COVID-19 and Probability

This was posted by a Facebook “friend” (who is among many on FB who seem to believe that figuratively hectoring like-minded friends on FB will instill caution among the incautious):

The point I want to make here isn’t about COVID-19, but about probability. It’s a point that I’ve made many times, but the image captures it perfectly. Here’s the point:

When an event has more than one possible outcome, a single trial cannot replicate the average outcome of a large number of trials (replications of the event).

It follows that the average outcome of a large number of trials — the probability of each possible outcome — cannot occur in a single trial.

It is therefore meaningless to ascribe a probability to any possible outcome of a single trial.

Suppose you’re offered a jelly bean from a bag of 100 jelly bean, and are told that two of the jelly beans contain a potentially fatal poison. Do you believe that you have only a 2-percent chance of being poisoned, and would you bet accordingly? Or do you believe, correctly, that you might choose a poisoned jelly bean, and that the “probability” of choosing a poisoned one is meaningless and irrelevant if you want to be certain of surviving the trial at hand (choosing a jelly bean or declining the offer). That is, would you bet (your life) against choosing a poisoned jelly bean?

I have argued (futilely) with several otherwise smart persons who would insist on the 2-percent interpretation. But I doubt (and hope) that any of them would bet accordingly and then choose a jelly bean from a bag of 100 that contains even a single poisoned one, let alone two. Talk is cheap; actions speak louder than words.

COVID-19: The Disconnect between Cases and Deaths

As many (including me) have observed, COVID-19 case statistics don’t give a reliable picture of the spread of COVID-19 in the U.S. Just a few of the reasons are misdiagnosis; asymptomatic (and untested) cases; and wide variations in the timing, location, and completeness of testing. As a result, the once-tight correlation between reported cases and deaths has loosened to the point of meaninglessness:


Source: Derived from statistics reported here.

So when you hear about a “surge” in cases, do not assume that they are actually new cases. It’s just that new cases are being discovered because more tests are being conducted. The death toll, overstated as it is, is a better indicator of the state of affairs. And the death toll continues to drop.

Reflections on Aging and Social Disengagement

Aging is of interest to me because I suddenly and surprisingly find myself among the oldest ten percent of Americans.

I also find myself among the more solitary of Americans. My wife and I rattle about in a house that could comfortably accommodate a family of six, with plenty of space in which to have sizeable gatherings (which we no longer do). But I am not lonely in my solitude, for it is and long has been of my own choosing. Lockdowns and self-isolation haven’t affected me a bit. Life, for me, goes on as usual and as I like it.

This is so because of my strong introversion. I suppose that the seeds of my introversion are genetic, but the symptoms didn’t appear in earnest until I was in my early thirties. After that I became steadily more focused on a few friendships (which eventually dwindled to none) and decidedly uninterested in the aspects of work that required more than brief meetings (one-on-one preferred). Finally, enough became more than enough and I quit full-time work at the age of fifty-six. There followed, a few years later, a stint of part-time work that also became more than enough. And so, at the age of fifty-nine, I banked my final paycheck. Happily.

What does my introversion have to do with my aging? I suspected that my continued withdrawal from social intercourse (more about that, below) might be a symptom of aging. And I found this, in the Wikipedia article “Disengagement Theory“:

The disengagement theory of aging states that “aging is an inevitable, mutual withdrawal or disengagement, resulting in decreased interaction between the aging person and others in the social system he belongs to”. The theory claims that it is natural and acceptable for older adults to withdraw from society….

Disengagement theory was formulated by [Elaine] Cumming and [William Earl] Henry in 1961 in the book Growing Old, and it was the first theory of aging that social scientists developed….

The disengagement theory is one of three major psychosocial theories which describe how people develop in old age. The other two major psychosocial theories are the activity theory and the continuity theory, and the disengagement theory [is at] odds with both.

The continuity theory

states that older adults will usually maintain the same activities, behaviors, relationships as they did in their earlier years of life. According to this theory, older adults try to maintain this continuity of lifestyle by adapting strategies that are connected to their past experiences [whatever that means].

I don’t see any conflict between the continuity theory and the disengagement theory. A strong introvert like me, for example, finds it easy to maintain the same activities, behaviors, and relationships as I did before I retired. Which is to say that I had begun minimizing my social interactions before retiring, and continued to do so after retiring.

