as 2.2 million people in the United States could die without urgent official action, a forecast generated by the Imperial model. That forecast, which made a national lockdown seem imperative, wouldn’t hold up.

Neil Ferguson, the Imperial model’s creator, has a history of exaggeration and faulty assumptions. The IHME model has been similarly criticized.

The two models have been all over the map. The Imperial model projected U.S. total deaths at more than 136,000 by the end of May. The IMHE projected 80,000, down from 161,000 in late March. The actual U.S. total as of May 13 was slightly more than 83,000 deaths.

Such variability doesn’t surprise Norbert Michel, director of the Heritage Foundation’s Center for Data Analysis.

“Generally speaking, with the data that is available to everybody, people like those who did the Imperial College one, the IHME, they’re all using the same sort of tools, the same sort of statistical models, and they’re all very sensitive to the assumptions you make,” Michel told The Epoch Times in a recent interview.

“So even if you have the best data, forecasting anything is inherently risky, and if you’re trying to forecast over more than a very short period of time, it’s just very dicey,” he said. Thus, statistical modelers must make assumptions about numerous factors that shape projections.

Michel noted the impact of testing. “If you have a lot of new testing going on, that’s going to give you more cases. So, to get a full picture, you really need to know how many new tests are out there and what percentage of those tests were positive. But we don’t have that data.”

An additional problem is the models’ lack of transparency, according to Michel, as the makers of the models don’t make available to other researchers their code.

“You don’t have to give everybody your data, but you can give everybody your code and let everybody see what you’re doing and see if they can replicate what you’re doing,” he said.

“That’s the key to any kind of scientific endeavor. That’s a basic scientific principle, so that’s a big problem. I have a huge problem with that.”

No wonder the National Bureau of Economic Research recently observed of the models, “In sum, the language of these papers suggests a degree of certainty that is simply not justified.”

Targeting the Vulnerable

The second mistake flowed directly from the first, according to Karl Dierenbach, a Colorado attorney-engineer who has closely studied COVID-19 death rates.

“When New York started publishing age-related/underlying condition data, that was a huge signal the virus is fatal to a very specific group of people,” Dierenbach told The Epoch Times.

“Having 327 million people behave in a way to save maybe a third of the population, it just seemed like there’s a better way, you should be concentrating on those people,” he said.

Dierenbach’s point—prioritize protecting the most vulnerable while allowing most normal activities to continue—is evident in a comparison of New York and Florida.

One in five (20.5 percent) Floridians are 65 or older, compared to one in six (16.4 percent) New Yorkers, making the former the state with the second-largest elderly population and the latter the fourth largest as a percentage of the total.

Dierenbach said he noticed early in the crisis how Florida Gov. Ron DeSantis “just immediately jumped on the old-age communities and how that was where they were concentrating all of their efforts. It just seemed such a logical approach, and I was amazed at how that message never filtered through to the mainstream media.”

The resulting difference in per-capita COVID-19 deaths in the two states is shocking, even given variables like population density: 137 per 100,000 New Yorkers, the highest in the nation, compared to eight in Florida, as of May 11.

Dierenbach conceded that policymakers were in the dark early on in the crisis. “I can’t really fault too many people early on because I don’t think we had the information we needed,” he said.

Second Outbreak?

The problem now, as the lockdown recedes, is that the lethal virus may come roaring back because the flattened curve delayed but didn’t arrest it.

“The lockdown has successfully slowed the spread of it, but that means once we lift it, it’s going to spread,” Stanford University medical professor Dr. Jay Bhattacharya told the Hoover Institution’s Peter Robinson in a May 8 interview.

“I think in the back of people’s heads is this idea that we can somehow eradicate the disease if we just stay locked down,” Bhattacharya said. “That is not possible. We have to come to terms with that.”

Meanwhile, on May 12, Pelosi unveiled yet another proposed $3 trillion coronavirus rescue bill, saying ending the lockdown must be “done on the basis of science and data.”

Contact Mark Tapscott at