Some things about those hill-shaped infection curves:
1) they assume that R (which essentially measures the rate of spread) is constant. People are expecting the actual curve to look like those but they won’t because we keep changing the way we do/do not distance which changes R

This is important because people see the curve flattening and assume we’re on the “downhill” so it’s okay to go back to normal, but that’s not at all what’s happening.

Which brings us to
2) those are the curves where R > 1. Whether the curve is flat or steep, they all have the same area and predict that eventually most people get the virus.

According to these models, you only start to reach the “downhill” when more than half the population has been infected. We’re not anywhere close to that yet.

Which brings us to
3) when R < 1 the curves don’t have hills but just go downward. When we’ve seen the data going downward, *this is what is happening*. It’s precisely because of successful distancing measures, and if we remove those measures we’re back on the upslope.

The point is that there are lots of possible curves we can go down, depending on R, and we have some control on R via distancing measures.

There are three ways this epidemic ends:
a) everybody eventually gets it and millions of people die
b) we keep R below 1 and stop the spread
c) we get a vaccine or effective treatment and make it available to everyone who needs it

We can hope for c) but avoid a) at all cost

I wrote this thread a week and a half ago but it’s still relevant. #COVID #coronavirus