Did We Actually Flatten the Curve?

Did We Actually Flatten the Curve?

“The most brilliant propagandist technique will yield no success unless one fundamental principle is borne in mind constantly…it must confine itself to a few points and repeat them over and over.”- Adolf Hitler (Mein Kempf, Chapter 6: War Propaganda)

From the start of the COVID-19 panic, we have been met with admonitions from roadside signs, from well-meaning friends, from talking heads on TV, and from trending hashtags on social media. These pleas have generally had the same form, albeit using different words. Some of them said, “Stay Home and Save a Life.” Some of them were more aggressive, with “Stay The F*&K Home!” All of them were predicated on the belief that by taking individual action we could “Flatten The Curve and Save Lives” during the COVID-19 Pandemic. This author has attacked that premise in multiple essays. As a mathematical and statistical fact, flattening the curve can possibly save lives in one case and in only one case: if there is overloading of the hospital resources. Given that these resources were not overwhelmed, at least outside specific pockets, we did not save any lives by attempting to flatten the curve.

What if the curve was not flattened? Here is my assertion: Not only did we not need to flatten the curve, we did not actually do much, if any, flattening of it. This despite the aggressive marketing and puffery used to convince the populace to “Stay Home. Save a Life.” While this is not an indictment of the concept of social distancing—particularly intelligent, voluntary approaches—it is an attack on the approaches marketed during the current pandemic. It is intuitively attractive to think staying home and away from everyone who could transmit the disease must necessarily lead to you not being infected. Further, if we lock up an entire population, the spread of a virus should be stopped. If those two points are true, why should it be that locking down as much as possible would not help slow the spread and/or prevent at least the sequestered from getting sick? Well, as a game show host might exclaim, “I am glad you asked!” The answer comes down to three factors:  Timing, Methodology, and (maybe surprisingly) Epidemiology.

Timing:  The mainstream media (MSM) has been vocal about the timing of the U.S. response to the COVID-19 outbreak. The liberal arm of the MSM has been particularly unrelenting in its assertion that POTUS acted too late in calling for social distancing generally and supporting lock-downs in particular. Jacquelyn Corley writes in Fortune Magazine:

As the data…show, the U.S. government was slow to respond and hesitant to escalate the stringency of its public policies compared to other countries in similar situations. One can’t help but think of the lives that might have been saved had we responded the way other countries, like South Korea, did.

Despite the fact that this author is no fan of Donald Trump, such arguments are specious. My previous articles discussed the statistical fact that flattening the curve via social distancing and lock-downs cannot be expected to save lives unless there is overloading of the medical system. There was no widespread overloading, so Ms. Corley’s premise is incorrect. (Ms. Corley must have missed the fact that South Korea did not impose a lock-down.) The data shows that the United States was something like 40 days late in aggressively embracing curve-flattening strategies. How then does one account for the fact that few, if any, of the expected negative outcomes occurred? Again, almost universally, hospital overcrowding was not a factor.

Not that there have not been hotbeds during the COVID-19 outbreak. In New York City the conversion of the Jacob K. Javits Convention Center into a field hospital resulted in about 2/3 occupancy at peak. Interestingly, New York is one of the “heavy lock-down” states. However, most other locales saw no such need—including another heavy lock-down state, Pennsylvania. Dr. Steven Shapiro, chief medical and scientific officer of the University of Pittsburgh Medical Center, was quoted as saying:

We indeed saw a steady stream of patients but never “surged.” At peak in mid-April, COVID-19 patients occupied 2% of our 5,500 hospital beds and 48 of our 750 ventilators. Subsequently, admissions have been decreasing with very few patients now coming from the community, almost all now being from nursing homes. Of note, in the 36 UPMC-owned senior facilities we have had zero positive cases.

This is far from the only example. Seems we can conclude that few people, if any, died due to lack of availability of care. How does it happen that the U.S. was simultaneously slow in beginning to flatten the curve yet also benefited from it being flatter? Maybe it was already as flat as it was going to get.

