Simas Kucinskas writes:

I would like to share some recent research (pdf “>here). In this paper, I develop a new method for estimating R in real time, and apply it to track the dynamics of COVID-19. The method is based on standard epidemiological theory, but the approach itself is heavily inspired by time-series statistics. I use Stan in estimating the model using Bayesian methods. It’s such a fantastic tool.

I provide an online dashboard where one can compare multiple countries and track the development of R over time (bit.ly/2KiPj9s). Here is a graph of the current estimates of R for the world as a whole:

I [Kucinskas] also use the estimates to take a first pass at evaluating the effectiveness of public health interventions. Here is how the estimates of R look like 4 weeks around the imposition of a lockdown (sample of 13 European countries):

There are obviously many caveats, and one should not over-interpret such evidence. But I [Kucinskas] find the graph quite striking.

I’ve only glanced at the paper so I’m not endorsing (or criticizing) its conclusions. The important thing from a statistical standpoint is that the assumptions and methods are transparent, so that if the ideas here are useful, they can be judged by experts and then used as components in other people’s models.

I’m always saying how statistics is the science of defaults. But there is a more general sense in which *all of science* is the science of defaults. In the exploration-exploitation tradeoff, we should just about always consider ourself in the exploration stage. The value of a study is almost always in how it gives clues to allow us to do better studies in the future.

**P.S.** The y-axis should go exactly to zero on both graphs. On the top graph, the y-axis goes below zero, which makes no sense; on the bottom graph, the y-axis has a hard stop at 1, which doesn’t make sense either.