Category Archives: Public Health

Fauci Flubs Herd Immunity

03 Sunday Jan 2021

Posted by in Coronavirus, Herd Immunity, Public Health, Vaccinations

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Anthony Fauci has repeatedly increased his estimate of how much of the population must be vaccinated to achieve what he calls herd immunity, and he did it again in late December. This series of changes, and other mixed messages he’s delivered in the past, reveal Fauci to be a “public servant” who feels no obligation to level with the public. Instead, he crafts messages based on what he believes the public will accept, or on his sense of how the public must be manipulated. For example, by his own admission, his estimates of herd immunity have been sensitive to polling data! He reasoned that if more people reported a willingness to take a vaccine, he’d have flexibility to increase his “public” estimate of the percentage that must be vaccinated for herd immunity. Even worse, Fauci appears to lack a solid understanding of the very concept of herd immunity.

Manipulation

There is so much wrong with his reasoning on this point that it’s hard to know where to start. In the first place, why in the world would anyone think that if more people willingly vaccinate it would imply that even more must vaccinate? And if he felt that way all along it demonstrates an earlier willingness to be dishonest with the public. Of course, there was nothing scientific about it: the series of estimates was purely manipulative. It’s almost painful to consider the sort of public servant who’d engage in such mental machinations.

Immunity Is Multi-Faceted

Second, Fauci seemingly wants to convince us that herd immunity is solely dependent on vaccination. Far from it, and I’m sure he knows that, so perhaps this too was manipulative. Fauci intimates that COVID herd immunity must look something like herd immunity to the measles, which is laughable. Measles is a viral infection primarily in children, among whom there is little if any pre-immunity. The measles vaccine (MMR) is administered to young children along with occasional boosters for some individuals. Believe it or not, Fauci claims that he rationalized a requirement of 85% vaccination for COVID by discounting a 90% requirement for the measles! Really???

In fact, there is substantial acquired pre-immunity to COVID. A meaningful share of the population has long-memory, cross-reactive T-cells from earlier exposure to coronaviruses such as the common cold. Estimates range from 10% to as much as 50%. So if we stick with Fauci’s 85% herd immunity “guesstimate”, 25% pre-immunity implies that vaccinating only 60% of the population would get us to Fauci’s herd immunity goal. (Two qualifications: 1) the vaccines aren’t 100% effective, so it would take more than 60% vaccinated to offset the failure rate; 2) the pre-immune might not be identifiable at low cost, so there might be significant overlap between the pre-immune and those vaccinated.)

Conceptual Confusion

Vaccinations approaching 85% would be an extremely ambitious goal, especially if it is recommended annually or semi-annually. It would be virtually impossible without coercion. While more than 91% of children are vaccinated for measles in the U.S., it is not annual. Thus, measles does not offer an appropriate model for thinking about herd immunity to COVID. Less than half of adults get a flu shot each year, and somewhat more children.

Fauci’s reference to 85% – 90% total immunity is different from the concept of the herd immunity threshold (HIT) in standard epidemiological models. The HIT, often placed in the range of 60% – 70%, is the point at which new infections begin to decline. More infections occur above the HIT but at a diminishing rate. In the end, the total share of individuals who become immune due to exposure, pre-immunity or vaccination will be greater than the HIT. The point is, however, that reaching the HIT is a sufficient condition for cases to taper and an end to a contagion. If we use 65% as the HIT and pre-immunity of 25%, only 40% must be vaccinated to reach the HIT.

Heterogeneity

A recent innovation in epidemiological models is the recognition that there are tremendous differences between individuals in terms of transmissibility, pre-immunity, and other factors that influence the spread of a particular virus, including social and business arrangements. This kind of heterogeneity tends to reduce the effective HIT. We’ve already discussed the effect of pre-immunity. Suppose that certain individuals are much more likely to transmit the virus than others, like so-called super-spreaders. They spur the initial exponential growth of a contagion, but there are only so many of them. Once infected, no one else among the still-susceptible can spread the virus with the same force.

Researchers at the Max Planck Institute (and a number of others) have gauged the effect of introducing heterogeneity to standard epidemiological models. It is dramatic, as the following chart shows. The curves simulate a pandemic under different assumptions about the degree of heterogeneity. The peak of these curves correspond to the HIT under each assumption (R0 refers to the initial reproduction number from infected individuals to others).

Moderate heterogeneity implies a HIT of only 37%. Given pre-immunity of 25%, only an additional 12% of the population would have to be infected or vaccinated to prevent a contagion from gaining a foothold for the initial exponential stage of growth. Fauci’s herd immunity figure obviously fails to consider the effect of heterogeneity.

How Close To the HIT?

We’re not as far from HITs as Fauci might think, and a vaccination goal of 85% is absurd and unnecessary. The seasonal COVID waves we’ve experienced thus far have faded over a period of 10-12 weeks. Estimates of seroprevalence in many localities reached a range of 15% – 25% after those episodes, which probably includes some share of those with pre-immunity. To reach the likely range of a HIT, either some additional pre-immunity must have existed or the degree of heterogeneity must have been large in these populations.

But if that’s true, why did secondary waves occur in the fall? There are a few possibilities. Of course, some areas like the upper Midwest did not experience the springtime wave. But in areas that suffered a recurrance, perhaps the antibodies acquired from infections did not remain active for as long as six months. However, other immune cells have longer memories, and re-infections have been fairly rare. Another possibility is that those having some level of pre-immunity were still able to pass live virus along to new hosts. But this vector of transmission would probably have been quite limited. Pre-immunity almost surely varies from region to region, so some areas were not as firmly above their HITs as others. It’s also possible that infections from super-spreaders were concentrated within subsets of the population even within a given region, in certain neighborhoods or among some, but not all, social or business circles. Therefore, some subsets or “sub-herds” achieved a HIT in the first wave, but it was unnecessary for other groups. In other words, sub-herds spared in the first wave might have suffered a contagion in a subsequent wave. And again, reinfections seem to have been rare. Finally, there is the possibility of a reset in the HIT in the presence of a new, more transmissible variant of the virus, as has become prevalent in the UK, but that was not the case in the fall.

Fragility

Tyler Cowen has mentioned another possible explanation: so-called “fragile” herd immunity. The idea is that any particular HIT is dependent on the structure of social relations. When social distancing is widely practiced, for example, the HIT will be lower. But if, after a contagion recedes, social distancing is relaxed, it’s possible that the HIT will take a higher value at the onset of the next seasonal wave. Perhaps this played a role in the resurgence in infections in the fall, but the HIT can be reduced via voluntary distancing. Eventually, acquired immunity and vaccinations will achieve a HIT under which distancing should be unnecessary, and heterogeneity suggests that shouldn’t be far out of reach.

Conclusion

Anthony Fauci has too often changed his public pronouncements on critical issues related to management of the COVID pandemic. Last February he said cruises were fine for the healthy and that most people should live their lives normally. Oops! Then came his opinion on the limited effectiveness of masks, then a shift to their necessity. His first position on masks has been called a “noble lie” intended to preserve supplies for health care workers. However, Fauci was probably repeating the standing consensus at that point (and still the truth) that masks are of limited value in containing airborne pathogens.