Consider a hypothetical scenario. A couple decides to have a child, and sets about having routine sex for six weeks straight. At some point, they change their minds. They begin to use a condom. At the end of eight weeks total, can any conclusion be drawn about the efficacy of the condom? If the woman is pregnant, was it the fault of the condom? If she remains not pregnant, was it because the condom was the key? That is exactly where the U.S. was regarding timing, the effects of social distancing, and curve flattening.

Methodology. The skin biome of a typical human is a cornucopia of bacteria, virus, mites, and all manner of other biological and inert material. When we say, “that made my skin crawl,” not only do we not know how correct we are, we are also a tad late with the admission! Put in the terms of a cartoon, although admittedly somewhat overstated, one could think of the Peanuts character “Pig Pen” and that floating cloud of dirt that surrounds him. Do you think having Pig Pen put on an N95 mask changes that cloud enough to worry about? A similar conclusion regarding one’s skin biome and the presence of a homemade mask seems apropos.

Early on as the pandemic moved through the U.S. population, there was little concern about social distancing, with even luminaries like Dr. Anthony Fauci suggesting that such measures were unnecessary. Then later, even shaking hands became too dangerous according to the good doctor. As recently as April 23, 2020, Bill Bryan of the DHS science and technology directorate shared the results of a federal study indicating that coronavirus is weakened by exposure to sunlight, heat, and humidity. So why were events such as running races, typically enjoyed by healthy adults and held outside, subject to closure? The methodology employed to mitigate the transmission was in the wrong direction.

Statewide mandates in places such as New York, supported by CDC recommendations made in mid-March, made it illegal to hold events with greater than 250 people. Then it became 10 people. At some point, it became blanket cancellation of all group activities and lawful opening of only businesses deemed “essential.” The rubric for establishing which businesses were non-essential varied state-to-state, with some states (like New York) including liquor stores, and some states (like Pennsylvania) characterizing liquor stores as non-essential. (No real science to be found, just guessing—and politics.)

What about widespread use of masks? The typical COVID-19 virus particle is supposedly 0.125-micron sphere, with a range of 0.06 microns up to 0.14 microns. An N95 mask filters down to 0.130 microns. One does not need to be a math whiz to see that COVID-19 virus particles are actually smaller. However, studies suggest that the N95 mask is approximately 95% effective. The size of the holes in the overwhelming majority of homemade masks are orders of magnitude larger than the N95. Even with multiple layers, a cloth mask will be approximately 2% effective in stopping virus flow according to the same study. Even surgical masks are ineffective in stopping virus flow, performing in the range of 55% effectiveness.

What about reports from places (such as Hong Kong) where it is believed that usage of masks made the difference? Some have suggested that this must be true since the only continued outbreaks were from 1: a restaurant where everyone eats from the same tray and 2: a Buddhist shrine where everyone takes their masks off. It seems safe to assume that the typical mask-wearer touches both his mask and his face repeatedly during the day and during each wearing.

Consider the typical pre-operation protocol. Every attempt is made to mitigate the unintended transmission of biological material. Despite this protocol, there is still debate about how much biological material could be transferred from doctor to patient. Writes Chris McCullough:

Another study, “Disposable surgical face masks: a systematic review,” published in 2005, also identified ways that masks might contribute to surgical site contamination. The conclusion of their systematic review was that, “it is unclear whether wearing surgical face masks results in any harm or benefit to the patient undergoing clean surgery.”

Not exactly a ringing endorsement. Further in Mr. McCullogh’s piece he cites an NIH paper, entitled “Reducing Surgical Site Infections: A Review,” by Drs. Reichman and Greenberg, wherein they reviewed a number of studies and concluded:

Several other practices, such as the standard use of “scrub suits,” surgical caps, and shoe covers have never been definitively demonstrated to reduce rates of surgical infection, although surgical site infection (SSI) outbreaks have been traced to hair or scalp organisms (regardless of whether a cap was worn), and increased foot traffic through the operating room has been demonstrated to increase ambient microbial levels and ensuing infection risk.

