Another slight uptick today, from 1.53 to 1.54
But the 3DDRR outside of New York went up even more, from 1.54 to 1.58
I expect the 3DDRR and the 3DDRRxNY to be lower tomorrow, perhaps even 1.45, just because the death numbers on Tuesday look suspiciously high, as if they borrowed numbers from either Monday or Wednesday. If you have a data series with that sort of daily noise, you may want to look at a longer time interval. If you look at a 5DDRRxNY, it is very close to 2, meaning that the death rate is doubling every five days instead of every three. The good news is that the trend for the 5DDRRxNY is down, but the bad news is that it is not falling fast enough to warrant saying that the worst is behind us.
Speaking of noise in the daily data, a commenter from Indiana pointed out to me the contrast between deaths reported by the covidtracking site that I use for the 3DDRR calculation and the Indiana Department of Health site.
Here is how I understand what is happening. Suppose that 15 deaths occur on Monday, but the Department of Health finds out about 5 of them on Monday, 7 on Tuesday, and the other 3 on Wednesday. As of Monday, it will have incremented its total cases by 5, which is what the covidtracking site will pick up as the incremental deaths for Monday. Then on Tuesday, the Department of Health will revise its Monday total up by 7, but the covidtracking site will pick up these 7 deaths as incremental for Tuesday. And on Wednesday, the Department of Health will revise its Monday numbers up by 3, but the covidtracking site will pick those up as incremental for Wednesday.
In a steady state, daily deaths and the proportions reported staying constant, this would not affect the covidtracking site. But if deaths are increasing (decreasing), that site will show the increase (decrease) with a lag. If something similar holds true for other states, then this will add noise to the 3DDRR calculation.
Use a 7 day moving average.
And we know when we hit equilibrium because the deaths will not lag. We will be able to measure them as a constant random flow with some bounded accuracy.
Just for interest. There is a wide discussion about whether I am correct, and when do the deaths becomes a bound, uniform random variable. A recent proof on the subject, proving the negative for some cases, has been published and is evidently a ground breaking proof.
The proof goes directly to the issue of finding patterns of sustainable specialties. 170 page proof that is rapidly become a textbook for mathematicians.
https://www.quantamagazine.org/mathematicians-grapple-with-sudden-answer-to-connes-embedding-conjecture-20200408/
Written in terms of a value added chain, as a matter of fact. Can a value added chain increase its layering and see a reduction in inventory volatility? This proof will lead to a criteria, mainly the depreciation cycles of the intermediate products. Can all their depreciation cycles be aligned?
Given the study cited yesterday by the Harvard-MIT guys, Stock et al, why does one believe anything about R rates?
https://www.nytimes.com/2020/04/10/nyregion/coronavirus-nyc.html
From Wednesday to Thursday, the number of hospitalizations increased by 200, to 18,279, or just 1 percent.
If the trend were to continue, the number of people in hospitals would soon start to decline — a sign that the virus had passed its apex.
But the number of people dying of the virus continues to grow. The state recorded 799 deaths from Wednesday to Thursday, another one-day high.
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Cops and docs are interfering with the spread rate.
The other alternative is that the spread rate remains and most of NYC has the virus. I dunno which.
Arnold, the COVID Tracking site numbers change all the time. So I think that if Indiana updates its Monday deaths on Wednesday, the COVID Tracking site will add that data to its Monday total on Wednesday. So the Monday total on the COVID Tracking site will be different on Wednesday than it was on Tuesday.