This time, Fauci admitted to changing his estimate of “herd immunity” in response to public opinion, a pathetic approach to matters of public health. What he called herd immunity was really an opinion about adequate levels of vaccination. Furthermore, he neglected to consider other forms of immunity: pre-existing and already acquired. He did not distinguish between total immunity and the herd immunity threshold that should guide any discussion of pandemic management. He also neglected the significant advances in epidemiological modeling that recognize the reality of heterogeneity in reducing the herd immunity threshold. The upshot is that far fewer vaccinations are needed to contain future waves of the pandemic than Fauci suggests.

Allocating Vaccine Supplies: Lives or “Justice”?

29 Tuesday Dec 2020

Posted by in Pandemic, Public Health, Uncategorized, Vaccinations

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There are currently two vaccines in limited distribution across the U.S. from Pfizer and Moderna, but the number and variety of different vaccines will grow as we move through the winter. For now, the vaccine is in short supply, but that’s even more a matter of administering doses in a timely way as it is the quantity on hand. There are competing theories about how best to allocate the available doses, which is the subject of this post. I won’t debate the merits of refusing to take a vaccine except to say that I support anyone’s right to refuse it without coercion by public authorities. I also note that certain forms of discrimination on that basis are not necessarily unreasonable.

The vaccines in play all seem to be highly effective (> 90%, which is incredible by existing standards). There have been a few reports of side effects — certainly not in large numbers — but it remains to be seen whether the vaccines will have any long-term side effects. I’m optimistic, but I won’t dismiss the possibility.

Despite competing doctrines about how the available supplies of vaccine should be allocated, there is widespread acceptance that health care workers should go first. I have some reservations about this because, like Emma Woodhouse, I believe staff and residents at long-term care facilities should have at least equal priority. Yet they do not in the City of Chicago and probably in other areas. I have to wonder whether unionized health care workers there are the beneficiaries of political favoritism.

Beyond that question, we have the following competing priorities: 1) the vulnerable in care homes and other elderly individuals (75+, while younger individuals with co-morbidities come later); 2) “essential” workers of all ages (from police to grocery store clerks — decidedly arbitrary); and 3) basically the same as #2 with priority given to groups who have suffered historical inequities.

#1 is clearly the way to save the most lives, at least in the short-run. Over 40% of the deaths in the U.S. have been in elder-care settings, and COVID infection fatality rates mount exponentially with age:

To derive the implications of #1 and #2, it’s more convenient to look at the share of deaths within each age cohort, since it incorporates the differences in infection rates and fatality rates across age groups (the number of “other” deaths is much larger than COVID deaths, of course, despite similar death shares):

The 75+ age group has accounted for about 58% of all COVID deaths in the U.S., and ages 25 – 64 accounted for about 20% (an approximate age range for essential workers). This implies that nearly three times as many lives can be saved by prioritizing the elderly, at least if deaths among so-called essential workers mimic deaths in the 25 – 64 age cohorts. However, the gap would be smaller and perhaps reversed in terms of life-years saved.

Furthermore, this is a short-run calculation. Over a longer time frame, if essential workers are responsible for more transmission across all ages than the elderly, then it might throw the advantage to prioritizing essential workers over the elderly, but it would take a number of transmission cycles for the differential to play out. Yes, essential workers are more likely to be “super-spreaders” than work-at-home, corporate employees, or even the unemployed, but identifying true super-spreaders would require considerable luck. Moreover, care homes generally house a substantial number of elderly individuals and staff in a confined environment, where spread is likely to be rampant. So the transmission argument for #2 over #1 is questionable.

The over-riding problem is that of available supply. Suppose enough vaccine is available for all elderly individuals within a particular time frame. That’s about 6.6% of the total U.S. population. The same supply would cover only about 13% of the younger age group identified above. Essential workers are a subset of that group, but the same supply would fall far short of vaccinating all of them; lives saved under #2 would then fall far short of the lives saved under #1. Quantities of the vaccine are likely to increase over the course of a few months, but limited supplies at the outset force us to focus the allocation decision on the short-term, making #1 the clear winner.

Now let’s talk about #3, minority populations, historical inequities, and the logic of allocating vaccine on that basis. Minority populations have suffered disproportionately from COVID, so this is really a matter of objective risk, not historical inequities… unless the idea is to treat vaccine allocations as a form of reparation. Don’t laugh — that might not be far from the intent, and it won’t count as a credit toward the next demand for “justice”.

For the sake of argument, let’s assume that minorities have 3x the fatality rate of whites from COVID (a little high). Roughly 40% of the U.S. population is non-white or Hispanic. That’s more than six times the size of the full 75+ population. If all of the available doses were delivered to essential workers in that group, it would cover less than half of them and save perhaps 30% of minority COVID deaths over a few months. In contrast, minorities might account for up to two-thirds of the deaths among the elderly. Therefore, vaccinating all of the elderly would save 58% of elderly COVID deaths and about 39% of minority deaths overall!

The COVID mortality risk to the average white individual in the elderly population is far greater than that faced by the average minority individual in the working age population. Therefore, no part of #3 is sensible from a purely mathematical perspective. Race/ethnicity overlaps significantly with various co-morbidities and the number of co-morbidities with which individuals are afflicted. Further analysis might reveal whether there is more to be gained by prioritizing by co-morbidities rather than race/ethnicity.

Megan McArdle has an interesting column on the CDC’s vaccination guidelines issued in November, which emphasized equity, like #3 above. But the CDC walked back that decision in December. The initial November decision was merely the latest of the the agency’s fumbles on COVID policy. In her column, McArdle notes that the public has understood that the priority was to save lives since the very start of the pandemic. Ideally, if objective measures show that identifiable characteristics are associated with greater vulnerability, then those should be considered in prioritizing individuals who desire vaccinations. This includes age, co-morbidities, race/ethnicity, and elements of occupational risk. But lesser associations with risk should not take precedence over greater associations with risk unless an advantage can be demonstrated in terms of lives saved, historical inequities or otherwise.

The priorities for the early rounds of vaccinations may differ by state or jurisdiction, but they are all heavily influenced by the CDC’s guidelines. Some states pay lip service to equity considerations (if they simply said race/ethnicity, they’d be forced to operationalize it), while others might actually prioritize doses by race/ethnicity to some degree. Once the initial phase of vaccinations is complete, there are likely to be more granular prioritizations based on different co-morbidities, for example, as well as race/ethnicity. Thankfully, the most severe risk gradient, advanced age, will have been addressed by then.

One last point: the Pfizer and Moderna vaccines both require two doses. Alex Tabarrok points out that first doses appear to be highly effective on their own. In his opinion, while supplies are short, the second dose should be delayed until all groups at substantially elevated risk can be vaccinated…. doubling the supply of initial doses! The idea has merit, but it is unlikely to receive much consideration in the U.S. except to the extent that supply chain problems make it unavoidable, and they might.

Spate of Research Shows COVID Lockdowns Fail

27 Sunday Dec 2020

Posted by in Lockdowns, Public Health

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For clarity, start with this charming interpretive one-act on public health policy in 2020. You might find it a little sardonic, but that’s the point. It was one of the more entertaining tweets of the day, from @boriquagato.

