Zorgi Scores

To everyone who commented on my last update who didn’t get an answer, I apologize. A busy weekend seeing my family for the first time in 4 months prevented me from keeping up with everything. Also, there were some very significant changes in the county reporting system that required a reorganization of the reorganization that I had just finished. I basically had redo everything on the county level.

Today won’t include any deep analysis; instead, I’ll explain the changes, tell you where the data comes from, and try to explain the Zorgi Score – what it is and how it’s calculated.

Last Thursday, right after I posted the new county charts, my heart sank when I tried to upload the current day’s data. All I got was an error message. I went to the CA DHHS site, and found a message that everything was on a new platform. That meant that none of my upload scripts worked anymore. Fortunately a very generous redditor, /u/FireSpaghetti stepped up and fixed my scripts over the weekend. Not only that, but 3 more redditors offered their help as well. I was gobsmacked with all these kind offers to help.

The next problem was the data. Cases and fatalities were in one dataset; hospital data was in another. More importantly, CA added two crucial statistics to their data: actual available beds overall, and actual available ICU beds. Before this I took a wild guess at the Hospital Bed Utilization Rate (HUR) and the ICU bed Utilization Rate (IUR). Now they can be calculated exactly.

Why are the HUR and IUR such important metrics? It all goes back to the primary goals of the lockdown: a) to keep the hospital system from being overwhelmed; b) to build an adequate supply of PPE, testing supplies, etc.; and c) to build an infrastructure of test-trace-treat-isolate to prevent community outbreaks from growing. So having a more accurate HUR and IUR is critical in assessing whether we’ve accomplished goal #1, the preservation of the hospital system.

If cases rise, but hospitalizations don’t, that’s not necessarily a catastrophic scenario. The lockdown was never meant to last forever. As the economy opened up, presumably after we had achieved all the goals above, we knew cases would increase. But we want them to increase without overwhelming the hospital system and without increasing the fatality rates enormously.

I also spent a lot of time over the past 3 days incorporating this new data into the “Zorgi Score”. Today, I’d like to spend more time explaining what this is and how it’s calculated.

First, the Zorgi Score, or ZS, is not a predictive model. It is not like Rt, the current rate of transmission of the virus. Since I am not a virologist, epidemiologist, or scientist, it isn’t my place to construct a predictive model. However, I do have the credentials (even if they’ve only been established over the past 100 days) to describe what is happening now in the data, what has happened over the past few weeks, and what the trends are. That doesn’t require epidemiological knowledge; it simply requires the ability to look at raw numbers and describe which way they are moving.

So ZS is basically a numeric representation of how seriously yours truly would look into a locality to see what was going on, and how worried I personally would be if I lived there. As I explain how it’s calculated, you can make up your own mind whether you agree or disagree with my observations and how I judge them.

If you look at CD4, the small bar graph of the Zorgis as of June 28, they pretty closely conform to how I would “grade” each county relative to each other. Imperial County, with ICU’s maxed out at 300% of capacity and cases gowing 243% in one month, exemplifies a very bad situation. I suppose if I gave NYC a Zorgi at the height of their crisis, it would be somewhere around 25, so Imperial’s ZS is getting close. At the other end is San Luis Obispo, with 1 death so far, only 270 cases, an HUR of 2% and an IUR of 9%, giving SLO a ZS of 1.0. For comparison purposes, I would probably give Denmark a ZS of around 0.3.

I suspect over the next 2 weeks I’ll tinker with the ZS a bit. And I hope many of you will argue with me or at least suggest modifications. My hope is that I can develop a single metric that can give most of you a fair indicator of where things stand, a TL;DR score as it were.

OK, here’s how the Zorgi Score calculation program works. If you have no interest in this part, time to look at the charts! For the rest of you, see what you think!

The ZS is based on 12 metrics, as shown in CD3. CD3 primarily has percentage changes over a 30 day period, but CD1 and CD2 show the numbers from which those percentages are derived. In this next section, I’m just going to describe the general concept, not every detail of the calculation. However, if you’re interested in going to that level of detail, DM me and I’ll make the source code available to you.

  1. Pctg Change Daily Cases: bigger increases in cases get more points, but the increase is dependent on the number of daily cases at the end of the period.
  2. Pctg Change in Case Doubling Days: smaller increases are penalized, and negative changes are especially penalized. Doubling days should be increasing significantly, not plateauing, or worse, declining. The score is modified by including the case count at the beginning of the period.
  3. Pctg. Change in Daily Fatalities: bigger increases in fatalities get more points, but the increase is dependent on the absolute number of fatalities at the end of the period.
  4. Pctg. Change in 7 Day Rolling Total Fatalities: same concept as above. Why are fatality counts included twice, although in somewhat different forms? Because this is the most important “result” of the pandemic. If no one died, we wouldn’t be nearly as worried.
  5. Pctg. Change in Fatality doubling Days: same concept as case doubling days, but the penalty is modified by the 7 day fatality count at the end of the period.
  6. Pctg. Change in Daily Patients: bigger increases in patient count get more points but the increase is dependent on the absolute number of patients at period end
  7. Pctg. Change in ICU Patients: same concept as daily patients above.
  8. HUR at end of period: score is based on the total HUR but if cases have increased a lot, there is a multiplier from 10% to 20%.
  9. IUR at end of period: same as HUR above, but multipliers range from 10% to 30%. This is why, for example, Imperial County gets a ZS of 7.2.
  10. Publication of testing data: the three counties that do this get a deduction of 2 points. This is partially to make up for the extra points they might get from the next two metrics. But it also “rewards” transparency. Counties that are transparent have a better chance, I believe, of containing the virus.
  11. Pctg. change in daily testing: penalties for drops in testing; negative scores for testing increases > 50%
  12. We would expect the positivity rate to decline, so if it’s < 0 the county gets a negative ZS. If it goes up, they can get from 1 to 2 points.

Tomorrow may be nothing more than charts with no commentary. I need a bit of time to reorganize the State data, and then work my way down to cities and zip codes.

Personally, I think we’ll be in this mess for months, maybe as long as a year or two. So I’m trying to organize my work so I can stay with you for the duration.

I can’t begin to express how much I appreciate all the feedback from everyone. I have really been overwhelmed at the generosity of your comments and your willingness to dive into all these numbers, ask great questions, point out my mistakes and challenge me where necessary. If I don’t get a chance to respond to you directly, it’s just because I ran out of time.

Have a terrific, safe, and healthy week everyone!

Leave a Reply

Your email address will not be published. Required fields are marked *

 

This site uses Akismet to reduce spam. Learn how your comment data is processed.