Finding Footing

When it gets too much
I need to feel your touch
--Bryan Adams

With COVID hysteria raging among the politicos, markets have found their footing. Major indexes have retraced much of the meltdown path.

Nasdaq has been the strongest index.

Despite tech strength, the best performing sector on the way up has been...energy. $12 oil and all...

Moving Toward Freedom

"Exit visas are imminent."
--Gordon Gekko (Wall Street)

A nice feature of our federalist design is that it invites policy competition among the states. States that impose high taxes on its citizens, for instance, risk losing a big chunk of its tax base if people choose to flee to lower tax regimes.

Currently, varied state responses w.r.t. re-opening their economies post COVID is creating a new basis for policy competition. States such as California, New York, and Michigan, which have signaled that their lockdown orders will extend into the foreseeable future, could see a wave of exits as people seek freedom.

Of course, it is also possible that some people ditch free states if they believe that rescinding lock-down orders puts their health in jeopardy.

While competition related to unlocking economies creates a two-way street, my sense is that traffic will be heavy in one direction only as people move toward freedom.

Time Until Reopen and Politics

I see you on the street and you walk on by
You make me want to hang my head down and cry
--Madonna

Although political bias seemed apparent as we went into lockdown, it now seems obvious as various states look to re-open their economies. Using party affiliation (D = Democratic, R = Republican) of the state governor as a proxy, let's list some states moving to re-open large portions of their economies:

Georgia - R
Iowa - R
Ohio - R
Tennessee - R
Texas - R
West Virginia - R

Now for states signaling reluctance to end lockdowns soon:

California - D
Michigan - D
New Jersey - D
New York - D
Oregon - D
Washington - D

A small and perhaps selective sample. But to my knowledge as of this date, there is no R state digging in its heels proclaiming that it will remain closed for the foreseeable future, nor is there a D state hustling to re-open.

Pressure does seem likely to build on laggards as leaders unlock the shackles. It will be difficult to keep people locked down in places like California when those prisoners see freemen going about their business once again in other states.

Meanwhile, building on the Days to Shutdown metric discussed previously, let's define a metric called Time Until Reopen. Suppose we measure it in terms of the number of days from the peak COVID death count in a state until that state rescinds a majority percentage (e.g., 80%) of its original lockdown orders.

The proposition is this: On average, D states will have a significantly higher Time Until Reopen than will R states.

Although many possible reasons might explain why political bias exists in the COVID situation, distilling those possibilities down into a lucid explanation of the factors at work in this case remains a task for the future.

Do Lockdowns Work?

"Shut it down. Shut it down now!"
--Telco operator (Die Hard)

Interesting study of lockdown effectiveness. The authors considered US states that have ordered mandatory lockdowns (I count 45 state data points on the graph). The independent variable was 'Days to Shutdown,' or the number of days after state COVID death rate hit 1 per million that businesses were ordered to close. The lower the number, the faster the time till shutdown. Some Days to Shutdown are negative because some states issued mandatory lockdowns before death counts had ticked above the 1/million threshold.

The dependent variable was Deaths per Million at 21 Days, or COVID death rate per million 21 days after the initial 1 per million threshold was met.

If lockdowns have been a major factor in preventing COVID deaths, then we should expect to see a positive relationship between X and Y. In other words, the longer it took states to lockdown, the greater the death rate should be.

That's not what we see, however. The slope of the regression line is shown to be slightly negative, with a reported R-squared of about 5%. The authors do not report the statistical significance of their regression results.

The authors suggest that lack of relationship here indicates other variables at work. They report that a regression analysis of per capita death rates vs state population density, for instance, produced a correlation coefficient of 0.44.

European countries were added for context (blue dots). Once again, no relationship between time to lockdown and COVID death rate is apparent.

Although the authors do not reflect on limitations of their study, I can think of a couple off-hand. The data are not distinguished by degree of lockdown, although measures have varied by jurisdiction. For example, Sweden (which the authors do discuss) has implemented relatively light lockdown measures compared to other places. It is interesting that states instituting some of the more draconian measures (e.g., NY, NJ, MI, MA) fare worse on the chart.

Another issue involves why the choice of 21 days after the touching one fatality per million to measure COVID deaths? The authors do not explain how they arrived at the three week reference point. Review of daily fatalities in many jurisdictions does seem to suggest peaks after 3-4 weeks, thus lending some intuitive appeal to the 21 day period. However, supporting rationale here would strengthen the analysis.

Even with those limitations, this study presents some interesting findings. These findings suggest that forced lockdowns are not primary factors in preventing COVID-related fatalities.

