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A New Understanding of Herd Immunity
A lengthy and interesting article from the Atlantic. (not behind their normal paywall)
This is a very interesting discussion of the predictive mathematics behind virus spread and herd immunity. The author brings up an interesting point about the initial death spike killing off the weakest in a community. I hadn't thought about that, but he never mentions the second wave that we are currently experiencing which is brought on by the stupidest in society. That aside, it's a lengthy and fascinating article.
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.
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.
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.
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.
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