Driving contagion

 "My health care is none of your business!"

To an extent, sure.

Your drinking is also none of my business - under certain circumstances.

If you drink yourself into a stupor in the privacy of your own home, that's your God-given right. If you do it repeatedly and bring on cirrhosis, I might note the cost you've thereby imposed on the medical system, but people make all sorts of choices that somewhat raise their risks of needing medical care. We don't want to go down the road of routinely policing behaviors where people increase their own risk of medical harm.

But if you get behind the wheel of a car, then your drinking is very much my business.

It's not that drunk driving is murder.

If you take a gun that you believe to be loaded, point it at someone, and pull the trigger, that's attempted murder. If the gun actually is loaded and if your aim is good enough for the distance you're at, then it will be murder in fact.

You had the intent to kill the person you aimed the gun at.

Driving drunk isn't like that.

First, most drunk drivers aren't intending to kill anyone. They simply want to get somewhere, and they tell themselves that they're not that impaired.

Second, it's pretty common for someone to drive drunk and not kill anybody. With enough luck, you can make it home from the bar with not even a scratch on your car.

Of course, you do have an elevated risk of having an accident, but maybe you just end up in the ditch with no more than some bruises.

Then again, you might kill yourself and nobody else - driving solo and smashing your car into a tree.

From here

Life's a beach, and then you die

Alternative title for this post: The Sunshine Charnel House

Last week I put up a post illustrating the tremendous lag in Florida's COVID death data.

Most states take a few days to collect their data and get it posted, but after that point their older data stabilize. They're not continually updating numbers that are 10 or 20 days old.

Not Florida. They're special. It's now 25 days since I started watching their death numbers, and they're still going back and adding deaths to data that are two months old.

But they're special in another way as well, and that is simply in the amount of COVID-related death they're producing.

Along the way to illustrating that, I'll give you some pretty good evidence that VACCINES WORK, and also give a sense of how much better we might be doing with a higher vaccination rate.

Back in July I posted a comparison of the Delta surge in a collection of nine not-randomly-chosen states. (I wanted places famously having trouble, like Florida and Missouri, and the state with our highest vaccination rate - Vermont - and then through in some others, including New York, where I live, and Massachusetts, where I grew up and where I still have family.)

Overall, states with higher vaccination rates started their surges later, from a lower level, and weren't rising as fast.

That's still true, but I wanted a more comprehensive measure, that somehow gathered in both how early you started, how high you started, how high you got, and how much time you spent at a very elevated level.

What I settled on was to look at cumulative infections in a state from June 1 through August 30. I measured that by taking total infections recorded as of August 31, and subtracting  total deaths recorded as of June 1.

With that "How was your summer?" question as the main effect, I chose as my causal variable a state's level of vaccination on June 1, at the start of the mess. And I went with "fully vaccinated" rather than "at least one shot" - early in the vaccine roll-out, there was evidence that even the first shot of the mRNA vaccines did a good job of preventing infection, but that with Delta it really took both shots to get a good effect.

So: I'm looking at the relationship between:

1. What was each state's population percentage that was fully vaccinated by June 1st; and
2. What portion of each state's population got infected between then and August 31st.

And that relationship looks like this:

It certainly does appear that states with higher vaccination rates at the beginning of the summer (further to the right on the chart) also had a smaller percentage of their population test positive over the summer (lower down on the chart).

And if you fit a power function to the scatter of points, you get an R-squared of 0.627, suggesting that this single factor - how vaccinated was your state on June 1 - can explain about 63% of the variance in how many people tested positive.

The t-statistic on the vaccination variable is over 9, implying a vanishingly small chance that there is no relationship in the real world, and that the apparent relationship is just random noise falling a certain way.

And the relationship is not merely statistically significant. Its estimated size is also large enough to be meaningful. The country's overall rate of full vaccination by June 1 was 40.7%. The regression predicts that a state with that level of vaccination would see 1.43% of its population test positive over the summer. For a state with 10,000,000 people (roughly Georgia, or North Carolina, among others), that would mean about 143,000 infections over the summer.

If the state had 10 percentage points more vaccination (meaning it was at 50.7%), it's predicted positives over the summer would be 0.83% of its population. Our hypothetical state with 10,000,000 people would see only 83,000 infections - 60,000 fewer than with the average vaccination rate.

