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Daily COVID analytical update for Friday, April 24

Daily deaths decline, daily new cases rises

A few interesting things to talk about today. First, a warning – a slight bit of math talk to come. If you’re allergic to this, skip down to the daily stats below to continue reading.

To me, the biggest news yesterday was the random antibody testing in NY. A sample of about 3,000 tests were done throughout the state, and an amazing 13.9% tested positive. You can read about it here: https://www.newsweek.com/test-shows-21-percent-new-york-city-residents-who-gave-samples-have-coronavirus-antibodies-1499878

This gives us the first solid clue into mortality rates. NY state has a population of about 19.45 million. If we extrapolate the testing results across the state (more on this in a minute), we’d estimate that 2.7 million New Yorkers have had COVID. As I write this, NY has suffered about 21,000 deaths. Simple division suggests a mortality rate of about 0.8%. OK, so what I just did is not scientific. To do this properly you’d have to analyze the demographics and biases in the sampling, and more importantly, align the exposure period with the death count – BUT, this gives us an idea. Since deaths are concentrated among seniors, my gut tells me that the mortality rate is much higher in the 65 and over cohort, and much closer to zero for those under 65. I’m sure you’ll begin to see lots of analysis on this in the coming days. It’s critical to get a bead on mortality rates by demographics – I believe that this will prove to the be the most important factor driving public policy should we see another wave next winter.

Next piece of news: My Logistic model is not tracking the back side of the curve very well. I thought it was a little soon to conclude that, but Dr. Conyers (on this list) sent me a fascinating podcast interviewing the Director of the IHME (https://fivethirtyeight.com/?s=Podcast), whose model I track and respect. He talks about many of the same frustrations I’ve had with shifting data definitions and protocol, and concludes that the shape of this disease in the United States is not classically symmetrical. The IHME model has many similarities to mine – they are both non-linear functional forms fit to time series data, rather than assumption driven Epidemiological models. However, the classic functional form for communicable disease growth in a resource constrained system is the Logistic function, which has the property that the growth of the disease mirrors the later decline (symmetry). What we seem to be seeing is a classic growth pattern, followed by a lingering plateau, followed by a somewhat slower decline than the corresponding ascent. IHME have made recent adjustments in their model to reflect this pattern.

I theorize that this pattern is a result of the continual expansion of the definition of a COVID case, and the rapidly increased scope of testing. The data was stable so long as the cases captured were a stable percentage of actual cases. With widening testing, we are discovering more of the disease. This doesn’t mean it’s growing faster, it means that we know about more of it. I believe this explains the divergence between reported case count and reported deaths (case count increasing, death count dropping).

In any event, after today I’m retiring my Logistic model. It did a wonderful job of predicting the peak and the magnitude, even as far back as weeks ago. As a mathematician, I feel good about that. But you must abandon your tools when they are no longer useful. I will continue to model active reported cases, as my methodology has produced results which correlate well with hospitalizations, and in most cases, death statistics.

As always, if you have any questions about my assumptions, or how I’m modeling, or want to know about Logistic modeling generally, drop me a line. If you’re bored with this daily report, let me know and I’ll remove you from the list.

  • Likely date of active case peak (Chalke modeling): April 10
  • Likely date of peak deaths (IHME): April 15 (last revision on April 22)
  • Short term projection for tonight: 920,000
  • Total Test Results reported yesterday: 193,691
  • Total Pending tests reported yesterday: 4,258 (very low)
  • National reported case Growth Rate yesterday: 4.1%

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