Multiple studies show that even in an operating room, with strict protocols followed by trained practitioners, there are still SSI outbreaks with “no definitive demonstration of reduction” despite the protocols noted above. Contrast that with our typical social distancing experience at any store. You pull up to a parking spot, having driven a bacteria-laden car. You reach into your bacteria-laden pocket with your bacteria, virus, mite-laden hands to get your bacteria-laden homemade mask, which you inoculated with virus the last time you wore it. You place that mask about your face, inoculating the outside of the mask with the plethora of bacteria and viruses present on your skin. How can such haphazard measures possibly “stop the virus in its tracks” rather than actually help spread the virus? Add to that the fact that mask use was mandated even later than was strict social distancing! There was not the application of a consistent, scientifically-supported methodology over the course of the virus progression through the population.

Let us return to our hypothetical. What if some of the condoms used by our couple had a hole in them? Again, can any conclusion be drawn? If the woman is not pregnant, is it because the condom worked anyway? If she is pregnant, was it the condom’s fault?

In fact, timing, biology, and methodology are confounded (to use a statistical term) in this analysis of the response to COVID-19. Given the lack of a consistent application of a measurably effective technique, the biological make-up of humans, and the admittedly late implementation of strict social distancing, making any assertion about how much the curve was flattened seems suspect. What if we compare epidemiological models to the actual data collected during the pandemic? Maybe the curve was just the curve?

Epidemiology. What if the virus was already relatively prevalent? This is exactly what people such as Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford, have concluded. Even more interestingly, Dr. Gupta’s initial model did not support the draconian response embraced after the Neil Ferguson model drove many politicians crazy. Her model was released one week after Ferguson’s. More recently, and in stark contrast to depending upon a model to be predictive, she is using emergent data to confirm her model. Dr. Gupta notes on a recent interview for UnHerd.com:

In almost every context, we’ve seen the epidemic grow, turn around and die away—almost like clockwork. Different countries have had different lockdown policies, and yet what we’ve observed is almost a uniform pattern of behavior that is highly consistent with the SIR Model. To me that suggests that much of the driving force here was due to the build-up of immunity. I think that’s a more parsimonious explanation than one which requires in every country for lockdown (or various degrees of lockdown, including no lockdown) to have had the same effect.

Apparently, the progression of COVID-19 though the population was typical for a virus already present and not a function of purposeful curve flattening via social distancing or lock-downs. Seems clear that continued reliance on so-called strict social distancing via lock-downs, needs to stop. An immediate reversal of any shelter-in-place orders or draconian closures of non-essential businesses, not a step-wise release, is warranted. Returning to Dr. Gupta:

Remaining in a state of lockdown is extremely dangerous from the point of view of the vulnerability of the entire population to new pathogens. Effectively we used to live in a state approximating lockdown 100 years ago, and that was what created the conditions for the Spanish Flu to come in and kill 50 million people.

Not only does remaining in lock-down hurt the economically vulnerable, it could hurt the entire population going forward. It seems clear that the damage done because of the lock-down has far outstripped even the imagined benefit from flattening the curve.

The problem is that while quarantining the sick has a long history in fighting a contagion, the approach of locking down the healthy is not nearly as tried-and-true. In a blog post from April 5, 2020, Tucker Goodrich cited a 1951 paper from the Journal of School Health:

Anderson and Arnstein in “Communicable Disease Control,” 1948, in discussing poliomyelitis, say: “School closure, as well as closure of moving picture theaters, Sunday schools, and other similar groups, is frequently attempted in response to popular demand that ‘something be done.’ Although tried repeatedly, it is of no proved value, never altering the usual curve of the epidemic: nor has the disease been more prevalent or persistent in those communities with the courage to resist those demands.”

The data indicates that Italian compliance with the lock-down orders was far higher than in the U.S. Yet, the progression of the disease showed no impact due to those lock-downs.

No one wants to minimize the current danger of COVID-19 or the deaths that have already occurred. Certainly not this author. Frankly, the severity with which the virus attacks an individual can appear to be random, with cases of healthy people with no underlying conditions being severely stricken. That said, our immune system was all we had at our disposal for the 6 million years since humankind began walking upright.