As you can see on the figure, the relationship is not linear. The lower your vax rate, the more quickly infections rise. So a state with 10 percentage points less vaccination than average (putting it at 30.7%) would expect an infection rate of 2.89%. In a state with 10,000,000 people, that's 289,000. That's 146,000 more infections than at the average vax rate.

On these charts, you might have noticed one particularly "out there" outlier.

In case you didn't, here's the second chart with the outlier highlighted.

It is 3.1 standardized residuals above its "predicted" value. The next-biggest outlier is only 1.9 standard deviations off. So Florida went into the summer with an almost average vaccination rate, but ended up really punching above its weight, with the second-highest rate of infections and an unbelievably high level given is vax status.

But that's just infections. Where Florida really excels is in COVID deaths.

Here's the chart. The horizontal axis is still percent fully vaccinated by June 1st, but the vertical is now deaths per 100,000 during June-August.

You probably noticed that outlier, but in case you didn't, I've made it easier to see.

Florida is 3.5 standardized residuals above its predicted value, wile the next largest error is 2.3 standardized residuals.

I don't know why Florida excels in death. It could be as simple as having an older population, in which case age-adjusted mortality rates would bring its numbers more in line with the other states.

Hypotheses are welcome.

The Florida slow-roll

You've probably heard in the news about what a hard time many states are having with the Delta surge, with schools having to close, ICU's overfilled, people denied care for other health problems because there's not hospital capacity, etc.

Ground Zero of this preventable catastrophe is ... Florida.

So you go to some data site like Worldometers, and you look at how things are going in Florida and:

From here, downloaded Sep. 12, 2021

Sure, they recently reached their highest daily death rate in the course of the pandemic, but since the peak on August 24th, things have been improving incredibly fast.

Here's another screenshot, focusing in on that end of the graph:

That's fantastic improvement!

Except that it's nothing more than a delay in reporting COVID-19 death data.

I would look in on the Florida page every two to four days, and it also seemed like they had recently peaked, and then things started getting better.


So on August 24th (as it happens) I started transcribing the day-by-day death stats for Florida off of Worldometers, checking back every couple of days to transcribe a new batch.


I somewhat arbitrarily chose July 30th as my starting point, opened up a new spreadsheet, and typed in the data from July 30th to August 24th.


Like on the Worldometers chart, I calculated the 7-day average, and the graph came out like this:





You can see the death rate peaking on August 11th, then falling very impressively, from 178 all the way down to 28, in just 13 days. Remarkable progress.


But let me re-scale that, so that there will be room for the later curves I'm going to have to add:





It's the identical data, just with the vertical axis allowed to rise all the way to 350, and the horizontal axis running out through September 9th.


I went back on August 27th, and the peak was a little later, and a little higher. That downturn after August 11th turned out not to be true:




That had been a Friday. I went back two days later, on Sunday, and the first two weeks of the death curve were unchanged from Friday, but the last seven days had all been bumped up:





Two days later, the whole peak had been raised. The peak now didn't come until August 16th, at 221 deaths per day.




After that, I missed five days. When I came back, the peak hadn't been pushed any later, but it had been raised, and a lot more deaths had been added to previously recorded days after that peak.





When I gathered data on September 8th, the peak had been pushed back to August 21st, and raised somewhat.





Friday, September 10th, saw a big jump and the peak not happening until August 24th.




Today, September 12th, and went and got the numbers again. Like August 27th and 29th I had a Friday-to-Sunday pair. And like that time, most of the curve was left unchanged from Friday to Sunday, with the update only affecting the last seven days of the curve.




I fully expect that if I go back on Tuesday, the entire curve will have been lifted again. The pattern over the last two weeks suggests that, even though the reported data show a decline in deaths, the underlying reality is that they're not done increasing their death rate in Florida.


I should take a moment to address one question that will be obvious to some readers: Is this a big deal? Isn't this just a function of data taking time to compile?


Maybe.


My best argument is that I've been looking at data for many states for a while (particularly for the nine states that featured in my previous post), and none of them have this obvious a rosy scenario. They report their data, and aside from a few small updates, their data don't keep drifting higher.