A growing body of research shows that stringent non-pharmaceutical interventions (NPIs) — “lockdowns” is an often-used shorthand — are not effective in stemming the transmission and spread of COVID-19. A compendium of articles and preprints on the topic was just published by the American Institute for Economic Research (AEIR): “Lockdowns Do Not Control the Coronavirus: The Evidence”. The list was compiled originally by Ivor Cummins, and he has added a few more articles and other relevant materials to the list. The links span research on lockdowns across the globe. It covers transmission, mortality, and other health outcomes, as well as the economic effects of lockdowns. AIER states the following:

“Perhaps this is a shocking revelation, given that universal social and economic controls are becoming the new orthodoxy. In a saner world, the burden of proof really should belong to the lockdowners, since it is they who overthrew 100 years of public-health wisdom and replaced it with an untested, top-down imposition on freedom and human rights. They never accepted that burden. They took it as axiomatic that a virus could be intimidated and frightened by credentials, edicts, speeches, and masked gendarmes.

The pro-lockdown evidence is shockingly thin, and based largely on comparing real-world outcomes against dire computer-generated forecasts derived from empirically untested models, and then merely positing that stringencies and “nonpharmaceutical interventions” account for the difference between the fictionalized vs. the real outcome. The anti-lockdown studies, on the other hand, are evidence-based, robust, and thorough, grappling with the data we have (with all its flaws) and looking at the results in light of controls on the population.”

We are constantly told that public intervention constitutes “leadership”, as if our well being depends upon behavioral control by the state. Unfortunately, it’s all too typical of research on phenomena deemed ripe for intervention that computer models are employed to “prove” the case. A common practice is to calibrate such models so that the outputs mimic certain historical outcomes. Unfortunately, a wide range of model specifications can be compatible with an historical record. This practice is also a far cry from empirically testing well-defined hypotheses against alternatives. And it is a practice that usually does poorly when the model is tested outside the period to which it is calibrated. Yet that is the kind of evidence that proponents of intervention are fond of using to support their policy prescriptions.

In this case, it’s even worse, with some of the alleged positive effects of NPI’s wholly made-up, with no empirical support whatsoever! So-called public health experts have misled themselves, and the public, with this kind of fake evidence, when they aren’t too busy talking out of both sides of their mouths.

COVID Testing: Cycle Thresholds and Coffee Grounds

19 Saturday Dec 2020

Posted by in Coronavirus, Public Health, Uncategorized

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Here’s some incredible data on PCR tests demonstrating a radically excessive lab practice that generates false positives. I’m almost tempted to say we’d do just as well using a thermometer and the coffee ground test. Open a coffee tin and take a sniff. Can you smell the distinct aroma of the grounds? If not, and if you have other common symptoms, there’s a decent chance you have an active COVID infection. That test is actually in use in some parts of the globe!

The data shown below on PCR tests are from the Rhode Island Department of a Health and the Rhode Island State Health Lab. They summarize over 5,000 positive COVID PCR tests (collected via deep nasal swabs) taken from late March through early July. The vertical axis in the chart measures the cycle threshold (Ct) value of each positive test. Ct is the number of times the RNA in a sample must be replicated before any COVID-19 (or COVID-like) RNA is detected. It might be from a live virus or perhaps a fragment of a dead virus. A positive test with a low Ct value indicates that the subject is likely infected with billions of live COVID-19 viruses, while a high Ct value indicates perhaps a handful or no live virus at all.

The range of red dots in the chart (< 28 Ct) indicates relatively low Ct values and active infections. The yellow range of dots, for which 28 < Ct <= 32, indicates possible infections, and the upper range of green dots, where Ct > 32, indicates that active infections were highly unlikely. It’s important to note that all of these tests were recorded as new COVID cases, so the range of Ct values suggest that testing in Rhode Island was unreasonably sensitive. That’s broadly true across the U.S. as well, which means that COVID cases are over-counted by perhaps 30% or more. And yet it is extremely difficult for subjects testing positive to learn their Ct values. You can ask, but you probably won’t get an answer, which is absurd and counterproductive.

Notice that the concentration of red dots diminished over time, and we know that the spring wave of the virus in the Northeast was waning as the summer approached. The share of positives tests with high Ct values increased over that time frame, however. This is borne out by the next chart, which shows the daily mean Ct of these positive tests. The chart shows that active infections became increasingly rare over that time frame both because positive tests decreased and the average Ct value rose. What we don’t know is whether labs bumped up the number of cycles or replications to which samples were subjected. Still, the trend is rather disturbing because most of the positive cases in May and the first half of June were more likely to be virus remnants than live viruses.

It’s also worth noting that COVID deaths declined in concert with the upward trend in Ct values. This is shown in the chart below (where the Ct scale is inverted). This demonstrates the truly benign nature of positive tests having high Ct values.

This is also demonstrated by the following data from a New York City academic hospital, which was posted by Andrew Bostom. It shows that a more favorable “clinical status” of COVID patients is associated with higher Ct values.

It’s astounding that the U.S. has relied so heavily on a diagnostic tool that gets so many subjects wrong. And it’s nearly impossible for subjects testing positive to obtain their Ct values. Instead, they are subject to self-quarantine for up to two weeks. Even worse, until recently there were delays in reporting the results of these tests of up to a week or more. That made them extremely unhelpful. On the other hand, the coffee ground test is fast and cheap, and it might enhance the credibility of a subsequent positive PCR test, if one is necessary … and especially if the lab won’t report the Ct value.

The PCR test has identified far too many false infections, but it wouldn’t have been quite so damaging if 1) a reasonably low maximum cycle threshold had been established; 2) test results had not been subject to such long delays; and 3) rapid retests had been available for confirmation. The cycle threshold issue is starting to receive more attention, quite belatedly, and more rapid tests have become available. As I’ve emphasized in the past, cheap, rapid tests exist. But having dithered in February and March in approving even the PCR test, the FDA has remained extremely grudging in approving newer tests, and it persists in creating obstacles to their use. The FDA needs to wake up and smell the coffee!

COVID Interventions: Costly, Deadly, and Ineffective

14 Monday Dec 2020

Posted by in Coronavirus, Liberty, Lockdowns, Public Health

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What does it take to shake people out of their statist stupor? Evidently, the sweet “logic” of universal confinement is very appealing to the prescriptive mindset of busybodies everywhere, who anxiously wag their fingers at those whom they view as insufficiently frightened. As difficult as it is for these shrieking, authoritarian curs to fathom, measures like lockdowns, restrictions on business activity, school closures, and mandates on behavior have at best a limited impact on the spread of the coronavirus, and they are enormously costly in terms of economic well-being and many dimensions of public health. Yet the storm of propaganda to the contrary continues. Media outlets routinely run scare stories, dwelling on rising case numbers but ignoring them when they fall; they emphasize inflated measures of pandemic severity; certain researchers and so-called health experts can’t learn the lessons that are plain in the data; and too many public officials feel compelled to assert presumed but unconstitutional powers. At least the World Health Organization has managed to see things clearly, but many don’t want to listen.

I’ll be the first to say I thought the federalist approach to COVID policy was commendable: allow states and local governments to craft policies appropriate to local conditions and political preferences, rather than have the federal government dictate a one-size-fits-all policy. I haven’t wavered in that assessment, but let’s just say I expected more variety. What I failed to appreciate was the extent to which state and local leaders are captive to provincial busybodies, mavens of precautionary excess, and fraudulent claims to scientific wisdom.