What about the activity theory? Well, it’s a normative theory, unlike the other two (which are descriptive), and it goes like this:

The activity theory … proposes that successful aging occurs when older adults stay active and maintain social interactions. It takes the view that the aging process is delayed and the quality of life is enhanced when old people remain socially active.

That’s just a social worker’s view of “appropriate” behavior for older persons. Take my word for it, introverts don’t need it social activity, which is stressful for them, and resent those who try to push them into it. The life of the mind is far more rewarding than chit-chat with geezers. Why do you suppose my wife and I will do everything in our power to stay in our own home until we die? It’s not just because we love our home so much (and we do), but we can’t abide the idea of communal living, even in an upscale retirement community.

Anyway, I mentioned my continued withdrawal from social intercourse. A particular, recent instance of withdrawal sparked this post. For about fifteen years I corresponded regularly with a former colleague. He  has a malady that I have dubbed email-arrhea: several messages a day to a large mailing list, with many insipid replies from recipients whose choose “reply all”. Enough of that finally became too much, and I declared to him my intention to refrain from correspondence until … whenever. (“Don’t call me, I’ll call you.”) So all of his messages and those of his other correspondents are dumped automatically into my Gmail trash folder, and I no longer use Gmail.

My withdrawal from that particular node of social intercourse was eased by the fact that the correspondent is a collaborationist “conservative” with a deep-state mindset. So it was satisfying to terminate our relationship — and devote more time to things that I enjoy, like blogging.

Just Another Thing or Two about COVID-19

Though it’s tough to make predictions, especially about the future, and I sort of promised not to make any more predictions about the spread of COVID-19 in the United States because the data are unreliable (examples at the link and here). But I can’t resist saying a few more things about the matter.

Specifically, since my last substantive post about COVID-19 statistics, I now project 2 million cases and 135,000 deaths by mid-August, as against my earlier projections of 1.3 million and 90,000. The new estimates rely on the same database as the old ones, so they aren’t any more reliable than the old ones.

But I have revised my calculations so that they are based on 7-day average numbers of cases and deaths. This is an attempt to smooth over obvious lags in reporting (sudden drops in numbers of cases and deaths followed by sudden surges).

The equations in these two graphs …

… yield these projections:

Those are nationwide numbers. The good news (pending the results of “re-opening”) is that the daily number of new cases has declined sharply from the peaks of late March and late April. But there’s still a long way to go. The first graph in this post is worrisome because recent observations are a bit above the trend line; that is, the incidence of new cases may not be declining quite as rapidly as the equation suggests.

The number of new deaths has declined also, from the peak 7-day average of 2,041 on April 21 to 1,430 as of May 15. Overall, the rate of new deaths per new case seems to have stabilized at 5.7 percent. (The overall percentage will be somewhat higher because the deaths/case rate was higher than 5.7 for quite a while.)

Of course, the situation varies widely from State to State (and, obviously, within each State):

Regional and state variations in death rates
(I am using same assignment of States to regions as used by my data source.)

Nine of the 12 States of the Northeast (including D.C.) are among the top 12 in deaths per resident. The exceptions are the more rural Northeastern States: Main, New Hampshire, and Vermont.

In general, States with large, densely populated metropolitan areas have fared worse than less-urbanized States with smaller cities. That’s unsurprising, of course. But it also underscores the resistance of large swaths of the populace to “New York” rules.


Other related posts:

Contagion Nation?
“Give Me Liberty or Give Me Death”

“It’s Tough to Make Predictions, Especially about the Future”

A lot of people have said it, or something like it, though probably not Yogi Berra, to whom it’s often attributed.

Here’s another saying, which is also apt here: History does not repeat itself. The historians repeat one another.

I am accordingly amused by something called cliodynamics, which is discussed at length by Amanda Rees in “Are There Laws of History?” (Aeon, May 2020). The Wikipedia article about cliodynamics describes it as

a transdisciplinary area of research integrating cultural evolution, economic history/cliometrics, macrosociology, the mathematical modeling of historical processes during the longue durée [the long term], and the construction and analysis of historical databases. Cliodynamics treats history as science. Its practitioners develop theories that explain such dynamical processes as the rise and fall of empires, population booms and busts, spread and disappearance of religions. These theories are translated into mathematical models. Finally, model predictions are tested against data. Thus, building and analyzing massive databases of historical and archaeological information is one of the most important goals of cliodynamics.

