Coronavirus Predictions Bedeviled by the Details
(Bloomberg Opinion) -- Until early this week, the U.K. government under Boris Johnson had been oddly relaxed in its response to the coronavirus pandemic. Unlike many other nations that had closed schools and restaurants and banned gatherings of even five people in an effort to curb the spread of Covid-19, the U.K. had allowed life to continue much as normal, only testing patients entering hospitals and requesting those with symptoms to self-isolate.
That's changed significantly, with the government now advising people to work from home and to avoid public transport and gatherings with friends and family; pubs, restaurants, gyms and movie theaters will be ordered to close. The about-face came after hundreds of scientists from around the world attacked the earlier policy as needlessly risky. And an epidemiological modeling group at Imperial College London — a key scientific resource for the government — revised its estimate of how soon serious cases would overwhelm the National Health Service, adding that the previous policy could have resulted in roughly 250,000 deaths.
It's good to fix a policy mistake, and the task now is to move forward while learning how to avoid similar mistakes. The biggest problem, it appears, was how decision makers handled — or in this case mishandled — modeling uncertainty. Sophisticated models look impressive and have their uses, but they can be less useful than simpler models if their predictions depend on a few small details.
The group at Imperial College used a complicated model to simulate the spread of the disease, as well as the effects of various countermeasures. It included millions of individuals; realistic patterns of human movement and contact in homes, businesses and public places; and details of how the virus can spread. That's all good. Models like these are important precisely because they allow modelers to include government responses and test the likely consequences of a range of policy scenarios.
There's a drawback, however. Models of this kind depend on parameters for such things as the incubation period of the virus and when people either with or without symptoms can pass it on to others. The values of these parameters are uncertain. The early U.K. policy was based on the belief that one key parameter — the fraction of hospitalized people needing intensive care — was lower than it turned out to be. Indeed, the actual number seems to be roughly twice as large as initially expected, rendering the earlier modeling results irrelevant.
This is an issue mathematicians have been writing about for a number of years: It's particularly easy for policy makers to misuse complicated models. “Modeling — especially complex modeling — can promote something of a fairy-tale state of mind,” says Erica Thompson of the London School of Economics and the London Mathematical Laboratory. (Full disclosure: I am an external fellow and colleague of Erica's at the laboratory.) “People come to believe that optimal outcomes in a simulation invariably reflect desirable pathways in the real world. But things get lost in the move back to reality.” As a result, she says, much simpler models can be more stable and trustworthy for use in policy making, because it is clearer that they are “only models” and don’t invite misplaced confidence in the details.
There's little question that most of the other nations that took more aggressive action against coronavirus also had access to complicated computer models to simulate epidemics. But they seem to have fashioned their policies on the basis of a simpler insight: that epidemics grow exponentially, at least in the early stages following an outbreak, and get harder to control as time goes on. In any epidemic, time is of the essence. Let's act now.
None of this is to blame the modelers from Imperial College. Building models is what modelers do, and including more details is often how models get better. This sophisticated model has met the test of peer review in modeling influenza. Problems arise when policy makers use those models to make decisions while forgetting that the model is not reality.
In essence, the old U.K. policy aimed for an optimal strategy in the midst of great uncertainty, hoping to thread the needle between disasters of public health on one side and economics on the other. The success of this policy depended on luck with the values of certain parameters, and they weren't lucky. It was a gamble, and the nation lost precious days in the fight against coronavirus as a result.
This column does not necessarily reflect the opinion of Bloomberg LP and its owners.
Mark Buchanan, a physicist and science writer, is the author of the book "Forecast: What Physics, Meteorology and the Natural Sciences Can Teach Us About Economics."
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