This is all we had at our disposal since the start of the COVID-19 panic. Fortunately, it appears—at least in the overwhelming majority of cases where underlying conditions are not present—to be enough. Let us do all we can to enhance it. Let us stop thrusting half-assed behavioral approaches, in some cases supported by a high school science experiment, upon the population as if they worked, despite a dearth of supporting evidence, and despite the massive longer term danger of further extending the lock-outs.

Wilt AlstonWilt Alston [send him mail] lives in Rochester, New York, with his wife and three children. When he’s not training for a marathon or furthering his part-time study of libertarian philosophy, he works as a safety engineer in transportation safety, focusing primarily on the safety of subway and freight train control systems.

Do Lockdowns Save Lives?

Do Lockdowns Save Lives?

Appearances are of four (4) kinds:

  1. Things are as they appear to be;
  2. Things neither are nor appear to be;
  3. Things are, but do not appear to be;
  4. Things are not, but yet appear to be.

     ~ Epictetus, Phrygian Philosopher

Despite what seems to me incontrovertible data that lockdowns not only did not help, but also were destructive, people on social media are still trotting out the “How many people do you want to kill?” false choice. (To my credit, this author has yet to block anyone for asking such a question. Baby steps!) The thing that troubles me, and in full disclosure this insight only recently occurred to me, is that we all appear to be debating the wrong issue. The people who support lockdowns seem to think they directly save lives. The people who decry lockdowns seem to be arguing against that specific point as if it were a valid question, given the stated purpose of lockdowns specifically and social distancing generally, which is slowing the progression of a disease through a population.

Whether or not lock-downs save lives is actually a straightforward data analysis exercise. Statistician William Briggs has presented a ton of data and analysis in this regard. In a podcast this author would highly recommended, from Ivor “The Fat Emperor” Cummins, Dr. Michael Levitt provides similar context around the issue. The bottom line: If one compares country-to-country and lockdown versus no lockdown, the data is completely mixed together. That is, there is no functionality showing that lockdowns prevent deaths.

This is not actually surprising if one understands one additional factor. There is no mechanism for lockdowns to change the number of deaths, unless and only unless, there is hospital over-crowding. Stated differently, the probability of a person dying from COVID-19 is not a function of when that person was infected, whether or not there was a lockdown, whether that person wore a mask, or any of that. Unless the laws of statistics regarding the fact that the area under a normal distribution is not effected by kurtosis—the length of the tails—there can be no effect on deaths due to curve flattening. (We covered this previously, in another article.) Comfortingly for the math geeks among us, the results (i.e., the actual data) bear this out. In a subsequent article, this author hopes to examine if what this author will term, ‘infection rate’—the rate of progression across the population, particularly in the U.S.—was significantly changed, despite all the social distancing marketing and puffery. For the time being, the focus of this article is on deaths after infection.

What are the results when one compares country-to-country with respect to strict social distancing via lockdowns? (One can assume, for the purposes of being fair, that it is entirely possible that people in some locales voluntarily practiced social distancing, despite all large gatherings and non-essential businesses not being forced out of existence by government edict.) Still though, one should see some obvious functionality between places that locked down and those that did not. Did we? The chart below is instructive.

Lockdown GraphThe Y-Axis on the left side (the vertical axis) is Population Density in People per Square Kilometer. The bars correspond to these values and show countries along the bottom. The Y-Axis on the right side (also a vertical axis) is Death Rate, i.e., Deaths per Million of Population, and is shown with the line. On that same line, a “Y” or an “N” indicates if a locale imposed a lockdown. The Death Rate values are shown on a Log10 scale, since the range between deaths per million is so large. The bars show Population Density in People per Square Kilometer for the countries listed. (Again, hat-tip to statistician William Briggs for making this data available. He also provides a similar set of charts using different variables on his site. Same conclusion.) This author downloaded Population Density data from another website and cross-referenced it by country. The data above is sorted so that countries that did not impose a lockdown are plotted separately from countries that imposed a lockdown, both ordered by Case Fatality Rate within each subgroup. Finally, fifty countries are shown on the chart.