I won’t dwell on the methods of cliodynamics, which involve making up numbers about various kinds of phenomena and then making up models which purport to describe, mathematically, the interactions among the phenomena. Underlying it all is the practitioner’s broad knowledge of historical events, which he converts (with the proper selection of numerical values and mathematical relationships) into such things as the Kondratiev wave, a post-hoc explanation of a series of arbitrarily denominated and subjectively measured economic eras.

In sum, if you seek patterns you will find them, but pattern-making (modeling) is not science. (There’s a lot more here.)

Here’s a simple demonstration of what’s going on with cliodynamics. Using the RANDBETWEEN function of Excel, I generated two columns of random numbers ranging in value from 0 to 1,000, with 1,000 numbers in each column. I designated the values in the left column as x variables and the numbers in the right column as y variables. I then arbitrarily chose the first 10 pairs of numbers and plotted them:

As it turns out, the relationship, even though it seems rather loose, has only a 21-percent chance of being due to chance. In the language of statistics, two-tailed p=0.21.

Of course, the relationship is due entirely to chance because it’s the relationship between two sets of random numbers. So much for statistical tests of “significance”.

Moreover, I could have found “more significant” relationships had I combed carefully through the 1,000 pairs of random number with my pattern-seeking brain.

But being an honest person with scientific integrity, I will show you the plot of all 1,000 pairs of random numbers:

I didn’t bother to find a correlation between the x and y values because there is none. And that’s the messy reality of human history. Yes, there have been many determined (i.e., sought-for) outcomes  — such as America’s independence from Great Britain and Hitler’s rise to power. But they are not predetermined outcomes. Their realization depended on the surrounding circumstances of the moment, which were myriad, non-quantifiable, and largely random in relation to the event under examination (the revolution, the putsch, etc.). The outcomes only seem inevitable and predictable in hindsight.

Cliodynamics is a variant of the anthropic principle, which is that he laws of physics appear to be fine-tuned to support human life because we humans happen to be here to observe the laws of physics. In the case of cliodynamics, the past seems to consist of inevitable events because we are here in the present looking back (rather hazily) at the events that occurred in the past.

Cliodynametricians, meet Nostradamus. He “foresaw” the future long before you did.

Contagion Nation?

The coronavirus outbreak in the United States is of a piece with the steady rise in influenza cases over the past 13 years, which is the period for which CDC maintains tallies of flu tests and test results.

Here are some raw statistics, representing weekly results since the 40th week of 1997:

The rate of positive tests has remained steady since 1997, with a slight upward bump coincident with the swine flu epidemic of 2009-2010:

The steadiness of the positive-test percentage suggests that the presence of flu-like symptoms was just as likely to have prompted testing in 1997 as in 2020. Another way to put it is that the first graph accurately represents a steady rise in the occurrence of flu-like symptoms in the population.

This can be seen in the following graph:

Despite the fairly stable incidence of positive tests, the number of positive tests has grown far more rapidly than the population of the U.S.

The bottom line: Americans have become increasingly prone to contract flu-like illnesses. Though the increase can’t be explained by the overall rise in the country’s population, it is probably due in part to greater population density in urban areas. It is probably also due in part to the weakening of immune systems relative to the ability of viruses to mutate.

It is possible that influenza won’t be as prevalent in the future as more Americans take precautions against contagion in the wake of COVID-19. But memories are short, and precautions are easily cast aside when the world seems to have returned to normal. So I expect that in a few years the incidence of flu will resume its long-term rise.

Ain’t It the Truth?

I’m reading Charles Murray’s Human Diversity, which I mention here. (It’s a book that leftists will hate, even though they won’t read it, just as they hated The Bell Curve without having read it.)

