A quick look at CA Covid data

Citing a spike in new coronavirus cases Governor Newsom yesterday announced new CA restrictions. In his press conference last Friday he encouraged listeners to download the state’s raw data and play with it, so I did.

Wanting to understand the spike, I grouped the data for each county (it’s reported by county in their files) into totals per day for the state. Heeding a cautionary note about irregularities in daily reporting, I calculated 7-day averages for new daily cases and new daily deaths. It should go without saying that “new daily cases” means new cases known among people tested, and therefore says nothing about the base rate in the population. The number of daily tests in CA grows roughly exponentially. Each day we do more tests than we did the previous day. This increase in daily testing is apparent in the blue line in the below chart. On the same chart I plotted 7-day averages of daily reported deaths. 7-day-averaged daily new deaths peaked on Apr 24 and have declined roughly steadily since.

covid datat plot

In the next chart I plotted total tests (not daily new tests) and total cases vs. time. The left vertical axis and the red line indicate total known cases. The blue line, which rises similarly, indicates the total test count.

The conditions of people tested has likely changed over time. Initially, tests were only available to sick people. Therefore we should expect a change in the ratio of deaths per person tested, and that is the case. To make the numbers more understandable, I plotted deaths per 1000 known cases over time (red line below). That rate peaks at about May 1, stays roughly even for three weeks, then drops by 50% at the end of June.

Another look at the change in nature of people being tested is the plot of cases per test (blue line below), or, as plotted here for easier reading, cases per 1000 tests. Note this plot is of a daily ratio. For the first two weeks of the plot (the last two weeks of March) both the numerator and denominator of the values forming the plotted values are small. So the first few weeks of data are unreliable. On Apr 3, CA performed three times as many tests as on the previous day (113687 vs. 35267) but the increase in positive tests between Apr 3 and 4 was small. Therefore there is an abrupt drop in the cases (positive tests) per 1000 tests on Apr 3.

covid datat plot

I see nothing in these plots that I would describe as a spike. I’ll leave any further interpretation to readers. The data plotted here is exactly as taken from data.ca.gov with the exception of one data point. The total test count in the ca.gov data for May 27 is obviously wrong. It is much higher than the total at May 28, and totals (as opposed to daily new values) cannot decrease. The value used in my plots is interpolated from the preceding and following days. Email me or leave a comment if you’d like a copy of my Excel file that combines data from several of the ca.gov files, groups the county data together, calculates the 7-day averages for smoothing, and shows the source of the plots shown here.

  1. #1 by ok on July 3, 2020 - 7:32 am

    Hi Bill. It would be interesting to see similar plots for the largest urban counties; the counties with protests; and the counties with beaches. Have a good day……neal

    • #2 by Bill Storage on July 3, 2020 - 9:12 pm

      Hi Neal. Yes I wanted to plot that too. Unfortunately CA provides deaths and cases by county but tests for the entire state only. So based on the data they supply there’s no way to see any change in positive tests per test given at the county level. Gov. Newsom’s press conference stated that all collected data was available on ca.gov. If the state truly does not know daily tests by county, I can’t imagine what grounds could exist for determining which counties were experiencing the reported spike. I plotted deaths by county for LA and Orange counties. The shape of those plots is identical to that the the same plot for the entire state.

  2. #3 by rick brakeman on July 3, 2020 - 12:03 pm

    We find the similar patterns in other states as Bill just noted in Calif. “Cases” are being detected rapidly as large numbers are testing: People are getting home test kits; Labs are soliciting people to get tested; Our doctors of course are testing when signs, symptoms or suspicions indicate.

    State to state death trends per day are not “spiking.”

    The most important metric that is ignored by fake science, fake experts and fake news is the numbers of covid19 taking up hospital beds, and the remaining capacity – these numbers by hospital and by health bureau within each state..

    The data has become so corrupt by double counting, and counting every confirmed or suspected covid19 situation as a covid19 death that in ordinary circumstances would be attributed to old age (depending upon the state, 50%-90% of deaths are rest home occupants) or some other ill, and admitted misinformation or deceit by the federal health bureau (CDC); we will never know the facts and so truth remain unrefined.