Of course, it should be obvious that the “knowledge problem” articulated by Friedrich Hayek is just as dangerous at low-levels of government as it is in a central Leviathan. And it’s not just a knowledge problem, but a political problem: officials become panicked because they fear bad outcomes will spell doom for their careers. Politicians are particularly prone to the hazards of “do-somethingism”, especially if they have willing, status-seeking “experts” to back them up. But as Scott Sumner says:

“When issues strongly impact society, the science no longer ‘speaks for itself’.

Well, the science is not quite as clear as the “follow-the-science” crowd would have you believe. And unfortunately, public officials have little interest in sober assessments of the unintended effects of lockdown policy.

In my last post, I presented a simple framework for thinking about the benefits and costs of lockdown measures, or non-pharmaceutical interventions (NPIs). I also emphasized the knowledge problem: even if there is some point at which NPI stringencies are “optimized”, government does not possess the knowledge to find that point. It lacks detailed information on both the costs and benefits of NPIs, but individual actors know their own tolerance for risk, and they surely have some sense of the risks they pose to others in their normal course of affairs. While voluntary precautions might be imperfect, they accomplish much of what interventionists hope will be gained via coercion. But, in an effort to “sell” NPIs to constituents and assert their authority, officials vastly over-estimate benefits of NPIs and under-estimate the costs.

NPI Stringency and COVID Outcomes

Let’s take a look at a measure of the strength of NPIs by state — the University of Oxford Stringency Index — and compare those to CDC all-cause excess deaths in each state. If it’s hard to read, try clicking on the image or turn your phone sideways. This plot covers outcomes through mid-November:

The chart doesn’t suggest any benefit to the imposition of greater restrictions, or more stringent NPIs. In fact, the truth is that people will do most of the work on their own based on perceptions of risk. That’s partly because government restrictions add little risk mitigation to what can be accomplished by voluntary social distancing and other precautions.

Here’s a similar chart with cross-country comparisons, though the data here ended in early October (I apologize for the fuzzy image):

But what about reverse causality? Maybe the imposition of stringency was a response to more severe contagions. Now that the virus has swept most of the U.S and Europe in three distinct waves, and given the variety and timing of NPIs that have been tried, it’s harder to make that argument. States like South Dakota have done fairly well with low stringency, while states like New Jersey with high stringency have fared poorly. The charts above provide multiple pair-wise examples and counter-examples of states or countries having faced hard waves with different results.

But let’s look at a few specific situations.

The countries shown above have converged somewhat over the past month: Sweden’s daily deaths have risen while the others have declined to greater or lesser degrees, but the implications for mask usage are unaltered.

And of course we have this gem, predicated on the mental gymnastics lockdown enthusiasts are fond of performing:

But seriously, it’s been a typical pattern: cases rise to a point at which officials muster the political will to impose restrictions, often well after the “exponential” phase of the wave or even the peak has passed. For the sake of argument, if we were to stipulate that lockdowns save lives, it would take time for these measures to mitigate new infections, time for some of the infected individuals to become symptomatic, and more time for diagnosis. For the lockdown arguments to be persuasive, the implementation of NPIs would have to precede the point at which the growth of cases begins to decline by a few weeks. That’s something we’ve seldom observed, but officials always seem to take credit for the inevitable decline in cases.

More informed lockdown proponents have been hanging their hats on this paper in Nature by Seth Flaxman, et al, published in July. As Philippe LeMoine has shown, however, Flaxman and his coauthors essentially assumed their result. After a fairly exhaustive analysis, Lemoine, a man who understands sophisticated mathematics, offers these damning comments:

“Their paper is a prime example of propaganda masquerading as science that weaponizes complicated mathematics to promote questionable policies. Complicated mathematics always impresses people because they don’t understand it and it makes the analysis look scientific, but often it’s used to launder totally implausible assumptions, which anyone could recognize as such if they were stated in plain language. I think it’s exactly what happened with Flaxman et al.’s paper, which has been used as a cudgel to defend lockdowns, even though it has no practical relevance whatsoever.”

The Economic Costs of Stringency

So the benefits of stringent lockdowns in terms of averting sickness and death from COVID are speculative at best. What about the costs of lockdowns? We can start with their negative impact on economic activity:

That’s a pretty bad reflection on NPI stringency. In the U.S, a 10% decline in GDP in 2020 amounts to about $2.1 trillion in lost goods and services. That’s just for starters. The many destroyed businesses and livelihoods carry an ongoing cost that could take years to fade, as this graphic on permanent business closures shows:

If you’re wondering about the distributional effects of lockdowns, here’s more bad news:

It’s possible to do many high-paying jobs from home. Not so for blue-collar workers. And distributional effects by size of enterprise are also heavily-skewed in favor of big companies. Within the retail industry, big-box stores are often designated as “essential”, while small shops and restaurants are not. The restaurant industry has been destroyed in many areas, inflicting a huge blow to owners and workers. This despite evidence from contact tracing showing that restaurants and bars account for a very small share of transmission. To add insult to injury, many restaurants invested heavily in safety measures and equipment to facilitate new, safer ways of doing business, only to be double-crossed by officials like Andrew Cuomo and Eric Garcetti, who later shut them down.

Public Health Costs of Stringency

Lives are lost due to lockdowns, but here’s a little exercise for the sake of argument: The life value implied by individual willingness-to-pay for risk reduction comes in at less than $4 million. Even if the supposed 300,000 COVID deaths had all been saved by lockdowns, that would have amounted to a value of $1.2 trillion, about half of the GDP loss indicated above. Of course, it would be outrageously generous to concede that lives saved by NPI’s have approached 300,000, so lockdowns fall far short at the very outset of any cost-benefit comparison, even if we value individual lives at far more than $4 million.

As AJ Kay says, the social and human costs go far beyond economic losses:

I cited specific examples of losses in many of these categories in an earlier post. But for the moment, instead of focusing on causes of death, take a look at this table provided by Justin Hart showing a measure of non-COVID excess deaths by age group in the far right-hand column:

The numbers here are derived by averaging deaths by age group over the previous five years and subtracting COVID deaths in each group. I believe Hart’s numbers go through November. Of greatest interest here is the fact that younger age groups, having far less risk of death from COVID than older age groups, have suffered large numbers of excess deaths NOT attributed to COVID. As Hart notes later in his thread:

These deaths are a tragic consequence of lockdowns.

Educational Costs of Stringency

Many schools have been closed to in-person instruction during the pandemic, leading to severe disruptions to the education f children. This report from the Fairfax County, VA School District is indicative, and it is extremely disheartening. The report includes the following table:

Note the deterioration for disabled students, English learners, and the economically disadvantaged. The surfeit of failing grades is especially damaging to groups already struggling in school relative to their peers, such as blacks and Hispanics. Not only has the disruption to in-person instruction been disastrous to many students and their futures; it has also yielded little benefit in mitigating the contagion. A recent study in The Lancet confirms once again that transmission is low in educational settings. Also see here and here for more evidence on that point.