Just today I started the process of a small check on my impression, by gathering today's death data for New York state, starting likewise on July 30th. In a week or so, I should be able to tell if NY is doing something like what Florida has been doing.


I doubt it will be that dramatic.


In the meantime, we can count up how many deaths Florida had failed do count on August 24th, compared to their report today.





On August 24th (indicated by vertical, black lines), there were a reported 2,907 COVID-19 deaths in Florida.


Today, not quite three weeks later, that total number of reported COVID-19 deaths from July 30th through August 24th has more than doubled, to 6,187.


That particular window (through August 24th) might be just about ready to stop getting worse.


Stay tuned.

FAaFO

Yesterday's post compared the surge in the U.S. as a whole to what's going on in the U.K. 

Adrienne Martini quite reasonably noted that this is probably playing out differently in different states, since our vaccination rates are so different: in Vermont it's 75%/67% (at least one dose / fully vaccinated), while in Alabama it's 38%/34%.

(The data n vaccination are from covidactnow.org.)

So I assembled a not-totally-random collection of states with varying vaccination rates and looked at their current covid increases.

The lines are color-shaded, with pure red being the lowest vaccination rate and blue being the highest. Florida's 56% with one dose matches the national average, so it has the central shading of gray.

The states are also listed in the legend in order of vaccination rate, from worst to best.

As with yesterday's post, the daily case rates are from Worldometers, 7-day averages, read weekly.

Not surprisingly, the redder lines of the unvaccinated states:

• Start up earlier;
• Didn't get as low before starting up; and
• Are now at significantly higher levels.

But Florida, with the national average vaccination rate, is doing much worse than Mississippi and Alabama, the two least vaccinated states.

Lining up the starting times of every state's increase, you see again the rough pattern that more vaccinated states reached lower lows of infection rates and haven't been rising for as long - but again: Florida ...

The most troubling news for the blue states with higher vaccination rates is that, although we got our rates lower and held off the increase longer, we're now increasing faster than the redder states were at a comparable point in their increases:

(Though note that Vermont is only a little more than two weeks past its low, and with such a small population, it's easy for it to have "noisy" numbers. It will be "interesting" to see how this shakes out in a couple more weeks.)

Bringing the UK into the mix, we can see that none of these nine states is (yet) as bad off as the UK, though that's not saying much, because the earliest of them is still four weeks behind the UK in terms of how long they've been experiencing an increase. 

Lining up the beginnings of everyone's increase, we can see that almost every US state in the sample is above the UK line, which forms sort of an "envelope" below the US lines. Most states didnt get their cases as low, and it looks like they've been increasing faster.

That is clearer to see when we focus in on just the first seven weeks, the length of the longest US increases.

(In transferring from Excel, this chart is somehow losing two labels from its legend. As in the other charts, the all-blue solid line is Vermont, and the black dashed line is UK.)

Adding the UK to the chart of growth, we see the envelope phenomenon more clearly - every US state in the sample has its case rates growing at least as fast as what the UK saw at the comparable point in its surge:

Again focusing in on the first eight weeks:

Missouri is kind of playing footsie with the UK line over the last few weeks, and Texas is pretty close. Everyone else is noticeably above.

I think it's time for masks back on in stores, etc. I'm wondering if in-person restaurants are a good idea.

Tempering that is the very low rate of serious illness among vaccinated people.

Un-tempering that is the way that increased infection rates increase the rate at which new variants arise, giving us higher odds of encountering a version that really cares very little about your vaccination status.

(A note about Texas: it's a little hard to pick the right point for Texas's "low," because they went along a low plateau for several weeks, with transient lows within that. For yesterday's and today's posts I've chosen June 17th, as the last low point before a sustained rise.)

What failure looks like

 In case you were wondering (and you know you were), this is what failure looks like:

Figure 1

Allow me to explain.

The UK reached its low in daily new covid-19 cases back on May 5th. The U.S. reached its low back on June 21st.

This is what the two countries' experiences have been in the time since each one's low:

Figure 2

(As the labeling on the chart indicates, the data are from Worldometers. I don't know if there's a way of downloading their data, so instead I set it to show me the 7-day average of new cases, found each country's low point, and then manually found and typed up the values. To save time, I picked up the values ever week rather than every day.)