Despite this author’s repeated attempts with different plots, there does not appear to be functionality with respect to lockdown. There does not appear to be functionality with respect to population density. One might ask if population density for a country is indicative of the population density for cities within a country—such as New York City or São Paulo—but if there was an effect from locking down, it should still be obvious. This author will stop short of suggesting that not only did lock-downs do no good, but also that they were harmful, as argued Briggs in the post noted above.

It seems clear that imposing a lock-down was not the magic bullet that certain control-enthusiast governors and presidential advisors have claimed. No improved performance, i.e., reduced death rate, is obvious from this data, and if lockdowns helped, it should be. (Similar data is available that compares U.S. states that imposed lockdowns to those that did not. Same conclusion. Similar data is available for Europe. Same conclusion.) Then again, for the reasons covered earlier, this should not be surprising. In recent days, as several states opted to “open up” critics have cried out that these states would see spikes in reported cases and resultant deaths. So far, not so much, although this data is emergent.

One other important factor which is worth understanding—and which Dr. Levitt also mentions in the podcast cited above. There is a necessity to reach virus saturation in order for us to move past this panic. Full, or almost full, population exposure is a necessity, regardless of whether there is corresponding manifestation of symptoms. We already know from the experience on the USS Theodore Roosevelt, that everyone will not manifest symptoms, regardless of exposure, and as Dr. Levitt mentions, when they may have been exposed. Writes William Sullivan for American Thinker:

The USS Theodore Roosevelt had a crew of 4,800. Given the acute sample, testing was holistic. This yields an actual infection rate of roughly 23 percent, and among those infected, the fatality rate is 0.09 percent. Among the Roosevelt’s entire crew of assumedly healthy and able-bodied sailors, on a floating Petri dish, during the thick of viral outbreak that shut down all schools and placed healthy citizens across America under house-arrest, the fatality rate was .002 percent.

In deference to the leadership of Dr. Levitt, this author is being careful not to use the somewhat loaded term, “herd immunity,” but the fact of the matter is, full exposure is necessary unless we plan to maintain the lockdowns for years. Furthermore, and with apologies for beating an ailing horse, the models used up to this point apparently assumed this. The only question was if virus saturation would happen so quickly as to “outrun” the number of beds, ventilators, and healthcare resources, given the percentage of those who were both symptomatic and needing advanced care. That did not happen.

As a final thought exercise, consider this question:  Has anyone ever died from AIDS? (Admittedly, this is something of a trick question.) AIDS stands for Acquired Immune Deficiency Syndrome. A person who succumbs to AIDS actually dies from one of many opportunistic infections. Agents that are present all the time cause these infections. The reason there is not widespread societal affliction with Karposi’s Sarcoma is that our immune system is up to the task. This is fortunate. Incidentally, this is also the primary reason that not everyone on the USS Theodore Roosevelt came down with COVID-19. As was posted on Twitter a few days ago, “The virus is tough. But humans are much, much tougher.” This despite knee-jerking politicians acting like saviors.

Wilt AlstonWilt Alston [send him mail] lives in Rochester, New York. When he is not training for a marathon or furthering his part-time study of libertarian philosophy, he works as a safety engineer in transportation safety, focusing primarily on the safety of subway and freight train control systems.

What Did Society Benefit By Social Distancing?

What Did Society Benefit By Social Distancing?

To be governed is to be watched, inspected, spied upon, directed, law-driven, numbered, regulated, enrolled, indoctrinated, preached at, controlled, checked, estimated, valued, censured, commanded, by creatures who have neither the right nor the wisdom nor the virtue to do so.”- Pierre-Joseph Proudhon

Despite the fact that there has already been ample writing, both in the mainstream and in alternative press, on this subject from authors who are both persuasive and amply qualified mathematically and scientifically, this author finds himself wanting to offer a brief entry. As someone who has spent the larger portion of his professional career designing experiments, analyzing data, generating graphs, and writing reports about all of it, articles featuring science, statistics, charts and graphs aplenty seems be natural for me. However, none of that is needed. Instead, let us confine ourselves to the most basic logic.

