Read on Twitter
1/ Yesterday I posed a question here on twitter after reading one of the daily updates by @bob_wachter (I love those updates!) The question was: why should the peak of the COVID19 epidemic come later when the curve is flatter. Should it? https://twitter.com/pleunipennings/status/1248835651732029440
2/ Specifically @bob_wachter wrote: "Having flattened curve, CA’s projected peak is later [than USA], April 15, w/ 1,616 total estimated deaths thru August."
– it sounded like he meant this to be a logical result: flatter curve --> later peak.
– it sounded like he meant this to be a logical result: flatter curve --> later peak.
3/ I know why in epidemics that ultimately infect many, we expect earlier higher peaks (w high R) and later, flatter peaks (w lower R). I was one (of many) who tried to teach the world about flattening the curve. https://vimeo.com/396866214
4/ But ... we are now in a different scenario. The stay-at-home /shelter-in-place orders are extreme and instead of a slow increase of the cases to reach a peak months from now, in many places we saw a decrease in the # cases about 10-14 days after the stay-at-home order.
5/ Here I show a clear example from my home country, The Netherlands. Schools closed on March 16th. The peak hospitalizations occurred 7-12 days later. Data from @rivm .
6/ We also see this in NYC. Peak hospitalizations 8-12 days after Stay-at-home order (on March 22).
7/ The fact that these curves are "bending down" (rather than flattening) is really great. The stay-at-home-orders and lock-downs and whatever they are called are working and saving lives.
8/ But that these curves are bending down is due to many staying at home and keeping a distance which leads to a sudden drop in R (how many people get infected by each case).
9/ The "normal" math of epidemics (flatter curve --> later peak) likely no longer holds under the current situation, because it depends on a slow decrease in R due to a build up of herd immunity.
10/ In the "classic" situation, the epidemic grows until R0*S < 1, where S is the fraction susceptible in the population. In a flatter curve, it can take longer to reach R0*S<1, which is why they peak later.
Now that we have reduced R drastically by staying at home as much as we can and not b/c R0*S<1, there is no longer reason to expect that lower curves peak later. Instead, curves would be expected to peak around 14d after a Stay-at-home-order (for hospitalizations).
12/ is this what we’re seeing?
13/ So if there is no reason to expect flatter curves to peak later, why does the @IHME_UW model predict this? The answer is not clear, and I don't know much about those models.
14/ But several colleagues reminded me that the @IHME_UW is a model that basically takes data, fits a curve and then projects into the future. This, in principle, is not wrong, but can lead to wrong predictions. @CT_Bergstrom @magnusnordborg @sarahcobey @DanSRosenbloom
15/ If all you have is the data (and no real understanding of the underlying process), then projecting from a fitted curve is not a bad idea.
16/ Say, you run a hospital and you had 40 new patients 3 days ago, 50 two days ago and 60 yesterday, it'd be smart to expect 70 today and 80 tomorrow and 140 a week from now. But if your mayor all but closes the city, then your predictions for next week may not be very good.
17/ It is actually really hard to predict the future of an epidemic when the behavior of almost everyone is changing dramatically, and it is unclear whether any of the models is doing a very good job at this. (That doesn't mean that we shouldn't try though).
Finally, I would like to suggest two reasons why a lower curve may actually really peak later. 1. people may be less strict about social distancing when there are few cases – meaning that R is not as much reduced.
19/ 2. Another is that when you have few cases, relatively many of these may be coming from travelers from elsewhere, which may be harder to stop than transmission within the city.