Conclusion

It’s clear that the “follow-the-science” mantra as a rationale for stringent NPIs was always a fraud, as was the knee-jerk response from those who conflated lockdowns with “leadership”. Such was the wrongheaded and ultimately deadly pressure to “do something”. We can be thankful that pressure was resisted at the federal level by President Trump. The extraordinary damage inflicted by ongoing NPIs was quite foreseeable, but there is one more very ominous implication. I’ll allow J.D. Tucille to sum that up with some of the pointed quotes he provides:

“‘The first global pandemic of the digital age has accelerated the international adoption of surveillance and public security technologies, normalising new forms of widespread, overt state surveillance,’ warned Kelsey Munro and Danielle Cave of the Australian Strategic Policy Institute’s Cyber Policy Centre last month.

‘Numerous governments have used the COVID-pandemic to repress expression in violation of their obligations under human rights law,’ United Nations Special Rapporteur on Freedom of Expression David Kaye noted in July.

‘For authoritarian-minded leaders, the coronavirus crisis is offering a convenient pretext to silence critics and consolidate power,’ Human Rights Watch warned back in April.

There’s widespread agreement, then, that government officials around the world are exploiting the pandemic to expand their power and to suppress opposition. That’s the case not only among the usual suspects where authorities don’t pretend to take elections and civil liberties seriously, but also in countries that are traditionally considered ‘free.’ … It’s wildly optimistic to expect that newly acquired surveillance tools and enforcement powers will simply evaporate once COVID-19 is sent on its way. The post-pandemic new normal is almost certain to be more authoritarian than what went before.”

COVID Externalities: the Costs and Benefits of Intervention

13 Sunday Dec 2020

Posted by in Coronavirus, Public Health, Social Costs

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This post offers a simple representation of the argument against public non-pharmaceutical interventions (NPIs) to subdue the COVID-19 pandemic. The chart below features two lines, one representing the presumed life-saving benefits of lockdown measures or NPI stringency, and another representing the costs inflicted by those measures. The values on the axes here are not critical, though measures of stringency exist (e.g., the University of Oxford Stringency Index) and take values from zero to 100.

The benefits of lives saved due to NPI stringency are assigned a value on the vertical axis, as are the costs of lives lost due to deferred health care, isolation, and other stressors caused by stringency. In addition, there are the more straightforward losses caused by suspending economic activity, which should be included in costs.

One can think of the benefits curve as representing gains from forcing individuals, via lockdown measures, to internalize the external costs of risk inflicted on others. However, this curve captures only benefits incremental to those achieved through voluntary action. Thus, NPI benefits include only extra gains from coercing individuals to internalize risks, while losses from NPI stringency are captured by the cost curve.

My contention is that the benefits of stringency diminish and may in fact turn down at some point, and that costs always increase in the level of stringency. In the chart, for what it’s worth, the “optimal” level of stringency would be at a value of 2, where the difference between total benefits and total costs is maximized (and where the benefits of incremental stringency are equal to the marginal costs or losses). However, I am not convinced that the benefits of lockdown measures ever exceed costs, as they do in the chart above. That is, voluntary action may be sufficient. But if the benefits of NPIs do exceed costs, it’s likely to be only at low levels of stringency.

To the extent that people are aware of the pandemic and recognize risk, the external costs of possible infectiousness are already internalized to some degree. Moreover, there is mutual risk in most interactions, and all individuals face risks that are proportional to those to which they expose others: if your contacts are more varied and your interactions are more frequent and intimate, you face correspondingly higher risks yourself. After all, in a pandemic, an individual’s failure to exercise caution may lead to a very hard internalization of costs if an infection strikes them. This mutuality is an element absent from most situations involving externalities. And to the extent that you take voluntary precautions, you and your contacts both benefit. Nevertheless, I concede that there are individuals who face less risk themselves (the young or healthy) but who might behave recklessly, and they might not internalize all risk for which they are responsible. Yes, stringency may have benefits, but that does not mean it has net benefits.

Even if there is some meaningful point at which NPIs are “optimized”, government does not possess the knowledge required to find that point. It lacks detailed knowledge of both costs and benefits of NPIs. This is a manifestation of the “knowledge problem” articulated by Friedrich Hayek, which hampers all efforts at central planning. In contrast, individual actors know their own tolerance for risk, and they surely have some sense of the risks they create in their normal course of affairs. And again, there is a strong degree of proportionality and voluntary internalization of mutual risks.

While relying on voluntary action is economically inefficient relative to an ideal, full-information and perfectly altruistic solution, it is at least based on information that individuals possess: their own risk profile and risk preferences. In contrast, government does not possess information necessary to impose rules in an optimal way, and those rules are rife with unintended consequences and costs inflicted on individuals.

My next post will present empirical evidence of the weakness of lockdown measures in curbing the coronavirus as well as the high costs of those measures. The coronavirus is a serious infection, but it is not terribly deadly or damaging to the longer-term health of the vast majority of people. This, in and of itself, should be sufficient to demonstrate that the array of non-pharmaceutical interventions imposed in the U.S. and abroad were and are not worthwhile. People are capable of assessing risks for themselves. The externality argument, that NPIs are necessary because people do not adequately assess the risk they pose to others, relies on an authority’s ability to assess that risk, and they invariably go overboard on interventions for which they underestimate costs. COVID is not serious enough to justify a surrender of our constitutional rights, and like every concession to government authority, those rights will be difficult to recover.

A Good Historical Backdrop for the Pandemic

07 Monday Dec 2020

Posted by in Pandemic, Public Health

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In this short post I’m trying to do my part to put our pandemic experience in perspective. Lord knows, I was on the low side in my U.S. case-load “guesstimate” last March, as well as the number of deaths induced by COVID. (A number of others, using highly sophisticated models, erred more severely in the opposite direction.) I also failed to anticipate the strength of the later seasonal waves we’ve experienced — I was excessively optimistic as the first wave ebbed. But now, as the fall wave is showing some signs of topping out, what can we say about the pandemic in historical perspective? I came across an interesting chart that sheds some light on the question.

In terms of all-cause mortality, we’ve clearly seen a bump upward this year. Take a look at the chart below. It shows deaths per million (DPM) of population (from all causes). Try clicking on it if it’s hard to read, or turn your phone sideways. See the little blip on the lower right? That’s our pandemic year through August. The blip made 2020, at least through August, look something like a normal year of the early 2000s.

The visible spike early in 2020 was the spring wave, which was concentrated on the east coast. Of course, the fall wave will yield another spike, probably a bit higher than the first. Nevertheless, against the historical backdrop, this chart shows that the magnitude of our current public health crisis is relatively minor.

If you scan to the left from 2020, you can see that DPM this year would have appeared normal around 2005. Remember how bad it was back in aught-five…. all the death? Yeah, me neither. That’s perspective.

The chart also reflects several mild flu seasons over the past few years. Because the flu, like COVID-19, tends to hit the elderly and infirm the hardest, the “soft” DPM numbers over the past few years support the theory that the population included a fair amount of so-called “dry tinder” for COVID as we entered the year.

One other note on the chart: the DPMs are “age-adjusted”, meaning that age groups are weighted for comparisons across countries with differing age distributions (not what we’re doing here). In this case, the DPM values are weighted based on the population in the year 2000.