    The governor of Calif didn’t believe that anyone would take up his offer to read the data. Thank you, Bill, for doing your part to expose the fakes.

    “Facts are stubborn things, but statistics are pliable,” said Mark Twain

    • #4 by Bill Storage on July 3, 2020 - 9:21 pm

      Hi Rick. Given the position you’ve stated above, you’ll likely be interested in my reply to Neal above. I considered the possibility that the officials simply didn’t understand that “new cases” doesn’t mean new disease transmissions and were inferring a spike from an increase in positive tests with corresponding positives. That can’t be the case either, as is apparent from the 2nd chart above. Beats me.

      • #5 by rick brakeman on July 4, 2020 - 4:24 am

        Indeed! Beats me too. Until I realize that this is, to some, a political opportunity to change the leadership of this country. More could be said but may be off topic (or is it?)

        On point: Unlike the Multidisciplinarian, the great experts leading states and their health bureaus, either wittingly or maliciously, see one grain of black sand on shore and say that represents the color of the whole beach. And so go the experts.

  3. #6 by atnorton54 on July 4, 2020 - 5:56 pm

    Hi Bill! Long time! Aaron here. Looking at the last graph you presented, it appears that there has been an increase from around 50 cases per 1000 to around 70 cases per 1000. That increase of ~20 per 1000 could rightly be considered a spike, no? That’s a large percentage increase in the daily positivity rate (around 40%). Multiple that across millions of people in CA and that seems like a problem. What am I missing in my assessment?

    • #7 by Rick Brakeman on July 4, 2020 - 6:18 pm

      You rightly ask that it seems like a problem, i.e. 20%-40% increase in cases. Absent signs, symptoms or discomfort, “cases” may not be known without testing. Rapidly increasing availability and use of testing makes many more “cases” known, whether illness is felt in each case.

      If I were in the service of a health bureau, my main metrics of concern would be, firstly, available beds trend for my jurisdiction; second, death count direction.

      If there is good news here, it’s that the widespread testing reveals that the death rate is getting lower as more becomes known.

    • #8 by Bill Storage on July 6, 2020 - 10:17 pm

      Hey Aaron – wonderful to hear from you. You may be correct about the rise in cases per test at the end of June being interpreted as a spike. I’m not seeing an increase from 50 to 70 though. Calculating that average from the raw CA data and rounding to an integer, the minimum value was 52. That was from 6/15 to 6/22. It then increased to 55 for the period 6/29 – 7/1. So if you take the minimum value (lasted 7 days) as a baseline, then the last value reported was 6% higher than the baseline (also 6% above absolute minimum). I wouldn’t call it a spike, but some might see it that way. For kicks I just calculated a weighted average value of cases/thousand tests, weighted according to the total tests per day. That results in 56, a number much lower than the apparent average on that chart (since the high charted values were during a period of far fewer tests). I’ll download the data in a week or so, run the code, and see if there’s a trend there.

  4. #9 by atnorton54 on July 4, 2020 - 5:58 pm

    (Or perhaps it’s more like 50 to 60 per 1000? A 20% increase?)

  5. #10 by PMOT on August 3, 2020 - 7:58 am

    If it was such a serious emergency then (presumably?) the politicians would legalize the best known medicine for this virus. Or maybe this is more about creating drama than caring for the public

    • #11 by rick brakeman on August 3, 2020 - 8:15 am

      Big medicine (the federal and state health bureaus, private and corporate hospitals, etc.) has betrayed us and bully the Rx people into bullying doctors away from HCQ which – at least observationally – is helpful to the point of fast recovery if the hospital hasn’t damaged the patient by premature or inappropriate ventilation. At best, the “experts” are at a loss to understand the big picture they try to solve; at worst, they are hostile in their actions. We can add Big Medicine to the list of “Bigs” that damage the domain that they pose as stewards of (Big Union, Big Business, Big Government, Big Charity,…).

  6. #12 by rick brakeman on August 3, 2020 - 8:20 am

    Current info about the covid in the attached graph, depicting: Lots and lots of tests keep getting done (unprecedented amount of testing), so also known positives; and death count per day stays low, and well below the April peak.

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