The following passage is consistent with my up-close and personal observations of women, which span more than six decades:

In the psychological literature, rumination refers to thoughts, typically autobiographical, that a person mentally rehearses over and over, usually not productively. When they are negative thoughts, rumination amounts to brooding. Taken to an extreme, rumination can become depression. In the 1990s, Susan Nolen-Hoeksema led several studies establishing that women were more likely than men to ruminate, particularly in response to negative events. In the early 2000s, two studies using brain imaging established a biological basis for those findings…. The finding in both studies was that males quickly became habituated to a stimulus—the response in the amygdala decreased rapidly after the first few exposures—whereas it persisted among women. In 2013, researchers at the Harvard Medical School (Joseph Andreano was the first author) tested whether this pattern replicated for both positive and negative stimuli. It did not. As in previous studies, men showed higher amygdala activity for novel stimuli than women no matter whether the stimulus was negative, neutral, or positive. For familiar positive stimuli, men again had a higher response than women. But when it came to negative stimuli, men quickly habituated while women continued to show substantial amygdala activity even after repeated exposure. The difference was large enough that it reached statistical significance despite the small sample size.

Now recall the table in chapter 2 that showed the prevalence of personality disorders by sex. Men had higher incidence rates on the autism spectrum, conduct disorders, ADHD, and schizophrenia, among others. Women had higher prevalence on another set, including three that involve rumination: major depression, generalized anxiety, and post-traumatic stress disorder. The findings I have just summarized point to a sex difference both in the intensity of initial reaction to negative stimuli and in the persistence of that reaction, which in turn point to a difference in rumination.

Amen. The world-class worriers-brooders-haters whom I have known were (and are) women.

COVID-19: Public Service Announcement

It has become obvious that COVID-19 stats are unreliable; see this, this, and this, for example. I am therefore withdrawing from the business of reporting official stats and making projections based on them. I leave that endeavor with this thought.

UHI in Austin Revisited

See “Climate Hysteria: An Update” for the background of this post.

The average annual temperature in the city of Austin, Texas, rose by 3.7 degrees F between 1960 and 2019, that is, from 67.2 degrees to 70.9 degrees. The increase in Austin’s population from 187,000 in 1960 to 930,000 in 2019 accounts for all of the increase. (The population estimate for 2019 reflects a downward adjustment to compensate for an annexation in 1998 that significantly enlarged Austin’s territory and population.)

My estimate of the effect of Austin’s population increase on temperature is based on the equation for North American cities in T.R. Oke’s “City Size and the Urban Heat Island”. The equation (simplified for ease of reproduction) is

T’ = 2.96 log P – 6.41

Where,

T’ = change in temperature, degrees C

P = population, holding area constant

The author reports r-squared = 0.92 and SE = 0.7 degrees C (1.26 degrees F).

I plugged the values for Austin’s population in 1960 and 2019 into the equation, took the difference between the results, and converted that difference to degrees Fahrenheit, with this result: The effect of Austin’s population growth from 1960 to 2019 was to increase Austin’s temperature by 3.7 degrees F. What an amazing non-coincidence.

Austin’s soup weather nazi should now shut up about the purported effect of “climate change” on Austin’s temperature.

COVID-19 in the United States: Updated Statistics and Projections

Details here.

It is noteworthy that the 2019-2020 flu season was taking a much heavier than normal toll until COVID-19 came along. That alone should cast a lot of doubt on the COVID-19 figures being reported by the States and D.C. Then there is the problem of comorbidity, especially among older persons. In sum, there won’t be a good estimate of the actual death toll of COVID-19  until it’s possible to compute “excess” deaths — taking all other causes into account — when the final tally of deaths in 2020 becomes available a few years hence.

There is also the looming possibility that (1) the COVID-19 infection rate is vastly understated; (2) the COVID-19 fatality rate is therefore vastly overstated; and (3) millions of persons who are already immune to COVID-19 because they have already (unknowingly) recovered from it (believing that they had a cold, the flu, or allergies) and are being held hostage by lockdown orders that are killing the economy. (See this for example.)

Insidious Algorithms

Michael Anton inveighs against Big Tech and pseudo-libertarian collaborators in “Dear Avengers of the Free Market” (Law & Liberty, October 5, 2018):

Beyond the snarky attacks on me personally and insinuations of my “racism”—cut-and-paste obligatory for the “Right” these days—the responses by James Pethokoukis and (especially) John Tamny to my Liberty Forum essay on Silicon Valley are the usual sorts of press releases that are written to butter up the industry and its leaders in hopes of . . . what?…

… I am accused of having “a fundamental problem with capitalism itself.” Guilty, if by that is meant the reservations about mammon-worship first voiced by Plato and Aristotle and reinforced by the godfather of capitalism, Adam Smith, in his Theory of Moral Sentiments (the book that Smith himself indicates is the indispensable foundation for his praise of capitalism in the Wealth of Nations). Wealth is equipment, a means to higher ends. In the middle of the last century, the Right rightly focused on unjust impediments to the creation and acquisition of wealth. But conservatism, lacking a deeper understanding of the virtues and of human nature—of what wealth is for—eventually ossified into a defense of wealth as an end in itself. Many, including apparently Pethokoukis and Tamny, remain stuck in that rut to this day and mistake it for conservatism.

