I now do a daily update for San Diego County and LA County. Since my base is here in San Diego, I’ve decided that with every update, I’ll post a minimum of 3 charts showing the situation here:
- LD1, the local dashboard showing Encinitas, Carlsbad, SD City, SD County, California, USA, and the World.
- LD2, the dashboard page showing mCharts for those same localities
- VS1, Vital statistics for the San Diego County area.
If the primary subject of the update is not our local area, as is the case today, I’ll simply present the charts at the beginning of the update without comment.
County Zorgi Scores
Since I introduced Zorgi Scores last week, they have been the object of love and hate. There are quite a few of you who want a quick “TLDR” picture of the localities, and you trust my judgement enough (perhaps too much!) to give the Zorgi Score some credence.
There were a few others who found it presumptuous of me to even come up with something like that. They preferred that I stick to straight statistics and not inject my personal point of view. One reader suggested that I wasn’t being transparent, and that perhaps I had a hidden agenda to make the Zorgi Score “favor” San Diego somehow. While I don’t believe I have ever been purposely opaque with my presentation of the data or my reasoning, this reader raised a valid point, i.e. I should have a way for readers to see exactly how the Zorgi Score is calculated and why I think each metric is important.
So, with this update, I’m presenting 3 images that explain the entire Zorgi Score calculation. After today, I’ll include a link to the pages, so that anyone can go through them. Another valid point this reader made is that if I want to improve or validate the Zorgi Score, how can I do it, if I don’t have a document like that?
Currently, the program that runs the Zorgi Score has all the values you see in the table embedded in the code. But in the long run, I plan to read the values directly from this table, so that you can be assured that what you read there is precisely how the score is calculated.
The other thing that I find compelling about the Zorgi Score is that it is based on 12 metrics, not just one. I have grown tired of the headlines that blare “LA sets record for cases in one day!” or “Hospitals in South Bay Overwhelmed!” To me, this is just clickbait. I believe the most significant metrics in a pandemic are highly interrelated, and that the focus on a single metric oversimplifies and distorts our understanding of what is going on.
One reader argued with the basic concept of using percentage change as a basis for anything, and really objected to my use of absolute numbers as a modifier of some of the scores. This person claimed that on a normalized basis, 10 new cases in Encinitas is the same as 200 new cases in San Diego City. Part of that is true. But I learned in 40 years of business that absolute numbers make a difference, too. People pay attention to them. If 10 people die, that is a tragedy for their families and friends. If 100,000 people die, that statistic takes on a different dimension. Anyone who interprets data for the general public forgets that at their peril.
A final note that I can’t emphasize enough. Most of us have been to the doctor when they ask you, “Tell me how much pain you’re in, on a scale of 1 to 10.” You know and the doctor knows that this is a purely subjective score. One person’s 10 is another person’s 5. But it gives the doctor an indication of how you feel about your situation.
The Zorgi Score is the same sort of scale. Some might argue that only statistics should be used. But the second you as a commentator say anything about those statistics, your comment is based on your subjective perception of their importance. Humans are not just mechanical repeaters of data. We are fundamentally interpreters of data. That is what hundreds of thousands of years of evolution have wired our brains to do. You can either deny that fact, and pretend that you are “100% objective,” or you can own it, and let your readers see how you think. If transparency is the issue, then I believe the second course of action is the transparent one. The first masquerades as transparency, but is in fact opacity.
A Very Quick Review of the Counties
When you look at CD4, some things might jump out at you. For example, why is Imperial County way up there at 19.0? Isn’t that too high? How can Orange be at 15.4, while LA with so many more cases, be at 9.4? Is San Diego really deserving of the lowest Zorgi Score?
To answer these questions, you’ll need to look at CD1, CD2, and CD3. CD3 has the actual base numbers for the calculations. Keep in mind that those base numbers are “modified” by other factors that are all explained in the images of the Zorgi Score calculations, so I won’t go into all of them here.
Imperial County’s score went up for four main reasons:
- Its prevalence index is 30. That’s one of the lowest indexes in the entire country. In Imperial, you just need a statistical group of 30 people to find a direct experience with COVID, while in San Luis Obispo, you’d need a group of 486 people.
- The daily fatality rate went up by 525%. That’s a giant increase, and one for something that people care about much more than cases alone.
- The percentage change in fatality day doubling decreased. This is very significant. Generally, as cases and fatalities grow, you expect doubling days to increase, even if the situation is bad. But when they plateau or decrease, that is extremely concerning.
- The IUR at the end of the period was 283%. That means that virtually every patient needing ICU care has to be turned away and sent elsewhere. That is the first sign in many cases of a hospital system that is getting overwhelmed.
Why does Orange County have such a high score?
- The rate of daily fatalities went up by 306%, second only to Imperial. At the beginning of the period, the number was 2 per day; now it’s 9.
- The fatality doubling days went up, but only by a small amount. Compare this to Riverside, where doubling days increased by 72%, or LA, where they went up by 60%.
- The daily case rate went up by 200%, and the case doubling days only went up a tiny amount, 8%. In this case, Orange got “penalized” much more than Imperial, getting a score of 2.2 compared to Imperial’s 0.1 for its 14% increase.
Is San Diego’s low score of 7.0 a reflection of my bias?
SD got penalized for its 266% increase in daily cases and its very small increase in case doubling days. But in every other metric, its numbers were quite low. In daily fatalities, there was a decrease. There was a 100% increase in fatality doubling days. Only a 17% increase in daily patients, and a 15% increase in ICU patients. And look at the final HUR and IUR — 7% and 54% respectively. Finally, San Diego increased their testing by 55% and the positivity rate went to 5.1%, well below that of LA at 11.8% and Orange at 12.6%.
As you look at the chart on CD4, I hope you’ll ask yourself a lot more questions like this. See if my thinking is justified. If you feel I’ve made a grievous miscalculation, let me know! Don’t bother with berating me for the general concept of the Zorgi Score, though. I’ve hashed that out pretty thoroughly. If you’re one of those people who gnash their teeth when you see it, just ignore it; there’s plenty of data here to satisfy you.
Thank you all for your great comments, suggestions, and error corrections. I even appreciate comments from people who totally disagree with me, as long as they’re not just trolling. I especially appreciate offers to buy me a cup of coffee, a burrito, etc. But instead of giving something to me, I’d like you to consider giving to the Equal Justice Initiative.
You can donate here.
Have a wonderful July 4th weekend, but please don’t go to inside parties, or go around people without a mask. On this July 4th, it is time we began reassessing our relation to others, particularly people who are vulnerable, whether they are people of color or those who are at higher risk for contracting COVID-19. We will fail as a nation if we value self indulgence, fear, and disrespect of others.