COVID-19 Ten County Roundup – May 3, 2020
This is the first weekly county summary. Every Sunday, I’ll try to post an update to this in lieu of the Daily Update. I’ll try to get it posted before noon.
This will also change the schedule of the Daily Update. Currently, I wait on SD County to release their data, supposedly around 4pm, but often not until 5pm. I don’t like rushing to get the Update posted by 6pm, and I’ve made some minor errors doing it. So I’m giving myself a bit more relaxed schedule by posting the Daily Update from now on in the morning of the following day.
The list of 10 counties is not meant to be a thorough picture of how things are going in the state overall, since there are 58 counties. I tried to pick counties that are major population centers, and some that are within close travel distance from San Diego.
The quality of the public health department dashboards varies wildly. Some are very thorough, others leave out a ton of information. In some cases, I’ve had to extrapolate numbers from percentages on the public health department websites.
And just to repeat, case and testing numbers to begin with have some fuzziness in them. Someone who is sick and was told to stay at home but never received a test, is probably not in the “covid system.” The bottom line is this: look at trends rather than individual days. That’s why I use so many charts: they help visualize what’s taking place without getting lost in the noise of daily data.
RECENT INDICATORS, PAGE 1 & 2
These pages compare some of the main indicators from a snapshot 3 weeks ago vs. May 1. I used colors to create a visual effect of how the trouble spots have shifted: orange = serious; yellow = 2nd most serious; green = 3rd most serious.
Looking at April 11, The serious areas are more or less split between SF and LA. Compare that to May 1, where the focus has clearly shifted to LA. This is reflected in many of the statistics over just 3 weeks: cumulative cases almost tripled, from 8,823 to 24,306; fatalities went up around 500%, etc. The case doubling rate in LA improved only slightly, from 12.9 days to 14.5 days. The fatality doubling rate for LA is about the same – 13.1 days.
CASE DATA BY RACE, PAGE 3 & 4, Notes & Sources on last 2 pages
That COVID-19 exacts an extra toll on non-white people has been documented hundreds of times. I was curious how that would look in a quantified way for the 10 counties. I encountered some big problems. First, Kern and Riverside had no data by race, at least in an easy place to find. Second, counties varied quite a bit in their presentation of the data. Some counties give percentages of cases; some exact numbers. Explanations are in the notes. Third, all the counties that report this data have a substantial number of unidentified cases. While I suspect that most of these are non-white, I had to exclude those from the overall case numbers.
The first chart is non-normalized. Stack #1 shows the distribution of cases by county for non-whites only, adding up to around 18,000 cases. It’s clear from this that San Diego, LA, Orange, and Alameda have the most cases. Stack #2 shows what the case distribution would look like if the number of cases for non-whites was exactly proportional to their percentage of the population for that county. Stack #3 shows the “surplus cases,” i.e. it quantifies the extra burden placed on non-whites. In other words, just over 6,000 cases.
The second chart has the same information, but normalized to cases per 100,000 people. This is helpful to visualize where the disparities are more severe in terms of proportion of the population, not just absolute numbers. For example, San Francisco, which is a sliver in the first chart, is a big chunk in the second.
I am implying NOTHING about the cause of this disparity. That’s outside my area of expertise. But one thing is very clear: we cannot discuss the cost of COVID-19, economic and social, without taking this gross disparity into account.
CUMULATIVE & DAILY CASES, PAGE 5, 6, 7
My son and daughter and their children live in L.A., so the data here give me pause. Though we desperately want to hug them, LA is not looking good. The cumulative cases per 100K have gone up about 700% in a month, and the trend line is rising sharply. SF is up there, but starting to flatten out a bit. Riverside is also a concern, moving from 20 to 150, more than 700% in a month.
Daily new cases on a 3 day moving average don’t look good for LA either. LA, Riverside, and San Diego are #1, 2, and 3, but LA’s rate is almost double that of Riverside and more than double San Diego’s.
All this is reflected in the Case Doubling Days. Anything less than 20 days is of great concern and LA, Orange, San Diego, and Kern are all in that territory. Santa Clara and Sacramento, on the other hand, are moving out of the woods.
FATALITIES, PAGE 8, 9
The Case Fatality Rate (CFR) can be calculated all sorts of ways, but the only one that’s remotely accurate is the one when the pandemic is history. I’ve used a ratio of fatalities today vs. cases 7 days ago. If I were to use cases 2 days ago, the CFR would go way down; if I were to use 15 days ago, it would go up. There is no perfect model. The main thing is to use the same calculation for all counties and over time.
Los Angeles once again is way on top here, and it’s possible this is also a reflection of the high percentage of non-white cases, where access to medical care, income disparity and other social factors manifest.
HOSPITALIZATIONS, PAGE 10
The state-provided dataset includes hospitalizations only for the current day, so there are no cumulative numbers, and they can’t be derived from this data. The chart on this shows both confirmed and suspected COVID-19 hospitalizations per 100,000 people. This gives us a very rough idea of the impact of the epidemic on a county’s hospital system. And once again, LA and Riverside are the danger spots.
TESTING, PAGE 11
Testing data is not part of the state-provided dataset, so I had to dig it up, where I could, from the Public Health Departments. Only 6 of the counties had it available in any sort of usable form.
It’s encouraging to note that all six counties show a steady increase, with very similar trend lines. The last point has relevance to LA’s climbing case numbers. If testing in LA were increasing much faster than other counties, that would partially explain why LA is so far ahead of them in cases per 100K. But that’s clearly not the case.
I’m hoping to see a change in a good direction in LA, though, because testing is now available to anyone, symptomatic or not.
Please let me know if you see any errors. If I can correct them easily, I’ll do it on this post; otherwise, you’ll see the corrections on the next 10 County Update on May 10. Also, I welcome suggestions and comments on the presentations. If you see something I can do to make the data clearer, let me know!
Stay well, everyone!