Evidence Man

"Let's play another game."
--Frank Dulaney (Body of Evidence)

Nice follow-up piece with Stanford MD and prof John Ioannidis. These pages discussed Ioannidis' mid-March warning of the 'evidence fiasco' surrounding COVID policymaking.

Not surprisingly, he was chastised by the public health establishment for breaking ranks from the party line. Fortunately, Dr Ioannidis did not bow to institutional pressures for compliance and remained on course. Since then, he has authored multiple academic articles on early COVID data analysis and need for better evidence-based policymaking. He has also collaborated with several Stanford colleagues to perform one of the early antibody studies on COVID infection prevalence.

One of the things I find most interesting is that most of Ioannidis' early conclusions--many drawn from analysis of the unique Diamond Princess cruise ship population sample--still hold today.

In the WSJ follow-up piece, Ioannidis notes that, although he enjoys models, "they're very, very low in terms of how much weight we can place on them and how much we can trust them." While models might offer some intuition about situation's early shape, "depending on models for evidence, I think that's a very bad recipe."

He laments that "there's a sort of mob mentality here operating that they just insist that this has to be the end of the world, and it has to be that the sky is falling." Rather than focusing on data and evidence, the mob favors "speculation and science fiction."

Well said, Dr Ioannidis. However, these pages have argued that games of COVID dissonance should be expected. We might also expect that such dissonance will dissipate as the body of evidence continues to grow.

Antibody Studies

Shiver and say the words
Of every lie you've heard
--Echo & the Bunnymen

'Antibody studies' are finally starting to roll in. The studies seek to estimate the percentage of people who have already been infected by COVID-19 by checking for the presence of serum antibodies in large samples (n = 100s or 1000s of individuals).

Results are streaming in. NYC, Boston, Santa Clara, LA, Miami--among others locations (hoping someone aggregates these studies soon on a website).

All suggest similar. The prevalence of COVID-19 among the population is much higher than official reported infection counts--orders of magnitude higher. Estimated prevalence from the antibody studies ranges from 4% to over 20% in some jurisdictions. Projecting these results suggest that tens of millions of Americans have been infected with COVID-19.

It should be noted that COVID antibodies may take 2-3 weeks to develop in the bloodstream after infection, meaning that the infection rate measured in a particular antibody study likely under-reports true prevalence at a single point in time. This is why antibody tests for prevalence need to be repeated to better understand incidence (rate of change) as well.

With total infection counts much higher than expected, fatality rates are much lower than reported--and certainly far lower than initially forecast. Alex Berenson believes COVID-19 death rates are likely to settle in the 0.25% - 0.40% range (deaths/# infected basis).

5/ Add all this up, and the COVID death rate will likely settle into the 0.25%-0.4% range (1 in 250 people infected to 1 in 400). Far from 20 times higher than flu. Meanwhile, the median age of death is 78-80. And unlike the flu, #SARSCoV2 is basically not dangerous to children.

— Alex Berenson (@AlexBerenson) April 24, 2020

It should be acknowledged that the Stanford folks were on top of this from the beginning. It began with Dr Ioannidis' call for prevalence and incidence testing (along with his analysis of the Diamond Princess population sample with conclusions and projections largely in-line with what we're seeing). Other Stanford professors weighed in as well, arguing that COVID infection rates were likely to be far higher than initially reported thus pushing fatality rates far lower--and that, consequently, draconian lock-down measures were likely to do far more harm than good. They echoed the call for prevalence studies.

Now, with the antibody data streaming in, another Stanford doc suggests that policymakers must focus on the data and fundamental biology, rather than clinging to woefully inaccurate hypothetical projections, to thoughtfully remove restrictions and restore order.

Although many people will not welcome the findings, antibody studies help to awaken reasoning minds.

COVID Dissonance

Take the children and yourself
And hide out in the cellar
--Mike and the Mechanics

Cognitive dissonance is mental stress realized when holding contradictory beliefs or thoughts, or by the appearance of new information that consists with existing beliefs. Because cognitive dissonance is difficult to live with, our minds seek to relieve it.

One way to resolve cognitive dissonance is to modify your beliefs or thoughts in the direction of truth. If, for example, you initially believed that COVID-19 pandemic would kill millions of people worldwide, and subsequent data suggest that the virus was much milder than originally thought, then you would concede that your forecast was mistaken. By doing so, of course, you would have to endure some psychic pain associated with admitting that you were wrong.

