A New Understanding of Herd Immunity
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Edward Lorenz was just out of college when he was recruited into World War II. He was assigned to be a weather forecaster, despite having no experience in meteorology. What Lorenz knew was math.
So he started experimenting with differential equations, trying to make predictions based on patterns in data on past temperatures and pressures. One day, while testing his system, he repeated a simulation with a few decimals rounded off in the data. To his surprise, a radically different future emerged.
He called this finding “the butterfly effect.” In a complex model, where each day’s weather influences the next day’s, a tweak in initial conditions can have wild downstream consequences. The butterfly effect became central to the emerging field of chaos theory, which has since been applied to economics, sociology, and many other subjects, in attempts to deconstruct complex phenomena. That field is now helping predict the future of the pandemic—in particular, how it ends.
[Read: How the pandemic will end]
Chaos theory applies neatly to the spread of the coronavirus, in that seemingly tiny decisions or differences in reaction speed can have inordinate consequences. Effects can seem random when, in fact, they trace to discrete decisions made long prior. For example, the United States has surpassed 125,000 deaths from COVID-19. Having suppressed the virus early, South Korea has had only 289. Vietnam’s toll sits at zero. Even when differences from place to place appear random, or too dramatic to pin entirely on a failed national response, they are not.
There is enormous variation even within the U.S., which could also seem chaotic. Some places took limited measures and were barely hit; others locked down but suffered greatly. New York City has been slowly since early June, but despite that—and despite mass outdoor gatherings in the throes of civil unrest over the past six weeks—the city has not seen even a small increase
