As Americans line up by the thousands at food banks, farmers are dumping gallons of milk and smashing eggs. Wall Street Journal reporter Jesse Newman explains why America's food supply chain isn't built for the coronavirus era. Aired on 04/13/2020.

Basically all of the suppliers to restaurants and suppliers to stadiums and concerts and bars have either stalled or are non-existent, and making the transition to supplying grocers only is difficult -- in addition to being costly.

And there are other issues related to COVID-19. For example, a large pork processing plant in North Dakota had to shut down when 300 of the workers contracted Coronavirus.

You may not know about how the great plains states have become Concentrated Animal Feeding Operations (CAFO). We Californians have a little bit of this in the Salinas Valley. From Northern Texas, through Oklahoma, Kansas, Nebraska, Colorado, Wyoming, Montana and the Dakotas have some region or area where these operations are located. About 70 miles north of Denver, there is an area around Greeley, Colorado where the air smells and burns your eyes.

Here is what Tobin Abasi and the band Animals As Leaders has to say. The song is called "CAFO".

From Robert Reich, 1 April 2019.

Well I'll be ... look-ie there (en francaise, "voila")

Jesse discusses the fact that not only did Donald Trump just fire the Inspector General overseeing the $2 Trillion relief funds, he (as well as his children & associates) are also investors in a pharmaceutical manufacturer named Sanofi, which makes Hydroxychloroquine!

Because that's how you "drain the swamp".

Yikes. Up 73. The model now has the a value at 15.0558 and the b value is .0893565, settling into just below 9%, however still exponential.

This spike might be due to Homes of concentrated Elderly that are getting hard hit everywhere, not just California.

Here are the model's updated 14 and 21 day predictions:

39,768 = 797×15.0558×(1+0.0893565)^14

72,399 = 797×15.0558×(1+0.0893565)^21

Potentially almost 72,400 by early May unless we continue bending the curve.

Just to say, the assumptions made in the above model, are as follows:

1. The a value accurately measures the number of known positive cases, ie. 1 out of 15. Thus multiplying the a value to 797 is attempting to include the unknown cases.
2. The b value consists of a rate of increase that is both exponential and based upon a consistent percentage rate. The model has been dropping the percentage over the last 10 days, however, the rate of growth is still exponential.

If for instance we assume that only 50% of cases are known (a = 2), and the percentage growth is 8% (1+b = 1.08), then the prediction over the next 3 weeks is ...

8023 = 797×2×(1+0.08)^21

Another calculation I am using is the Gaussian regression model based upon increases per day. I sum over the interval where the model predicts 0.5 per day (currently at x=191 -- about 4.8 months from now) and I get 10,289 cases.

Because in chaos, they can steal.

Click here for an article describing how 51 cases of recovered patients might have caught or reactivated Coronavirus AGAIN !!

At a briefing earlier this week, Jeong Eun-kyeong, director-general of the Korean Centers for Disease Control and Prevention, said that 51 people previously thought to have recovered completely tested positive again shortly after they got out of quarantine. Jeong said the virus may have been “reactivated” in some fashion since it was in the CDC’s judgment too early after their recovery for them to have been reinfected.

[SOURCE: Donald Kirk | Daily Beast | 9 April 2020]

Click here for an article in Paleoleap that indicates just how faulty the research was that beget the mantra of "low-salt" good, "high-salt" bad.

The first study in 1972, was based upon feeding rats the equivalent (by body weight) of 250 teaspoons of salt per day. The second study in 1988 was an inter-population study that included 4 outliers (out of 52 groups) which influenced the weak correlation from positive to negative, and

...even the Intersalt study itself admitted that without the four outliers, “both regression analyses showed no significant associations of sodium with median systolic pressure” ...

By 1998, studies were increasingly finding fewer and fewer benefits of salt reduction, even at a public health level undetectable to individual subjects. In August of that year, Gary Taubes wrote an article summarizing the controversy and explaining why the link between salt and blood pressure (if it exists) is so difficult to determine. First of all, blood pressure is regulated by a complex homeostatic system: sodium affects it, but so do potassium, calcium, caloric intake, sex, age, and race. This introduces numerous complications to any study claiming that salt intake alone is responsible for high blood pressure.

Second, the early studies such as INTERSALT are all “ecologic” studies, comparing members of different populations (Yanomami Indians compared to Finns compared to Vietnamese compared to Americans). These studies appear to show that societies with a low salt intake have lower blood pressure, but they can’t account for the results of intrapopulation studies, which compare individuals within a certain population (white middle-aged men in Toronto). Within more homogenous population groups, researchers could find no direct relationship between dietary salt and higher blood pressure. Taking out the confounding factors that plague ecologic studies seemed to also reduce or eliminate the correlation between salt intake and blood pressure.

On the Stephanie Miller show:

Don't give up the ship.

Jumped up 54 from yesterday. I waited a few days since my last update because these crude models that I am using aren't very predicative and can fluctuate based upon incoming data.

Click here if you want to read an excellent article in fivethirtyeight from late March on how difficult statistical modeling can be.

Anyway, in my exponential model the b value has been decreasing, and is now .0944122, which means the rate of increase has been decreasing and is now less than 9.5%. The a value has however shot up and is now 12.7004. Which implies that there are 12.7 times more positives than we have actually measured/tested. So there could be upwards of 8,585 (12.7 times 676) positive cases.

I am keeping data for San Francisco, Alabama, California, Florida, Kentucky, Mississippi, New York State, and Tennessee, for the sake of comparison. Here are the current b values:

Here are the model's updated 14 and 21 day predictions:

30,359 = 676×12.7004×(1+0.0944122)^14

57,090 = 676×12.7004×(1+0.0944122)^21

I am using an additional model, the sideways "S" curve. That's called a logistic model, or 1 over 1 plus the euler growth factor. It enables prediction to the plateau or "flattening out", and I am using it to compare the exponential to the linear (the dashed red line). So you might notice that this morning's SF Department of Public Health release of 54 more cases puts the trend above the linear and logistic but less than the exponential. The trend has been above the linear for the last 5 days.

The small bell curve on the bottom regresses the daily increases from the previous date (which was +54 today). That model gets down to plus 0.5 cases when x equals 75, or 32 days after today, which is 10 May 2020.