At present, the United States and the world are locked in the grip of the COVID-19 Pandemic. One might be inclined to call it, “The COVID-19 Panic.” If he did, this author would agree. To fight this threat to life and apparently our very existence on Earth as we know it, the public has been forced to abide by the edicts of mayors, governors, and other leadership, at the local and national level. Those edicts can be summarized via two hashtags that started to trend on Twitter about six weeks ago. To assure that we have the same understanding of these concepts, a couple of definitions are in order.

The concept of flattening the curve, represented by the hashtag #FlattenTheCurve, refers to a statistical approach to mitigating the virus’s impact on society, using what is called a normal distribution to model the number of cases over time. In real life, this type of distribution is a representation of an idealized histogram, that is, vertical bars representing the count of observations per unit time, and positioned on a graph next to each other. The unit of time could be days, hours, minutes, or anything like that. (As an aside, for the vast majority of phenomena observed in our daily lives, a normal distribution is applicable, as justified by the Central Limit Theorem. Why this is true could be the subject of another essay, or hell, an entire book.)

The basic premise is that the height of the bars—representing counts of observations—start out small, and get bigger and bigger, eventually reaching a maximum value or peak, and then returning to getting smaller and smaller. The tallest bar—or peak of the distribution—can then be thought of as the maximum number of individuals (per unit time) who actively have the virus. Let us term this ‘Maximum COVID-19 Patients’. The area under the curve, which is equivalent to simply adding up all the observations in each of the bars, is the total number of people who are stricken with COVID-19. Let us term this, ‘Total COVID-19 Cases’.

From the start of discussions of the pandemic and how to deal with it, this number—the total number of people who would be stricken with the virus, ‘Total COVID-19 Cases’—has not been the subject of major debate; that is to say, the area under the curve was not expected to change markedly. In fact, no one in his or her right mind thought that anything could be done to stop the spread of the disease via behavior. The best we could hope for would be to slow the spread, ostensibly so that the medical establishment—hospitals and other front-line structures—could deal with the onslaught. (One might, if he were optimistic, think that we could develop and distribute a vaccine quickly enough that lengthening societal exposure time was no big deal. That is, if he were an idiot.) That everyone, or effectively everyone, would eventually be exposed was not in doubt.

Flattening the curve could, at best, decrease that peak value, i.e., the maximum number of people exhibiting the disease at a point in time, in exchange for a longer timeframe of population exposure, what I will term ‘Societal Exposure Time’. In summary, ‘Total COVID-19 Cases’ (the area under the curve) would be unchanged, but we would exchange a lower ‘Maximum COVID-19 Patients’ for a longer ‘Societal Exposure Time’. Total number of deaths, unchanged. Length of time, extended. Put a pin in that point.

The concept of social distancing, represented by the hashtag #SocialDistancing, refers to the limiting interpersonal contact. Standing X feet from someone, or wearing a mask, or canceling events that are crowd-centric (such as basketball games or concerts) are all implementations of social distancing. The same is true of forcing the temporary closure of ostensibly “non-essential” businesses. So then, #FlattenTheCurve is the what, and #SocialDistancing is the how. No matter what methodology is utilized for social distancing, it is a means that has, as its raison d’être, flattening the curve. Put a pin in that point as well.

So then, flattening the curve via social distancing could produce, at best, one outcome: slow the progression of the disease so as to limit the loading on hospitals and treatment centers. In a perfect world, that outcome could also result in fewer deaths overall, i.e., a reduction in ‘Maximum COVID-19 Patients’ could, given limited medical resources, decrease the net number of deaths. As already noted, built into this approach is the secondary effect that it also must increase ‘Societal Exposure Time’. Slowing the progression of the disease means, automatically, that the disease is present in a society for longer, other factors being equal.

The direct outcome of government-imposed social distancing was a lock-down on businesses such as bars, restaurants, as well as sporting events and concerts, and on non-essential businesses. This led inexorably to limited or no income for certain sectors of the economy. That lack of income placed a huge strain on people who depended upon the “interaction economy,” such as hospitality and those non-essential businesses, to exist, i.e., people who are paid by or receive a large percentage of their income from those industries and industries related to or dependent upon them. The calculus of this lock-down on the economy generally, and these specific sectors of the economy in particular, was supposedly always a consideration, although it is becoming increasingly obvious that it was not given full examination by those with the power to impose the lock-downs. A simple trade-off was presented: “Put a little strain on a few industries now, and save lives as a result.”

