Case study: Michigan

Michigan currently has the highest deaths per day (per capita) of any US state.

It is an instructive case in both the effectiveness of lockdowns as well as the non-seasonality of COVID
Michigan, like many states in spring of 2020, had an explosive first wave. Lockdowns were put in place but not soon enough to prevent the first wave from generating the highest deaths per day of all three weeks

But notice how the sharp uptick in deaths is followed very quickly
by a sharp downtick.

This is a shape similar to what Belgium experienced -- a very "pointy" (steep and high) initial wave of deaths

This is a common pattern that we see when:
a. There is a comprehensive attribution of COVID deaths -- for instance Belgium confirmed COVID cases
on the basis of clinical symptom presentation. Not necessarily a COVID PCR test. It also tested comprehensively

and

b. When fairly stringent "lockdown" measures are put in place to contain the disease
Compare the shape of Belgium and Michigan's first waves to that of Sweden's first wave. Sweden's first wave is nowhere near as "pointy" and has a long tail

This is what we see when relatively few mitigations (lockdown) are put in place. A peak equal to (or even lower)
than that of "lockdown" places. But a duration much, much longer

Back to Michigan. Like many places, Michiganders are suffering from lockdown fatigue

This lead to a resurgence in infections beginning in mid-September and a resurgence in deaths starting about a month later
This resurgence was followed by the re-implementation of lockdown orders, but they were tame compared to the first wave.

Not surprising given that COVID deniers/Trump supporters had tried to kidnap and kill Michigan's governor over the first set of lockdown orders
This tameness resulted in a second wave that looks a lot like Sweden's first wave.

Fat, long and nowhere near ready to go quickly and quietly into the night
But it went down nonetheless. No doubt a combination of restrictions as well as the infection-suppressing effect of people getting out of the workplace and schools over the holidays

Note how the dip in infections and deaths after Michigan's second wave perfectly correlates with
the traditional rise in Michigan's influenza seasons (dashed black line on second graph at bottom)

Still think COVID is seasonal, like influenza?
Michigan's second wave was then followed by a third wave -- which started WELL after the traditional influenza season would be on the outs.

Infections in the third wave have been declining but deaths per day are only now dropping off

A month before summer
RE: Lockdown fatigue

In Michigan's case this can be seen by the mobility graph (dashed red line in bottom graph) -- showing a sharp slowdown in interactions

That slowdown reverses mid-summer and by autumn people are mixing it up

As is COVID

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More from @greg_travis

4 May
1/Why there is no such thing as a "healthy" immune system -- Tesla Autopilot version

I have a Tesla. Best car I've ever owned, by far.

Only flaw is that Elon Musk is a complete asshole.

Part of his assholery is selling a fantasy that a car can drive itself
2/It can't. And if you give the the chance to drive itself, it's going to kill you

The autopilot in my car is made up of hardware -- the stuff that drives the brakes, the steering wheel, the accelerator, the battery

And the "eyes" -- the cameras that look around for threats
3/All of that is pretty straight forward. We've known how to make motors, servos, electronics -- even ccd cameras for a long time

The problem is that all of that is brought together and controlled by...

Software
Read 16 tweets
4 May
There are those of us who have worked with "the data" for some time, but who are not data scientists

And there are those who know all about data, statistical analysis, etc. but who have little "boots on the ground" experience

There is a natural antagonism between the camps
Which is counter productive

The boots on the ground folks, of which I include myself, need to get over their natural fear of being talked down to by the scientists

And the scientists need to acknowledge that those of us who have been slogging through the shit for decades...
probably gained some insight along the way

Demographic data and health data is UNBELIEVABLY dirty, balkanized and full of characteristics that make it very difficult to coalesce -- example: longitudinal data recorded across different time frames
Read 9 tweets
3 May
1/Some quick stats (consider these tailings off work I've done today)

Fattest county/city in the United States:
Owsley County, KY (men), Issaquena County, MS (women)

Skinniest:
San Francisco County, CA (men) (except when I am visiting my mother), Falls Church (women)
2/Least physically active county/city in the United States:
Owsley County, KY (men), Issaquena County, MS (women)

Most:
Teton County, WY (men), Routt County, CO (women)
3/Shortest life expectancy:
McDowell County, WV (men), Perry County, KY (female)

Longest:
Fairfax County, VA (men), Marin County, CA (female)
Read 6 tweets
3 May
Can anyone name a single pollyannish prediction about a country or region by @MLevitt_NP2013 that didn't turn into bodies stacked three deep in mass graves?

Because I am very worried about India
Seriously, help me out

Israel? Predict: 10, Actual: 6,366
USA? P: 170K, A: 600K
Brazil? P 98K, A: 408K
Italy? P: 20K, A: 121K
Peru? P: 14K, A: 62K
Russia? P: 13K, A: 109K
Bangladesh? P: 5K, A: 12K
Indonesia? P: 3K, A: 46K
Argentinia? P: 2K, A: 64K
Ukraine? P: 2K, A: 47K
Nigeria? P 840, A: 2063
Armenia? P: 666, A: 4K
Sudan? P: 615, A: 2350
Los Angeles? P: 3506, A: 24K
El Paso? P: 190, A: 2658
India? P: 33K, A: 220K
World: P: 700K, A: 3.2 Million
Read 4 tweets
3 May
1/COVID and Obesity

For a primer on BMI and the ranges used here, see:

Objective: I wanted to find a way to easily understand the relationship between body mass index (BMI) and COVID severity
2/Executive summary:

The underweight represent 1.5% of the US population and 2.4% of those admitted to hospital

Normal: 26.10% of US pop & 19.70% of those admitted

Overweight: 30.00% and 27.80% admitted

Obese: 33.20% and 36.20%

Severely obese: 9.2% and 14% Image
3/In other words:

Those with a normal or overweight BMI are admitted to hospital at a lower rate than their population prevalence (75% and 92% respectively)

The underweight, the obese and the severely obese are admitted in excess of their population prevalence (160%/109%/152%)
Read 7 tweets
2 May
1/BMI/Obesity primer

In advance of talking some more about this in a bit, I thought it would be helpful to know what we mean in a clinical sense when we say things like "Obese, Overweight, Underweight and Normal"
2/These clinical categories are calculated from a very simple formula:

Your Gender -> How much you weigh -> How tall you are

The result is something called the "Body Mass Index," or BMI
3/BMI is divided into ranges:

A BMI of 0-18.5 is considered underweight
18.6-24.9 is normal
25-29.9 is overweight
30+ is obese
Read 7 tweets

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