He's updated his death numbers to reflect NYC:
And here's his explanation:
Covid-19 "USA Deaths Tracking with NY"
This graph is really revealing. And more importantly, it confirms an hypothesis we had a couple days ago. Cha-ching!
I have changed nothing about any of the models. Think of this as merely "accounting." This is the first time I have ever looked at this plot, because I just made it. I've been stewing over the right way to do this for a couple days, and it was the first thought on my mind when I woke up. (The power of the subconscious mind...)
Let me walk you through it. I usually don't like putting so many traces on a single graph for public viewing. It scares people off. But it is worth it, trust me. Take your time, digest each plot. Look at the scale for it, and don't move on until you get it intuitively.
Let's start with the axes. The left vertical axis is for all "Deaths/day" curves, the right vertical axis is for the two "sigmoids," or total death curves that finish into it. (Near 1400 and 2900.)
Let's start with the red dashed plot with the red dots, USA Reported deaths per day. The scale for this is in red, on the left. It is real data, so it is noisy. But it is reality. (Always start with the DATA!!)
The pink shaded line that is tracking the red-dots-curve is the sum of my original projection for the whole USA (deaths/day) plus the model predictions for NY (deaths/day). This "sum of models" is closely tracking what we are **actually** observing. (It is the sum of the light gray and blue peaks.)
Next, the solid dark gray line is my original model death tally estimate for the whole country (finishing on the right, near 1400 total death tally). The light gray peak is the cases/day for that model (use left scale).
The light blue curve is the peak corresponding to the deaths/day predicted by our NY model.
When you add the original model to the NY model, you get the dark red plot, finishing just under 3,000 total deaths.
The dark red dots and dashed curve is the total reported death tally for the entire US (actual data). Note the "inflection point" in the curve corresponding to when the NY death peak starts growing and the trace begins diverging from my earlier estimate. (This is exactly what a "secondary infection looks like in other countries, eg Italy.)
I am not saying NY is a secondary infection. I'm saying, when you consider the data and models separately *and* together, this approach provides valuable insight into what is going on in our country.
I will update this graph every day for a while. I might simplify it too... but there is so much good information in here, I don't want to leave anything out. I will think on it.
What I love about this, is that we hypothesized a couple of days ago that this way of thinking about the country would match the data... AND IT DOES. An a priori hypothesis confirmed, making us more confident in our models. This is the scientific method.
It also reveals that my original assumptions (and Midwestern bias?) about our country were likely incorrect, and that I will need to revise my assumptions. Note that I am being careful to let the data guide my thinking. Make an hypothesis, then test it. Learn. The scientific method works.