Both critics were especially appalled by my daring to criticize modern tech’s latest innovations. Who am I to judge what people want to sell or buy? From a libertarian standpoint, of course, no one may pass judgment. Under this view, commerce has no moral content…. To homo economicus any choice that does not inflict direct harm is ipso facto not subject to moral scrutiny, yet morality is defined as the efficient, non-coercive, undistorted operation of the market.

Naturally, then, Pethokoukis and Tamny scoff at my claim that Silicon Valley has not produced anything truly good or useful in a long time, but has instead turned to creating and selling things that are actively harmful to society and the soul. Not that they deny the claim, exactly. They simply rule it irrelevant. Capitalism has nothing to do with the soul (assuming the latter even exists). To which I again say: When you elevate a means into an end, that end—in not being the thing it ought to be—corrupts its intended beneficiaries.

There are morally neutral economic goods, like guns, which can be used for self-defense or murder. But there are economic goods that undermine morality (e.g., abortion, “entertainment” that glamorizes casual sex) and fray the bonds of mutual trust and respect that are necessary to civil society. (How does one trust a person who treats life and marriage as if they were unworthy of respect?)

There’s a particular aspect of Anton’s piece that I want to emphasize here: Big Tech’s alliance with the left in its skewing of information.

Continuing with Anton:

The modern tech information monopoly is a threat to self-government in at least three ways. First its … consolidation of monopoly power, which the techies are using to guarantee the outcome they want and to suppress dissent. It’s working….

Second, and related, is the way that social media digitizes pitchforked mobs. Aristocrats used to have to fear the masses; now they enable, weaponize, and deploy them…. The grandees of Professorville and Sand Hill Road and Outer Broadway can and routinely do use social justice warriors to their advantage. Come to that, hundreds of thousands of whom, like modern Red Guards, don’t have to be mobilized or even paid. They seek to stifle dissent and destroy lives and careers for the sheer joy of it.

Third and most important, tech-as-time-sucking-frivolity is infantilizing and enstupefying society—corroding the reason-based public discourse without which no republic can exist….

But all the dynamism and innovation Tamny and Pethokoukis praise only emerge from a bedrock of republican virtue. This is the core truth that libertarians seem unable to appreciate. Silicon Valley is undermining that virtue—with its products, with its tightening grip on power, and with its attempt to reengineer society, the economy, and human life.

I am especially concerned here with the practice of tinkering with AI algorithms to perpetuate bias in the name of  eliminating it (e.g., here). The bias to be perpetuated, in this case, is blank-slate bias: the mistaken belief that there are no inborn differences between blacks and whites or men and women. It is that belief which underpins affirmative action in employment, which penalizes the innocent and reduces the quality of products and services, and incurs heavy enforcement costs; “head start” programs, which waste taxpayers’ money; and “diversity” programs at universities, which penalize the innocent and set blacks up for failure. Those programs and many more of their ilk are generally responsible for heightening social discord rather than reducing it.

In the upside-down world of “social justice” an algorithm is considered biased if it is unbiased; that is, if it reflects the real correlations between race, sex, and ability in certain kinds of endeavors. Charles Murray’s Human Diversity demolishes the blank-slate theory with reams and reams of facts. Social-justice warriors will hate it, just as they hated The Bell Curve, even though they won’t read the later book, just as they didn’t read the earlier one.

Flattening the Curve

What does it mean to “flatten the curve”, in the context of an epidemic? Here is Willis Eschenbach’s interpretation:

What does “flattening the curve” mean? It is based on the hope that our interventions will slow the progress of the disease. By doing so, we won’t get as many deaths on any given day. And this means less strain on a city or a country’s medical system.

