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Daily COVID analytical update for Tuesday, April 28

1st cut at NY mortality rates

For well over a month now, I’ve been using data from Infection2020 for the daily growth rate, daily new cases, and cumulative reported cases. I’ve been using The COVID Tracking Project for state level data, testing data, and deaths. I’ve been reporting on active cases derived from both data sources. Lately, however, Infection2020 has been less reliable. They’ve been reporting only once a day (it used to be continuous). They note on the site that they are adding new sources of data and are under financial stress. You can donate on their site – I did. In any event, their data is diverging from other sources. As of late last night, they were showing over a million reported cases, while the COVID Tracking Project was showing 981K and JMU this morning is showing 988K. Also, at one point in the evening they were showing over 50,000 new reported cases, which is substantially out of line with other sources. As a result, I’m going to standardize my reporting and analysis on COVID Tracking Project data beginning later today. I will rerun all of the charts from the beginning with COVID Tracking Project data, and suspend use of Infection2020 data. Of course, I’ll still monitor a variety of sources to make sure I’m using reasonable source data.

Since The COVID Tracking Project uses a 4pm EDT cutoff, I will begin issuing my analysis in the early evening, instead of mid-day. So this will be the last daytime report, and you will receive another analysis around 6 or 7pm today, and each day thereafter at the same time.

Now, a thought or two about mortality. On April 23rd NY reported completing a somewhat random test of 3,000 New Yorkers for COVID antibodies. https://www.newsweek.com/test-shows-21-percent-new-york-city-residents-who-gave-samples-have-coronavirus-antibodies-1499878 Tests were done earlier that week for randomly selected individuals in grocery stores and shopping centers. Of the 3,000 tested, 15.9% were shown to have had the COVID infection. Since that time, New York has revised that number up to 16.9%. This is incredibly useful information, and provides us with a first meaningful clue about mortality. I first did a rough calculation of NY mortality rate by extrapolating the random test results to the NY population, then aligned deaths with the exposure period. From this I calculate a mortality rate of 0.53%. This is not unexpected, but it is much more interesting when broken down by age. When I look at cohorts of age 60 and over, and 59 and younger, and go through the same calculations, I get an estimate of mortality rate for these two age groups of:

Age COVID Mortality Rate
0-59 0.09%
60+ 2.50%

Now there are lots of issues with my calculations. First, although the sample may have been random, there was some self-selection, as the sample pool was composed of those willing to leave their homes, which I bet skewed younger and less risk-averse. I also do not have a demographic breakdown of the sample. The only thing I know is that no one over age 75 was sampled. This is a major issue, as over ½ of all deaths in NY are over age 75. This leads me to believe that COVID prevalence is actually lower among the 60+ cohort, and higher among the 59- cohort. As a result, I believe that the mortality rate for the elderly is higher than that calculated above, and the mortality rate for those under 60 is lower than that calculated above. By the way, I would have preferred to use an age 65 cutoff, but unfortunately the NY Department of Health only reports deaths in deciles. Ideally, if I had the data, I could study mortality in 2 cohorts – those over 65 or with certain co-morbidities, and a second group of under 65 without certain co-morbidities. I believe you’d find a mortality rate in the first group well over the 2.50%, and the mortality rate in the second group much closer to nil.

So as we recover as a society, the methodology seems to settled that we’ll unlock one geographic area at a time. But we have 2 easily identifiable populations, once with relatively high risk, and one with a tiny fraction of the risk. It occurs to me that we could more safely unlock by doing it demographically, rather than geographically. We could extend shelter in place rules for seniors and those with definable risk factors, and eliminate them for the under 65 and healthy population. It would also be fairly easy to define perhaps one concentric circle around the vulnerable by maintaining shelter in place rules for certain caregivers and health care workers. So that’s my message for today: Unlock Demographically, Not Geographically.

OK that’s a lot for this morning. As always, if you have any questions about my assumptions, how I’m modeling, data sources, 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 27)
  • Short term projection for tonight: 1,010,000
  • Total Test Results reported yesterday: 152,416
  • Total Pending tests reported yesterday: 4,077 (very low)
  • National reported case Growth Rate yesterday: 3.4% (suspect – likely lower)

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