However, people tend to be loss averse, meaning that, in the case of cognitive dissonance, they are reluctant to cope with the anguish of being wrong. As such, individuals are more likely to pivot away from truth. To avoid psychic pain, they are likely to 'double down,' and escalate their commitment to losing intellectual positions. In the COVID situation, you will be prone to cling to your belief that the virus is a large-scale killer. Those who do not share your views on the COVID-19 pandemic become the enemy.

As the COVID threat dissipates, we should therefore expect multitudes who bought into the original COVID story to collectively dig in their heels and insist that the worst is yet to come.

They will do this until their minds can find a less painful, more convenient way to get off the ride.

Science Deniers

Juror #3: Well, what do you want? I say he's guilty.
Juror #8: We want to hear your arguments.
Juror #3: I GAVE you my arguments!
Juror #8: We're not convinced. We want to hear them again. We have as much time as it takes.
--12 Angry Men

One of the more tiresome replies to people who question statements coming from so-called public health officials' is that the questioners are obviously 'science deniers.'

I have never seen a convincing explanation of why this claim should be true. Absent such scientific (!) justification, we are left with the claimants' ad hominem rhetoric and what it seems to imply. The 'logic' seems to be as follows:

Public health officials are experts in their field, many of them with advanced degrees.

This means that these people are steeped in science.

Therefore, their statements constitute 'science' and therefore are undeniable.

Paradoxically, such a thought process lands some distance away from chains of logic central to the scientific method.

Moreover, a scientific mind is skeptical. It is constantly asking 'what else can it be?' In the case of public health officials, a plausible rival hypothesis in a format similar to the one above is this:

Public health officials are bureaucrats.

This means that these people are steeped in politics.

Therefore, their statements are grounded in self-interested active agency and in hiding policy errors and therefore are highly questionable.

Why isn't the second hypothesis the more valid one? If the original claimants above cannot convincingly answer this question, then who are the true science deniers?

Prosperity and Production

"We're gonna give the people what they need, at prices they can afford to pay. And as fresh needs come up, we'll satisfy them to--with something new and even more exciting. And when we achieve that, we'll really start to grow. We're not gonna die, we're gonna live! And it's gonna take every bit of business judgment and creative energy in this company--from the mills and the factories right to the top of the tower! And we're going to do it together, every one of us, right here at Treadway."
--McDonald Walling (Executive Suite)

Rand Paul's observation is not just 'opinion.' The only way to alleviate axiomatic scarcity is via production--i.e., combining labor with tools to create consumable output. When production is forcibly curtailed, no amount of money printing compensates for the prosperity that is lost. Less output is being produced.

The one thing that will get our economy going again: reopening! We can’t continue printing bailout money. It’s not a lack of money that plagues us. It’s lack of commerce. Until we reopen our economy, job losses will continue.

— Senator Rand Paul (@RandPaul) April 21, 2020

The only way to improve prosperity is to produce more. More production comes from more people working. The more that people produce, the greater the prosperity.

Excess Deaths

Carla: You have a lifetime left. Why waste it missing what's already gone?
Louden Swain: It does seem morbid, doesn't it?
--Vision Quest

These pages have discussed conditions of active agency surrounding public health officials and associated policymakers, including their incentive to 'fudge' data to cover-up errors. It is not surprising, then, that officials are taking increasingly liberal approaches to recording COVID-19 mortality, including backdating deaths and counting anyone even presumed to have the virus upon death as a COVID fatality.

Given that the 'official' COVID numbers are being infected (!) with error at growing rates, it is possible that the most accurate picture of COVID-19 fatalities will come from examining total death counts from all causes (which are much harder to fudge vs cause of death), and taking the difference between actual deaths and the number of fatalities expected from historical patterns to arrive at 'excess deaths' attributable to the coronavirus.

If COVID-19 death counting was 100% accurate, then we would expect to see total deaths surge by the amount of this new, unique morbidity. However, if COVID death counts surge but total fatalities don't surge similarly, then the lack of 'excess' provides more perspective of the true effect of the new disease.

As Ryan McMaken reports, weekly US death counts as of April 4 have yet to show a nationwide surge. He notes that some states, such as New York, did see a significant increase in weekly deaths in early April. However, other states thought to be areas of concern, such as Colorado and Florida, have seen no increase in deaths. In some states, total death counts are down.

Of course, it is possible that deaths from other causes have declined as people are locked down and not socially interacting. On the other hand, suicides and health problems that generally increase when people are out of work and experiencing difficult economic times could serve to push fatalities higher.

What is evident is that, thus far, COVID-19 related deaths have not been sufficient to push nationwide mortality above that significantly exceeds historical levels.