It’s amazing how so many have bought into the narrative that the current pandemic is historically bad. Yes, our DPMs are high relative to the last decade, but a significant number of those deaths were caused not by COVID, but by our own overreaction to the virus. That’s something else I failed to anticipate in the spring. It’s something we can put behind us now, however, if only we’re willing to put our experience in perspective. Unfortunately, many public officials, along with their public health advisors, continue to promote the deluded view that the virus can only be stopped by stopping our lives, our educations, our earnings, our health, and our sanity.

The Pernicious COVID PCR Test: Ditch It or Fix It

02 Wednesday Dec 2020

Posted by in Coronavirus, Public Health

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We have a false-positive problem and even the New York Times noticed! The number of active COVID cases has been vastly exaggerated and still is, but there is more than one fix.

COVID PCR tests, which are designed to detect coronavirus RNA from a nasal swab, have a “specificity” of about 97%, and perhaps much less in the field. That means at least 3% of tests on uninfected subjects are falsely positive. But the total number of false positive tests can be as large or larger than the total number of true positives identified. Let’s say 3% of the tested population is truly infected. Then out of every 100 individuals tested, three individuals are actively infected and 97 are not. Yet about 3 of those 97 will test positive anyway! So in this example, for every true infection identified, the test also falsely flags an uninfected individual. The number of active infections is exaggerated by 100%.

But again, it’s suspected to be much worse than that. The specificity of PCR tests depends on the number of DNA replications, or amplification cycles, to which a test sample is subjected. That process is illustrated through three cycles in the graphic above. It’s generally thought that 20 – 30 cycles is sufficient to pick-up DNA from a live virus infection. If a sample is subjected to more than 30 cycles, the likelihood that the test will detect insignificant dead fragments of the virus is increased. More than 35 cycles prompts real concern about the test’s reliability. But in the U.S., PCR tests are regularly subjected to upwards of 35 and even 40-plus cycles of amplification. This means the number of active cases is exaggerated, perhaps by several times. If you don’t believe me, just ask the great Dr. Anthony Fauci:

“It’s very frustrating for the patients as well as for the physicians … somebody comes in, and they repeat their PCR, and it’s like [a] 37 cycle threshold, but you almost never can culture virus from a 37 threshold cycle. So, I think if somebody does come in with 37, 38, even 36, you got to say, you know, it’s just dead nucleotides, period.“

Remember, the purpose of the test is to find active infections, but the window during which most COVID infections are active is fairly narrow, only for 10 – 15 days after the onset of symptoms, and often less; those individuals are infectious to others only up to about 10 days, and most tests lag behind the onset of symptoms. In fact, infected but asymptomatic individuals — a third or more of all those truly infected at any given time — are minimally infectious, if at all. So the window over which the test should be sensitive is fairly narrow, and many active infections are not infectious at all.

PCR tests are subject to a variety of other criticisms. Many of those are discussed in this external peer-review report on an early 2020 publication favorable to the tests. In addition to the many practical shortfalls of the test, the authors of the original paper are cited for conflicts of interest. And the original paper was accepted within 24 hours of submission to the journal Eurosurveillance (what a name!), which should raise eyebrows to anyone familiar with a typical journal review process.

The most obvious implication of all the false positives is that the COVID case numbers are exaggerated. The media and even public health officials have been very slow to catch onto this fact. As a result, their reaction has sown a panic among the public that active case numbers are spiraling out of control. In addition, false positives lead directly to mis-attribution of death: the CDC changed it’s guidelines in early April for attributing death to COVID (and only for COVID, not other causes of death). This, along with the vast increase in testing, means that false positives have led to an exaggeration of COVID as a cause of death. Even worse, false positives absorb scarce medical resources, as patients diagnosed with COVID require a high level of staffing and precaution, and the staff often requires isolation themselves.

Many have heard that Elon Musk tested positive twice in one day, and tested negative twice in the same day! The uncomfortable reality of a faulty test was recently recognized by an Appeals Court in Portugal, and we may see more litigation of this kind. The Court ruled in favor of four German tourists who were quarantined all summer after one of them tested positive. The Court said:

“In view of current scientific evidence, this test shows itself to be unable to determine beyond reasonable doubt that such positivity corresponds, in fact, to the infection of a person by the SARS-CoV-2 virus.” 

I don’t believe testing is a bad thing. The existence of diagnostic tests cannot be a bad thing. In fact, I have advocated for fast, cheap tests, even at the sacrifice of accuracy, so that individuals can test themselves at home repeatedly, if necessary. And fast, cheap tests exist, if only they would be approved by the FDA. Positive tests should always be followed-up immediately by additional testing, whether those are additional PCR tests, other molecular tests, or antigen tests. And as Brown University epidemiologist Andrew Bostom says, you should always ask for the cycle threshold used when you receive a positive result on a PCR test. If it’s above 30 and you feel okay, the test is probably not meaningful.

PCR tests are not ideal because repeat testing is time consuming and expensive, but PCR tests could be much better if the number of replication cycles was reduced to somewhere between 20 and 30. Like most flu and SARS viruses, COVID-19 is very dangerous to the aged and sick, so our resources should be focused on their safety. However, exaggerated case counts are a cause of unnecessary hysteria and cost, especially for a virus that is rather benign to most people.

On COVID, NPIs, and “Human” Data Points

24 Tuesday Nov 2020

Posted by in Lockdowns, Pandemic, Public Health

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The other day a friend told me “your data points always seem to miss the people points.” He imagines a failure on my part to appreciate the human cost of the coronavirus. Evidently, he feels that I treat data on cases, hospitalizations, and deaths as mere accounting issues, all while emphasizing the negative aspects of government interventions.

This fellow reads my posts very selectively, hampered in part by his own mood affiliation. Indeed, he seems to lack an appreciation for the nuance and zeitgeist of my body of blogging on the topic… my oeuvre! This despite his past comments on the very things he claims I haven’t mentioned. His responses usually rely on anecdotes relayed to him by nurses or doctors he knows. Anecdotes can be important, of course. But I know nurses and doctors too, and they are not of the same mind as his nurses and doctors. Anecdotes! We’re talking about the determination of optimal policy here, and you know what Dr. Fauci says about relying on anecdotes!

Incremental Costs and Benefits

My friend must first understand that my views are based on an economic argument, one emphasizing the benefits and costs of particular actions, including human costs. COVID is dangerous, but primarily to the elderly, and no approach to managing the virus is free. Here are two rather disparate choices:

  1. Mandated minimization of economic and social interactions throughout society over some time interval in the hope of reducing the spread of the virus;
  2. Laissez faire for the general population while minimizing dangers to high-risk individuals, subject to free choice for mentally competent, high-risk individuals.

To be clear, #2 entails all voluntary actions taken by individuals to mitigate risks. Therefore, #1 implies a set of incremental binding restrictions on behavior beyond those voluntary actions. However, I also include in #1 the behavioral effects of scare mongering by public officials, who regularly issue pronouncements having no empirical basis.

The first option above entails so-called non-pharmaceutical interventions (NPIs) by government. These are the elements of so-called lockdowns, such as quarantines and other restrictions on mobility, business and consumer activity, social activities, health care activities, school closures, and mask mandates. NPIs carry costs that are increasing in the severity of constraints they impose on society.

