Simulating Many Scenarios of an Epidemic
Back when the COVID-19 pandemic was beginning to be taken seriously by the American public, 3blue1brown’s Grant Sanderson released a video about epidemics and exponential growth. (It’s excellent — I recommend watching it if you’re still a little unclear on how things are got so out of hand so quickly in Italy and, very soon, in NYC.) In his latest video, Sanderson digs a bit deeper into simulating epidemics using a variety of scenarios.
Like, if people stay away from each other I get how that will slow the spread, but what if despite mostly staying away from each other people still occasionally go to a central location like a grocery store or a school?
Also, what if you are able to identify and isolate the cases? And if you can, what if a few slip through, say because they show no symptoms and aren’t tested?
How does travel between separate communities affect things? And what if people avoid contact with others for a while, but then they kind of get tired of it and stop?
These simulations are fascinating to watch. Many of the takeaways boil down to: early & aggressive actions have a huge effect in the number of people infected, how long an epidemic lasts, and (in the case of a disease like COVID-19 that causes fatalities) the number of deaths. This is what all the epidemiologists have been telling us — because the math, while complex when you’re dealing with many factors (as in a real-world scenario), is actually pretty straightforward and unambiguous.
The biggest takeaway? That the effective identification and isolation of cases has the largest effect on cutting down the infection rate. Testing and isolation, done as quickly and efficiently as possible.
See also these other epidemic simulations: Washington Post and Kevin Simler.
Note: Please keep in mind that these are simulations to help us better understand how epidemics work in general — it’s not about how the COVID-19 pandemic is proceeding or will proceed in the future.
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