This graph has two problems in terms of comparing the countries: the action isn't happening at the same time, and the U.S. is substantially bigger than the UK, so we expect to see higher numbers here, even for an equal severity.

Still no word from Salka

This is a follow-up to a letter I sent on Saturday, reproduced here.

Dear Assemblyman Salka,

I still don't really have an answer to my question as to whether you support insurrection or democratic government. I know you're busy, but the response seems both important and straightforward.

Returning to your thought that the attempted coup may have been the fault of leftists, is [WARNING: Link to Parler] this video the work of antifa?

For anyone who doesn't want to click on a Parler link, or in case it's no longer visible, the words are excerpts from Donald Trump speaking over the last four years, backed by dramatic videos and written text.

One example: there's a clip of Mr. Trump saying, "The time for empty talk is over. Now arrives the hour of action." The clip appears to be from his inauguration in 2017, in which context it is an entirely appropriate and moving sentiment. Used in the context of this video, following a lost election where Mr. Trump has failed to overturn the result by talking, and just days before the end of his term, the use of Mr. Trump's words can reasonably be interpreted as a call for violence.

Let's take up your contention that anti-Trump forces were behind Wednesday's insurrection, and extend that idea to this video. In other words, let's assume that it was made by "antifa" or someone other than Trump supporters, in order to lure otherwise-well-intentioned Trump supporters into betraying their country by resorting to violence to overturn an election.

I think that's an absurd set of assumptions, but it's the only way to continue your act of deflecting blame for the insurrection away from Trump supporters. And so, for the sake of argument and to be as generous as possible to your position, let's go with it.

Will this provocation work into tricking good Trump supporters into participating in a violent overthrow of the United States government?

Only if they believe the election was stolen from their hero.

And that's where you come in.

Local leader playing with fire

On Wednesday evening I wrote to Assemblyman John Salka asking whether he supported democratic governance or insurrection.

On Thursday morning I received a reply from Mr. Salka’s Community Relations Director who suggested that because it was such a hefty question and a nuanced topic, it would be best if I spoke with the assemblyman directly.

Although I agree that the question was hefty, I didn’t really see the nuance in the topic. A band of the president’s supporters had swarmed the Capitol to prevent the official confirmation of the legitimate election result. My question was in essence, “Do you think this is OK?” How much nuance was entailed in answering that?

Nonetheless, I appreciated the assemblyman’s willingness to talk with a random constituent, and we set up a time for yesterday afternoon.

My expectation was that Mr. Salka would make a clear and public statement about the legitimacy of President-elect Biden’s election as part of a clear and public denunciation of the insurrection that happened on Wednesday.

The short story is that he was not willing to do that.

He purports to believe that the election was stolen.

Regarding the insurrection, he doesn't want to be hasty with any public remarks before we really know  who was involved, because he heard that "antifa" or other leftists may have been involved.

The bulk of my long reply follows.

Dear Assemblyman Salka,

I appreciate you taking the time to talk with me yesterday.

I am disturbed, however, by how firmly you cling to hopes of narratives that will exonerate your political faction, in the absence of the least bit of substantive evidence for them.

Regarding your continued devotion to election conspiracies, I note your response in our exchange about affidavits, which you brought up as some of the evidence of wrongdoing. I pointed out that in some cases the affidavits were disregarded because the affiants hadn’t participated in observer-training sessions and therefore didn’t know that the behaviors they saw were absolutely routine.

This seemed like new information for you, but it gave you no pause. You learned that there was no substance in one of the things you thought discredited the election, and you didn’t reflect, you didn’t take a moment to ponder what other pieces of “evidence” might be equally worthless. You weren’t the least bit prepared to defend the position you were arguing. Nor did you care to. You abandoned that point and retreated to the platitude that we have no way of knowing what might be discovered eventually.

In other words, you have no evidence, and you don’t know of any evidence, but you’re still willing to question the integrity of the election.

In our call I mentioned Monday’s press conference by Gabriel Sterling, the elections manager for the state of Georgia, in which he systematically debunked President Trump’s baseless claims about the Georgia election. You can see it here. (And I made sure to find a clip with the awesome sign-language interpreter whom I mentioned.)

If you’ve got time to question the integrity of the election, you should be able to find 30 minutes to watch this. After you do, you should ask yourself the following questions: