Those arguments make sense, but for example what if despite our best modeling, the cases just start to decline and then the whole thing just disappears in a month? At what point would we have to seriously re-evaluate everything we know about this virus? Say new cases plunge 90% next week? 50%?
Scenario planners try to think of every possible alternative, including those that seem far-fetched. I’m trying to figure out what the positive alternatives would look like.
I mean Y2K in the sense of lots of fretting about something that turns out not to happen, whether because of significant preparation or from just being wrong about the urgency.
I realize it doesn’t seem likely, but in the spirit of humility before humanity’s collective ignorance, how might we know we were wrong? Like, clearly nobody’s expecting US cases to suddenly level off and then disappear, but what if that happens anyway? At what point would we say we were just totally wrong?
Is somebody keeping track of the “what if we’re wrong and it turns out this is another Y2K” scenario? Social distancing, closing borders, heightened awareness and preventative measures—seems like a lot is happening that could make this way less scary, at least in the US, than most of the mainstream scenarios.
I’m not interested in the hearing from denier-types who think this is “just another flu”, but rather the thoughtful people who have specific testable predictions that would demonstrate this is more social contagion than most of us suspect.
I’d love to see a Github version of this post. I’d be interested in re-running the same data to generate results for other cities (e.g. Seattle).
Or maybe some-such site/notebook already exists? If so, please do tell!