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Comparing the US’s coronavirus response with its Western European peer group

Comparing the US’s coronavirus response with its Western European peer group

Western Europe is a reasonable peer group of countries against which to compare the US response to coronavirus. The 5 largest countries in Western Europe in particular – in order, Germany, the UK, France, Italy, and Spain – together have a population of about 324 million, vs. 332 million for the US.

So let’s take a look at this peer group of European States vs. the United States in terms of cases, deaths, and testing.

One significant difference is that Italy was among the first countries struck in force by the pandemic. In early March it was common to note that the US was “two weeks behind Italy” in the number of cases. The other four all saw their pandemics start a few days to one week ahead of the US. To accomodate that, the below calculations compare each country 58 days after they reached 100 cases nationwide.

As of yesterday, according the Covid Tracking Project, the US had 1,104,161 cases, or 18.7% more than the 5 big Western European States, which had a combined total of 930,231:

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A note about the weekly and monthly economic data

A note about the weekly and monthly economic data

For the past month or so, with the exception of the weekly catastrophe of new jobless claims it hasn’t been very important to keep track of the economic data. Now that it is May, that will start to change with the weekly data as of next week (reporting on this week). The monthly data fo May, of course, won’t be reported for until June starts.

That’s because the month of April was fully involved in the pandemic crisis. So with the month over month May data we will be able to see if the economy is beginning to stabilize at a lower level, sink even further as more second-order effects ripple out from the epicenter, or perhaps even rebound.

With the exception of finding out what happened to wages during April (because of all of the reports of wage cuts), even this Friday’s employment report, while surely catastrophic, won’t be that important looking forward.

The only monthly data from April that has been reported recently of note is that comparing personal income and spending (which was released last Thursday).

The personal saving rate skyrocketed by over 60% compared with February:

Figure 1

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Coronavirus dashboard for May 4: new infections, deaths continue slow decline

Coronavirus dashboard for May 4: new infections, deaths continue slow decline

Here is the update through yesterday (May 3).

As usual, significant developments are in italics. The bottom line is the same as several days ago: trends in new infections, deaths, and in testing have all turned positive – if not positive enough. But the good news remains primarily a NY story.

I have discontinued giving the % increases day/day in infections and deaths. They were included when important to determine if the US was “bending the curve.” The two issues now are (1) whether any States (beyond the least populated rural or isolated States) can “crush the curve;” and, sadly, (2) whether those States that have “reopened” see a renewed increase in the growth of cases and deaths – and whether customers in those States largely stay away from the reopened businesses. 

Number of new and total reported Infections (from Johns Hopkins via arcgis.com and 91-divoc.com):

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Coronavirus dashboard for May 2: good news, but still primarily due to NY

Coronavirus dashboard for May 2: good news, but still primarily due to NY

Here is the update through yesterday (May 1).

As usual, significant developments are in italics. The trends in new infections, deaths, and in testing have all turned positive – if not positive enough. But the good news remains primarily a NY story.

I have discontinued giving the % increases day/day in infections and deaths. They were included when important to determine if the US was “bending the curve.” The issue now is whether any States (beyond the least populated rural or isolated States) can “crush the curve.”<

Number of new and total reported Infections (from Johns Hopkins via arcgis.com and 91-divoc.com): 
Number: 34,129, total 1,104,161 (vs. day/day high of +36,161 on April 24)

Figure 1

There has been a slight decrease in the number of new cases in the US. The US has the worst record in the world, by far, with no sign of any big decrease.

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Coronavirus dashboard for April 30: the US has the worst record in the world, by far

Coronavirus dashboard for April 30: the US has the worst record in the world, by far

Here is the update through yesterday (April 29):

Number of new and total reported Infections (from Johns Hopkins via arcgis.com and 91-divoc.com):

    • Number: South Korea: ZERO (4 detected from incoming flights at airport)
    • Number: Taiwan: ZERO
    • Number: Vietnam: ZERO
    • Number: Germany: 1,627 (up from 988 on April 27; 3 day average of 1,256 down -81.5% from 6,790 peak on April 1-3) (highlighted in graph below)
    • Number: US: up +24,114 to 1,040,488 (vs. day/day high of +36,161 on April 24; 3 day average of 24,709, down -26.1% from 33,437 peak on April 8-10)(#1 in the world, 5.7x #2 Spain)(outlier at top of graph below)

Figure 1

There has been a slight decrease in the number of new cases in the US. The US has the worst record in the world, by far.
I have discontinued tracking the rate of new cases and deaths each day. That was to determine if we were “bending the curve.” We were. the issue now is whether cases will continue to go down in any significant way or not.

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The widely followed IHME model of coronavirus cases has been much too optimistic

The widely followed IHME model of coronavirus cases has been much too optimistic

 

The IHME model by the University of Washington has gotten a lot of attention in the past month, most likely because it has always forecast a much lower number of total deaths caused by coronavirus than, for example the Imperial College of London’s model, that forecast over 1 million US deaths if no quarantine measures were put in place.