Be clear, however, that this is just a delaying tactic. Flattening the curve does not reduce the total number of cases or deaths. It just spreads out the same amount over a longer time period. Valuable indeed, critical at times, but keep in mind that these delaying interventions do not reduce the reach of the infection. Unless your health system is so overloaded that people are needlessly dying, the final numbers stay the same.

I beg to differ. Or, at least, to offer a different interpretation: Flattening the curve — reducing its peak — can also reduce the total number of persons who are potentially exposed to the disease, thereby reducing the total number of persons who contract it. How does that work? It requires not only reducing the peak of the curve — the maximum number of active cases — but also reducing the length of the curve — the span of time in which a population is potentially exposed to the contagion.

Consider someone who has randomly contracted a virus from a non-human source. If that person is a hermit, the virus may kill him, or he may recover from whatever illness it causes him, but he can’t infect anyone else. Low peak, short duration.

Here’s an example of a higher peak but a relatively short duration: A person who randomly contracts a virus from a non-human source then infects many other persons in quick succession by breathing near them, sneezing on them, touching them, etc., in a short span of time (e.g., meeting and greeting at a business function). But … if the originator of the contagion and those whom he initially infects are identified and quarantined quickly enough, the contagion will spread no further.

In both cases, the “curve” will peak at some number lower than the number that would have been reached without isolation or quarantine. Moreover, and more important, the curve will terminate (go to zero) more quickly than it would have without isolation or quarantine.

The real world is more complicated than either of my examples because almost all humans aren’t hermits, and infections usually aren’t detected until after an infected person has had many encounters with uninfected persons. But the principle remains the same: The total number of persons who contract a contagious disease can be reduced through isolation and quarantine — and the sooner isolation and quarantine take effect, the lower the total number of infected persons.

Climate Hysteria: An Update

I won’t repeat all of “Climate Hysteria“, which is long but worth a look if you haven’t read it. A key part of it is a bit out of date, specifically, the part about the weather in Austin, Texas.

Last fall’s heat wave in Austin threw our local soup weather nazi into a tizzy. Of course it did; he proclaims it “nice” when daytime high temperatures are in the 60s and 70s, and complains about anything above 80. I wonder why he stays in Austin.

The weather nazi is also a warmist. He was in “climate change” heaven when, on several days in September and October, the official weather station in Austin reported new record highs for the relevant dates. To top it off, tropical storm Imelda suddenly formed in mid-September near the gulf coast of Texas and inundated Houston. According to the weather nazi, both events were due to “climate change”. Or were they just weather? My money’s on the latter.

Let’s take Imelda, which the weather nazi proclaimed to be an example of the kind of “extreme” weather event that will occur more often as “climate change” takes us in the direction of catastrophe. Those “extreme” weather events, when viewed globally (which is the only correct way to view them) aren’t occurring more often, as I document in “Hurricane Hysteria“.

Here, I want to focus on Austin’s temperature record.

There are some problems with the weather nazi’s reaction to the heat wave. First, the global circulation models (GCMs) that forecast ever-rising temperatures have been falsified. (See the discussion of GCMs here.) Second, the heat wave and the dry spell should be viewed in perspective. Here, for example are annualized temperature and rainfall averages for Austin, going back to the decade in which “global warming” began to register on the consciousnesses of climate hysterics:

What do you see? I see a recent decline in Austin’s average temperature from the El Nino effect of 2015-2016. I also see a decline in rainfall that doesn’t come close to being as severe the a dozen or so declines that have occurred since 1970.

Here’s a plot of the relationship between monthly average temperature and monthly rainfall during the same period. The 1-month lag in temperature gives the best fit. The equation is statistically significant, despite the low correlation coefficient (r = 0.24) because of the large number of observations.

Abnormal heat is to be expected when there is little rain and a lot of sunshine. In other words, temperature data, standing by themselves, are of little use in explaining a region’s climate.

Drawing on daily weather reports for the past five-and-a-half years in Austin, I find that Austin’s daily high temperature is significantly affected by rainfall, wind speed, wind direction, and cloud cover. For example (everything else being the same):

  • An additional inch of rainfall induces an temperature drop of 1.4 degrees F.
  • A wind of 10 miles an hour from the north induces a temperature drop of about 5.9 degrees F relative to a 10-mph wind from the south.
  • Going from 100-percent sunshine to 100-percent cloud cover induces a temperature drop of 0.3 degrees F.
  • The combined effect of an inch of rain and complete loss of sunshine is therefore 1.7 degrees F, even before other factors come into play (e.g., rain accompanied by wind from the north or northwest, as is often the case in Austin).