However, and this is the worrisome case, if flattening the curve via social distancing does not result in fewer deaths, which it could only do in the event that hospitals were overwhelmed, then the best-case result of the approach is to only increase ‘Societal Exposure Time’. The net effect of supposedly flattening the curve, assuming the curve was actually flattened, could actually be looked upon as wasted time, while people dependent on the interaction economy and/or unlucky enough to work in an ostensibly non-essential sector have limited, reduced, or no income—along with all the related secondary and tertiary industries who supply or are served by them. From much of the reporting, the vast majority of hospitals were not overwhelmed.

One could even argue if the healthcare establishment would have been overwhelmed without ostensibly flattening the curve. While it is possible that in some places, such as New York City or Detroit, social distancing had some effect, it is equally likely, nay probable, that some social distancing, without lockdowns, would have sufficed almost everywhere else. Moreover, what if the progression of COVID-19 through the population was unaffected by social distancing? Every year the flu comes and goes and not everyone gets it. This, despite almost no social distancing practices and despite the fact that not everyone gets vaccinated, all without machinations such as lock-downs imposed on the public and marketed with puffery such as, “Stay Home and Save a Life.” By virtually any evaluation then, flattening the curve via social distancing had almost no net positive effect for the majority of the United States! Zilch. Zip. Zero. Bupkis.

Even if these draconian lock-downs did have a positive effect, (and that is a big-assed IF) the time for them is long over. The increased ‘Societal Exposure Time’ has turned directly into massive negative economic impact across multiple sectors of the economy. And let us be clear, this negative economic impact is not about rich dudes going on fewer vacations, it is about the people who previously depended upon the interaction economy—that ecosystem of businesses, one of them the hospitality and restaurant sector and another of them the supposedly non-essential sector—for income to eat and pay bills. Those people are part of the over twenty-five million people who have gone from working to unemployed over a few weeks as a result of those lock-downs.

Will they find new employment quickly? Will businesses closed in the wreckage of bungled government approach to COVID-19 rapidly re-open? Who knows? Doubtful on both counts. Built into the supposed calculus of flattening the curve via social distancing was the horribly simplified and sound-byte-ready idea and/or belief that “saving lives trumps worrying about any negative economic effects.” The negative side of that calculus was evidently never fully grasped, particularly in the event that flattening the curve via social distancing did not result in markedly fewer deaths, which it did not.

This is exactly where we are today in the United States: massive negative economic impact and still no obvious plan to immediately remove the government-imposed lock-downs. Little (if any) benefit, but all the pain—with more pain on the horizon. The fact that many government leaders are taking a measured, pensive approach to ending the lock-downs and thereby un-doing what they did with knee-jerking half-assery is laughable. The government-mandated lock-downs should end just as quickly as they were implemented. That none of the losers who imposed them is likely to apologize for any of the irreparable damage done to society is par for the course.

Wilt AlstonWilt Alston [send him mail] lives in Rochester, New York, with his wife and three children. When he’s not training for a marathon or furthering his part-time study of libertarian philosophy, he works as a safety engineer in transportation safety, focusing primarily on the safety of subway and freight train control systems.

Why Didn’t This Election Scare Me?

Why Didn’t This Election Scare Me?

“A Drunk Man’s Mouth Speaks With a Sober Man’s Heart.”

As the presidential race played out, there was a recurring hue and cry that, “This time it’s different!” Pundits all over the mainscream media proclaimed it. Psychologists tried to explain it. My brother even texted me about my plans to seek safety, buy ammo, make sandwiches, or otherwise get ready for when all hell broke loose after the election. I openly admit that at various points, I too thought, “Are these two arrogant megalomaniacs are all that’s left?” (By the way, is it just me, or does the American political process better resemble a professional wrestling cage match versus a time-honored quest for intelligent leadership?) I finally concluded that this election wasn’t really all that different, and there was never really a need for special consideration.


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