But that model has come in for a lot of criticism, and I have come to distrust it. Its main feature – and biggest shortcoming in my opinion – is that it assumes that the US path will follow that of China and South Korea, where after the peak is reached, the disease ramps down just as quickly as it ramped up.

Here is what the model predicts today: a quick ramp-down in new deaths to below 500/day no later than the 3rd week of May (and most likely before May 10), and virtually no deaths at all after June 1:

Figure 1

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Abbreviated coronavirus dashboard for April 29: actual good news on testing, deaths 

Abbreviated coronavirus dashboard for April 29: actual good news on testing,deaths 

Here is the update through yesterday (April 28). This is somewhat abbreviated since I want to post about a couple of other items.

As usual, new items of significance are in italics. Yesterday was the 3rd day in a row of not just significantly increased testing, but actual lower number of infections found by that testing – a very good sign. The 7 day average of deaths also moved into significant decline. At least those States which are sticking with a “crush the curve” strategy appear to be turning the corner.

Here are yesterday’s numbers.

Number and rate of increase of Reported Infections (from Johns Hopkins via arcgis.com)
    • Number: up +24,114 to 1,012,583 (vs. day/day high of +36,161 on April 24)

Figure 1

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Coronavirus dashboard for April 28: good news on testing at least

Coronavirus dashboard for April 28: good news on testing at least

Here is the update through yesterday (April 27).

s usual, significant developments are in italics. There were some late-reporting States for testing yesterday, so the initially discouraging number was actually pretty good. We are now seeing much more testing, and for the last two days an actual decrease in new infections being found. One problem is that this is mainly due to one State: New York. 

 

Discouragingly, 11 States have decided to at least partly “open up.” A few of these – Alaska and Idaho – a mainly rural and sparsely populated, with near single-digit new cases, so limited openings with social distancing restrictions can be justified. But most of the rest are recalcitrant States from the Confederacy that were among the last to issue stay-at-home orders. These will now be watched for a resurgence in cases over the next several weeks.

Here are yesterday’s numbers.

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The biochemistry of how COVID-19 attacks the body: a synopsis of the medical studies

The biochemistry of how COVID-19 attacks the body: a synopsis of the medical studies

I’ve been doing some reading over the past several weeks, trying to understand how the COVID-19 virus attacks the human body. Below are quotes I found most noteworthy or interesting from these articles.
In essence, they indicate that biochemically the novel coronavirus, COVID-19, mainly binds to the ACE-2 receptor of cell surfaces to gain entrance. These are most prevalent in nasal and mucus cells, alveoli (oxygen-exchanging cells in the longs), and some cells lining the small intestine, which explains why the disease may start as abdominal discomfort in many patients. There are some conditions, especially high blood pressure and diabetes – or, possibly, medications for those conditions – which cause these ACE-2 receptors to be more expressed.
Conversely, nicotine may cause a decline in the expression of ACE-2 receptors, acting to protect against the disease. But if smokers do get the disease and are admitted to the hospital, the cessation of nicotine ingestion may lead to a rebound in those receptors, worsening the condition.
The virus also appears to bind to an iron ion in hemoglobin in red blood cells, causing blood clots and also preventing those cells from carrying oxygen to the rest of the body. This may explain the horribly low blood oxygen levels seen in many patients; and also why some otherwise asymptomatic patients suffer heart attacks or strokes.
Even more alarming, like HIV the coronavirus appears to attack the immune system itself, binding to a different receptor, called CD147, on the T-cell leukocytes that are sent to attack it, disabling them and causing the immune system to be suppressed. It seems to be when the immune system is overwhelmed in this way (in about 10 days on average) that the disease suddenly takes a deadly turn.
[One obvious note of caution: since the following research was necessarily conducted on the fly, in the teeth of a raging pandemic, it would not be a surprise if ultimately of much of it were  found, if not wrong, at least significantly in error.]
Here are the synopses:

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What the ratio of positive tests to total test results for coronavirus is telling us

What the ratio of positive tests to total test results for coronavirus is telling us

I want to make a comment about the value of measuring the number of coronavirus tests being administered vs. the number of cases found by those tests. This is because a few people are claiming that the ratio of positive tests to total tests does not give us value. Rather, they claim, increased testing simply reveals increased infections.
I will make a bold, unqualified claim: they’re wrong. Here’s why.
Empirically, about 6 weeks ago I looked at the South Korea data and realized that the peak was in once the percentage of positives to total tests started to decline. I suggested tracking that to Bill McBride, who has since included it in his daily testing updates. It turns out this ratio has a name in the epidemiological literature (which, sorry, I’ve forgotten), and is regarded as a useful “second-best” type of measurement.
Imagine we are tasked with finding out whether there is a constant ratio of red M&M’s vs. the entire batch produced. We are asked to figure out if the ratio is the same, or if the number of red M&M’s is increasing or decreasing.
There are 1000 M&M’s in a batch. We can only sample 100 of them. So we pull 100 at random every day, and count the number of red M&M’s in our sample. Depending on whether the number of red M&M’s in our sample is increasing, decreasing, or staying the same, we conclude that the total number of red M&M’s in the entire batch is likewise increasing, decreasing, or staying the same.

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