The combined effects of variations in rainfall, wind speed, wind direction, and cloud cover are far more than enough to account for the molehill temperature anomalies that “climate change” hysterics magnify into mountains of doom.

Further, there is no systematic bias in the estimates, as shown by the following plot of regression residuals:


Meteorological seasons: tan = fall (September, October, November); blue = winter (December, January, February); green = spring (March, April, May); ochre = summer (June July, August). Values greater than zero = underestimates; values less than zero = overestimates.

Summer is the most predictable of the seasons; winter, the least predicable; spring and fall are in between. However, the fall of 2019 (which included both the hot spell and cold snap discussed above) was dominated by overestimated (below-normal) temperatures, not above-normal ones, despite the weather-nazi’s hysteria to the contrary. In fact, the below-normal temperatures were the most below-normal of those recorded during the five-and-a-half year period.

The winter of 2019-2020 was on the warm side, but not abnormally so (cf. the winter of 2016-2017). Further, the warming in the winter of 2019-2020 can be attributed in part to weak El Nino conditions.

Lurking behind all of this, and swamping all other causes of the (slightly upward) temperature trend is a pronounced urban-heat-island (UHI) effect (discussed here). What the weather nazi really sees (but doesn’t understand or won’t admit) is that Austin is getting warmer mainly because of rapid population growth (50 percent since 2000) and all that has ensued — more buildings, more roads, more vehicles on the move, and less green space.

The moral of the story: If you really want to do something about the weather, move to a climate that you find more congenial (hint, hint).

COVID-19 in the United States: Latest Projections

UPDATED 04/21/20

Relying on data collected through April 20, I project about 1.3 million cases and 90,000 deaths by the middle of August. Those numbers are 50,000 and 6,000 higher than the projections that I published three days ago. However, the new numbers are based on statistical relationships that, I believe, don’t fully reflect the declining numbers of new cases and deaths discussed below. If the numbers continue to decline rapidly, the estimates of total cases and death should decline, too.

Figure 1 plots total cases and deaths — actual and projected — by date.

Figure 1

Source and notes: Derived from statistics reported by States and the District of Columbia and compiled in Template:2019–20 coronavirus pandemic data/United States medical cases at Wikipedia. The statistics exclude cases and deaths occurring among repatriated persons (i.e., Americans returned from other countries or cruise ships).

But there is good news in the actual and projected numbers of new cases and new deaths (Figure 2).

Figure 2

As shown in Figure 3, the daily percentage changes in new cases and deaths have been declining generally since March 19.

Figure 3

But there is, of course, a lag between new cases and new deaths. The best fit is a 7-day lag (Figure 4).

Figure 4

Figure 5 shows the tight relationship between new cases and new deaths when Figure 3 is adjusted to introduce the 7-day lag.

Figure 5

Figure 6 shows the similarly tight relationship after removing 8 “hot spots” which have the highest incidence of cases per capita — Connecticut, District of Columbia, Louisiana, Massachusetts, Michigan, New Jersey, New York, and Rhode Island.

Figure 6

Figures 5 and 6 give me added confidence that the crisis has peaked.

COVID-19 Update and Prediction

I have updated my statistical analysis here. Note especially the continued decline in the daily rate of new cases and the low rate of new deaths per new case.

Now for the prediction. Assuming that lockdowns, quarantines, and social distancing continue for at least two more weeks, and assuming that there isn’t a second wave of COVID-19 because of early relaxation or re-infection:

  • The total number of COVID-19 cases in the U.S. won’t exceed 250,000.
  • The total number of U.S. deaths attributed to COVID-19 won’t exceed 10,000.

In any event, the final numbers will be well below the totals for the swine-flu epidemic of 2009-10 (59 million and 12,000) but you won’t hear about it from the leftist media.

UPDATE 03/31/20: Some sources are reporting higher numbers of U.S. cases and deaths than the source that I am using for my analysis and predictions. It is therefore possible that the final numbers (according to some sources) will be higher than my predictions. But I will be in the ballpark.

UPDATE 04/10/20: See my revised estimate.