And before I proceed, remember this: tallying all fatal COVID cases is really irrelevant to the policy exercise. Nothing we do, or could have done, would save all those lives. We should compare what lives can be saved from COVID via lockdowns, if any, with the cost of those lockdowns in terms of human life and human misery, including economic costs.

Economic Losses

NPIs involve a loss of economic output that can never be recovered… it is gone forever, and a loss is likely to continue for some time to come. That sounds so very anodyne, despite the tremendous magnitude of the loss involved. But let’s stay with it for just a second. The loss of U.S. output in 2020 due to COVID has been estimated at $2.5 trillion. As Don Boudreaux and Tyler Cowen have noted, what we normally spend on safety and precautionary measures (willingness-to-pay), together with the probabilities of losses, implies that we value our lives at less than $4 million on average. Let’s say the COVID death toll reaches 300,000 by year-end (that’s incremental in this case— but it might be a bit high). That equates to a total loss of $1.2 trillion in life-value if we ignore distinctions in life-years lost. Now ask this: if our $2.5 trillion output loss could have saved every one of those 300,000 lives, would it have been worth it? Not even close, and the truth is that the sacrifice will not have saved even a small fraction of those lives. I grant, however, that the economic losses are partly attributable to voluntary decisions, but goaded to a great extent by the alarmist commentary of public health officials.

The full depth of losses is far worse than the dollars and cents comparison above might sound. Output losses are always matched by (and, in value, are exactly the same as) income losses. That involves lost jobs, lost hours, failed businesses, and destroyed careers. Ah, now we’re getting a bit more “human”, aren’t we! It’s nothing short of callous to discount these costs. Unfortunately, the burden falls disproportionately on low-income workers. Our elites can mostly stay home and do their jobs remotely, and earn handsome incomes. The working poor spend their time in line at food banks.

Yes, government checks can help those with a loss of income compete with elites for the available supply of goods, but of course that doesn’t replace the lost supply of goods! Government aid of this kind is a palliative measure; it doesn’t offset the real losses during a suspension of economic activity.

Decimated Public Health

The strain of the losses has been massive in the U.S. and nearly everywhere in the world. People are struggling financially, making do with less on the table, depleting their savings, and seeking forbearance on debts. The emotional strains are no less real. Anxiety is rampant, drug overdoses have increased, calls to suicide hotlines have exploded, and the permanence of the economic losses may add to suicide rates for some time to come. Dr. Robert Redfield of the CDC says more teenagers will commit suicide this year than will die from COVID (also see here). There’s also been a terrifying escalation in domestic abuse during the pandemic, including domestic homicide. The despair caused by economic losses is all too real and should be viewed as a multiplier on the total cost of severe NPIs.

More on human costs: a health care disaster has befallen locked-down populations, including avoidance of care on account of panic fomented by so-called public health experts, the media, and government. Some of the consequences are listed here. But to name just a few, we have huge numbers of delayed cancer diagnoses, which sharply decrease survival time; mass avoidance of emergency room visits, including undiagnosed heart attacks and strokes; and unacceptable delays in cardiac treatments. Moreover, lockdowns worldwide have severely damaged efforts to deal with scourges like HIV, tuberculosis, and malaria.

The CDC reports that excess mortality among 25-44 year-olds this year was up more than 26%, and the vast bulk of these were non-COVID deaths. A Lancet study indicates that a measles outbreak is likely in 2021 due to skipped vaccinations caused by lockdowns. The WHO estimates that 130,000,000 people are starving worldwide due to lockdowns. That is roughly the population of the U.S. east coast. Again, the callousness with which people willfully ignore these repercussions is stunning, selfish and inhumane, or just stupid.

Excess Deaths

Can we quantify all this? Yes we can, as a matter of fact. I’ve offered estimates in the past, and I already mentioned that excess deaths, COVID and non-COVID, are reported on the CDC’s web site. The Ethical Skeptic (TES) does a good job of summarizing these statistics, though the last full set of estimates was from October 31. Here is the graphic from the TES Twitter feed:

Note particularly the huge number of excess deaths attributable to SAAAD (Suicide, Addiction Abandonment, Abuse and Despair): over 50,000! The estimate of life-years lost due to non-COVID excess deaths is almost double that of COVID deaths because of the difference in the age distributions of those deaths.

Here are a few supporting charts on selected categories of excess deaths, though they are a week behind the counts from above. The first is all non-COVID, natural-cause excess deaths (the vertical gap between the two lines), followed by excess deaths from Alzheimer’s and dementia, other respiratory diseases, and malignant neoplasms (cancer):

The clearest visual gap in these charts is the excess Alzheimer’s and dementia deaths. Note the increase corresponding to the start of the pandemic, when these patients were suddenly shut off from loved ones and the company of other patients. I also believe some of these deaths were (and are) due to overwhelmed staff at care homes struck by COVID, but even discounting this category of excess deaths leaves us with a huge number of non-COVD deaths that could have been avoided without lockdowns. This represents a human cost over and above those tied to the economic losses discussed earlier.

Degraded Education and Health

Lockdowns have also been destructive to the education of children. The United Nations has estimated that 24 million children may drop out of school permanently as a result of lockdowns and school closures. This a burden that falls disproportionately on impoverished children. This article in the Journal of the American Medical Association Network notes the destructive impact of primary school closures on educational attainment. Its conclusions should make advocates of school closures reconsider their position, but it won’t:

“… missed instruction during 2020 could be associated with an estimated 5.53 million years of life lost. This loss in life expectancy was likely to be greater than would have been observed if leaving primary schools open had led to an expansion of the first wave of the pandemic.“

Lockdown Inefficacy

Lockdowns just don’t work. There was never any scientific evidence that they did. For one thing, they are difficult to enforce and compliance is not a given. Of course, Sweden offers a prime example that draconian lockdowns are unnecessary, and deaths remain low there. This Lancet study, published in July, found no association between lockdowns and country mortality, though early border closures were associated with lower COVID caseloads. A French research paper concludes that public decisions had no impact on COVID mortality across 188 countries, U.S. states, and Chinese states. A paper by a group of Irish physicians and scientists stated the following:

“Lockdown has not previously been employed as a strategy in pandemic management, in fact it was ruled out in 2019 WHO and Irish pandemic guidelines, and as expected, it has proven a poor mitigator of morbidity and mortality.”

One of the chief arguments in favor of lockdowns is the fear that asymptomatic individuals circulating in the community (and there are many) would spread the virus. However, there is no evidence that they do. In part, that’s because the window during which an individual with the virus is infectious is narrow, but tests may detect tiny fragments of the virus over a much longer span of time. And there is even some evidence that lockdown measures may increase the spread of the virus!

Lockdown decisions are invariably arbitrary in their impact as well. The crackdown on gyms is one noteworthy example, but gyms are safe. Restaurants don’t turn up in many contact traces either, and yet restaurants have been repeatedly implicated as danger zones. And think of the many small retailers shut down by government, while giant competitors like Wal-Mart continue to operate with little restriction. This is manifest corporatism!

Then there is the matter of mask mandates. As readers of this blog know, I think masks probably help reduce transmission from droplets issued by a carrier, that is, at close range. However, this recent Danish study in the Annals of Internal Medicine found that cloth masks are ineffective in protecting the wearer. They do not stop aerosols, which seem to be the primary source of transmission. They might reduce viral loads, at least if worn properly and either cleaned often or replaced. Those are big “ifs”.

