“There are things called models,” Dr. Anthony Fauci explained to CNN’s Jake Tapper in March. “And when someone creates a model, they put in various assumptions. And the model is only as good and as accurate as your assumptions.”
Over the past several months, models attempting to project outcomes have shown their limits. In March, the Imperial College model shocked the world with a projection that, without any interventions, the United States could lose 2.2 million people. The only solution, the paper argued, was widespread lockdown. Any loosening up of these lockdowns, it warned, would lead to a significant surge in cases and the collapse of the healthcare system.
In the time since, experienced modelers have poked holes in this projection. One called the model “quite possibly the worst production code I have ever seen.”
The IHME model, which has been cited by Dr. Deborah Birx and has influenced the White House, saw its death estimates bounce around like a beach ball from over 100,000 deaths to around 60,000, and now back up to around 150,000. The model also massively overshot hospitalization estimates — a key number given that fears of an overwhelmed medical system are precisely what justified the initial lockdown policy.
To be sure, in the early days of the coronavirus, in a vacuum of hard data, models served some purpose. At a time when there were only a few dozen cases in the U.S., for instance, models helped policymakers and the public understand the gravity of the situation.
But now, there are about 4.5 million cases of the coronavirus worldwide, and 1.4 million in the U.S. Different countries and different states have taken a variety of approaches to combating its spread. From this, policymakers have hard data upon which to base decisions. It is the data that should now guide their decisions on reopening.
For example, with millions of cases to study, we now have evidence that there is much less risk of transmission in the outdoors. We know that nursing homes have been hot spots and that subways were a huge cause of the spread. We know that older individuals and those with underlying health conditions are at a significantly higher risk. While the risk isn’t zero among younger age groups, the disease is mild for them in an overwhelming majority of cases.
Data have also started to show that many warnings about the risk of reopening have been false alarms. When Wisconsin held a primary election on April 7, there were warnings that it would trigger a major outbreak. That was nearly six weeks ago, and there is no evidence of a surge in cases as a result of the election.
Experts have consistently warned that Florida, with lots of tourism and international travel and a high elderly population, was at severe risk of becoming a hot spot. Republican Gov. Ron DeSantis was brutally attacked for failing to close its beaches quickly and for trying to reopen the state relatively early in reopening. Likewise, Republican Georgia Gov. Brian Kemp was blasted as reckless for pushing to reopen his state in late April. Yet, both Georgia and Florida have very low rates of death per capita. Their healthcare systems were not overwhelmed, and so far, both states have seen cases consistently decline. There is nothing in the data, at least not yet, to back up what naysayers claimed based on models.
Then again, the data could also lead to a more stringent approach. For instance, there are growing reports of a Kawasaki-like inflammatory syndrome developing among young children. It is unproven whether this is connected to the coronavirus. Without knowing the number of asymptomatic cases of the COVID-19 virus, it’s hard to say how likely it is for those infected with the coronavirus to develop this syndrome. But if data show that this is a greater risk, then policymakers may have to reconsider their assumption that the risk to children is negligible.
The overarching point is that models fill the vacuum in the absence of data, but as more data accumulates, it should now be driving policy decisions.