To the extent that masks offer any protection, I’m happy to wear them within indoor public accommodations, at least for the time being. To the extent that people are “scared”, I’m happy to observe the courtesy of wearing a mask, but not outside in uncrowded conditions. To the extent that masks are required under private “house rules”, of course I comply. Public mask mandates outside of government buildings are over the line, however. The evidence that those mandates work is too tenuous and our liberties are too precious too allow that kind of coercion. And private facilities should be subject to private rules only.

QED

So my poor friend is quite correct that COVID is especially deadly to certain cohorts and challenging for the health care community. But he must come to grips with a few realities:

  • The virus won’t be defeated with NPIs; they don’t work!
  • NPIs inflict massive harm to human well-being.
  • Lockdowns or NPIs are little or no gain, high-pain propositions.

The rejection of NPI’s, or lockdowns, is based on compelling “human” data points. As Don Boudreaux says:

“The lockdowns and other restrictions on economic and social activities are astronomically costly – in a direct economic sense, in an emotional and spiritual sense, and in a ‘what-the-hell-do-these-arbitrary-diktats-portend-for-our-freedom?’ sense.” 

This doctor has a message for the those denizens of social media with an honest wish to dispense helpful public health advice:

“Americans have admitted that they will meet for Thanksgiving. Scolding and shaming them for wanting this is unlikely to slow the spread of SARS-CoV-2, though it may earn you likes and retweets. Starting with compassion, and thinking of ways they can meet, but as safely as possible, is the task of real public health. Now is the time to save public health from social media.”

And take some Vitamin D!

Predicted November COVID Deaths

08 Sunday Nov 2020

Posted by in Pandemic, Public Health

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Reported COVID deaths do not reflect deaths that actually occurred in the reporting day or week, as I’ve noted several times. Here is a nice chart from @tlowdon on Twitter showing the difference between reported deaths and actual deaths for corresponding weeks. The blue bars are weekly deaths reported by the COVID Tracking Project. The solid orange bars are the CDC’s “provisional” deaths by actual week of death, which is less than complete for recent weeks because of lags in reporting. Still, it’s easy to see that reported deaths have overstated actual deaths each week since late August.

I should note that the orange bars represent deaths that involved COVID-19, though a COVID infection might not have actually killed them. This CDC report, updated on November 4th, shows the importance of co-morbidities, which in many cases are the actual cause of death according to pre-COVID, CDC guidance on death certificates.

Leading Indicators

Researchers have studied several measures in an effort to find leading indicators of COVID deaths. The list includes new cases diagnosed (PCR positivity) and the percentage of emergency room visits presenting symptoms of COVID-like illness (%CLI). These indicators are usually evaluated after shifting them in time by a few weeks in order to observe correlations with COVID deaths a few weeks later. Interestingly, @tlowdon reports that the best single predictor of actual COVID deaths over the course of a few weeks is the sum of the %CLI and the percentage of ER patients presenting symptoms of influenza-like illness (%ILI). Perhaps adding %ILI to %CLI strengthens the correlation because the symptoms of the flu and COVID are often mistaken for one another.

The chart below reproduces the orange bars from above representing deaths at actual dates of death. Also plotted are the %Positivity from COVID tests (shifted forward 2 weeks), %CLI (3 weeks), the %ILI (3 weeks), and the sum of %CLI and %ILI (3 weeks, the solid blue line). My guess is that %ILI contributes to the correlation with deaths mainly because %ILI’s early peak (which occurred in March) led the peak in deaths in April. Otherwise, there is very little variation in %ILI. That might change with the current onset of the flu season, but as I noted in my last post, the flu has been very subdued since last winter.

What About November?

So where does that leave us? The chart above ends with our leading indicator, CLI + ILI, brought forward from the first half of October. What’s happened to CLI + ILI since then? And what does that tell us to expect in November? The chart below is from the CDC’s web site. The red line is %CLI and the yellow line is %ILI. The sum of the two isn’t shown. However, there is no denying the upward trend in CLI, though the slope of CLI + ILI would be more moderate.

As of 10/31, CLI + ILI has increased by almost 40% since it’s low in early October. If the previous relationship holds up, that implies an increase of almost 40% in actual weekly COVID deaths from about 4,000 per week to about 5,500 per week by November 21 (a little less than 800 per day).

FiveThirtyEight has a compilation of 13 different forecast models with projections of deaths by the end of November. The estimate of 5,500 per week by November 21, or perhaps slightly less per week over the full month of November, would put total COVID deaths at the top of the range of the MIT, UCLA, Iowa State, and University of Texas models, but below or near the low end of ranges for eight other models. However, those models are based on reported deaths, so the comparison is not strictly valid. Reported deaths are still likely to exceed actual deaths by the end of November, and the actual death prediction would be squarely in the range of multiple reported death predictions. That reinforces the expectation an upward trend in actual deaths.

Third Wave States

States in the upper Midwest and upper Mountain regions have had the largest increases in cases per capita over the past few weeks. Using state abbreviations, the top ten are ND, SD, WI, IA, MT, NE, WY, UT, IL, and MN, with ID at #11 (according to the CDC’s COVID Data Tracker). One factor that might mediate the increase in cases, and ultimately deaths, is the possibility of early herd immunity: in the earlier COVID waves, the increase in infections abated once seroprevalence (the share of the population with antibodies from exposure) reached a level of 15% to 25%.

Unfortunately, estimates of seroprevalence by state are very imprecise. Thus far, reliable samples have been limited to states and metro areas that had heavy infections in the first and second waves. One rule of thumb, however, is that seroprevalence is probably less than 10x the cumulative share of a population having tested positive. To be very conservative, let’s assume a seroprevalence of four times cumulative cases. On that basis, half the states in the “top ten” listed above would already have seroprevalence above 15%. Those states are ND, SD, WI, IA, and NE. The others are mostly in a range of 12% to 15%, with MI coming in the lowest at about 9%.

This gives some cause for optimism that the wave in these states and others will abate fairly soon, but there are a number of uncertainties: first, the estimates of seroprevalence above, while conservative, are very imprecise, as noted above; second, the point at which herd immunity might cause the increase in new cases to begin declining is real guesswork (though we might have confirmation in a few states before long); third, we are now well into the fall season, with lower temperatures, lower humidity, less direct sunlight, and diminishing vitamin D levels. We do not have experience with COVID at this time of year, so we don’t know whether the patterns observed earlier in the year will be repeated. If so, new cases might begin to abate in some areas in November, but that probably wouldn’t be reflected in deaths until sometime in December. And if the flu comes back with a corresponding increase in CLI + ILI, then we’d expect further increases in actual deaths attributed to COVID. That is only a possibility given the weakness in flu numbers in 2020, however.

Closing Thoughts

I was excessively optimistic about the course of the pandemic in the U.S. in the spring. While this post has been moderately pessimistic, I believe there are reasons to expect fewer deaths than previous relationships would predict. We are far better at treating COVID now, and the vulnerable are taking precautions that have reduced their incidence of infections relative to younger and healthier cohorts. So if anything, I think the forecasts above will err on the high side.