A fiftyish guy just ahead of me is wearing a mask, walking from the beach toward a public pavilion where there are restrooms. He is barefoot…. and he enters the men’s room and steps right up to the row of urinals. He leaves the restroom without washing his hands.
Perhaps he’s not quiteDarwin Award material, but I ask: do you think this guy’s precautions against potential pathogens and disease vectors were well balanced? It’s not terribly uncommon to see “moisture” or even shallow puddles around public urinals. Don’t go barefoot! Wear flip-flops to the john, at the very least. And wash your hands when you’re done!
Amazingly, the only message related to health and hygiene that our friend has absorbed is to wear a useless mask. And he wears it at the beach! I’m sure he got around to adjusting his mask with unwashed hands at some point. I’ll cut him some slack for wearing a mask inside the restroom, but as my last post noted, that precaution is almost surely wasted effort.
It’s been clear since the beginning of the pandemic that your chance of getting infected with COVID outside is close to zero. (Also see here). Yet I still see a few masked people on the beach, in the park, on balconies, and walking in the neighborhood. Given the negligible risk of contracting COVID outdoors, the marginal benefit of masking outdoors is infinitesimal. Likewise, the benefit of a mask to the sole occupant of a vehicle is about zilch. Okay, some individuals might forget to remove their masks after leaving a “high-risk” environment. Sure, maybe, but cloth masks really don’t stop the dispersion of fine aerosols anywhere, indoors or outdoors. Of course, the immune-compromised have a reasonable excuse to apply the precautionary principle, but generally not outside with good air quality.
The following link provides a list of mask studies, and meta-studies. Several describe randomized control trials (RCTs). They vary in context, but all of them reject the hypothesis that masks are protective. Positive evidence on mask efficacy is lacking in health care settings, in community settings, and in school settings, and the evidence shows that masks create “pronounced difficulties” for young children and “emotional interference” for school children of all ages. Here’s another article containing links to more studies demonstrating the inefficacy of masks. Also see here. And this article is not only an excellent summary of the research, but it also highlights the hypocrisy of the “follow the science” public health establishment with respect to RCTs. Compliance is not even at issue in many of these studies, though if you think masks matter, it is always an issue in practice. Even studies claiming that cloth masks of the type normally worn by the public are “effective” usually concede that a large percentage of fine aerosols get through the masks… containing millions of tiny particles. In indoor environments with poor ventilation, those aerosols remain suspended in the air for periods long enough to be inhaled by others. That, in fact, is why masks are ineffective at preventing transmission.
Another dubious claim is that masks are responsible for virtually eliminating cases of influenza in 2020 and 2021. Again, to be charitable, masks are of very limited effectiveness in stopping viral transmission. Moreover, compliance has been weak at best, and areas without mask mandates have experienced the same plunge in flu cases as areas with mandates. A far more compelling explanation is that viral interference caused the steep reduction in flu incidence. The chance of being infected with more than one virus at a time is almost nil. Simply put, COVID outcompeted the flu.
Again, I grant that there are studies (though only a single randomized control trial out of India of which I’m aware) that have demonstrated significant protective effects. Even then, however, the mixed nature of this body of research does not support intrusive masking requirements.
Nevertheless, masks are still mandated in some jurisdictions. Those mandates usually don’t apply outdoors, however, and not in your own damn car! Mask mandates contribute to the general climate of fear surrounding COVID, which is wholly unjustified for most children and healthy working-age people. Public health messaging should focus on high-risk individuals: the elderly, the obese, and those having so-called comorbidities and compromised immune systems. Those groups have obvious reasons to be concerned about the virus. They have excuses to be germaphobic! Still, they are at little risk outdoors, the value of masks is doubtful, and breathing deep of fresh air is good for you in any case!
The incidence of COVID has declined substantially in many areas since early September, but the virus is now almost certainly endemic and is likely to return in seasonal waves. However, the Delta wave was far less deadly than earlier variants, a favorable trend many believe will continue. These charts from the UKposted by Michael Levitt demonstrate the improvement vividly. Perhaps the mask craze will fade away as the evidence accumulates.
The pandemic has been a moment of redemption for germaphobes, but no reasonable assessment of risk mitigation relative to the cost, inconvenience, discomfort, and psychological debasement of face jackets can prove their worth outdoors. Their value indoors is nearly as questionable. Yet there remains a stubborn reluctance by public health authorities to lift mask mandates. There are far too many individuals masking outdoors, and to be nice, perhaps it’s mere ignorance. But there are still a few would-be tyrants on Twitter presuming to shame others into joining this pathetic bit of theatre. I believe Anne Wheeler nailed it with this recent tweet:
“This is one of the first things you learn in OCD therapy – you don’t get to make people participate in your compulsions in order to lesson your own anxiety. It’s bizarre that it’s been turned into a virtue.”
There’s also no question that masks are still in vogue as a virtue signal in some circles, but a mask outdoors, especially, is increasingly viewed as a stupid-signal, and for good reason. I’ll continue to marvel at the irrationality of these masked alarmists, who just don’t understand how foolish they look. Give yourself permission to get some fresh air!
If this post has an overarching theme, it might be “just relax”! That goes especially for those inclined to prescribe behavioral rules for others. People can assess risks for themselves, though it helps when empirical information is presented without bias. With that brief diatribe, here are a few follow-ups on COVID vaccines, the Delta wave, and the ongoing “mask charade”.
Israeli Vax Protection
Here is Jeffrey Morris’ very good exposition as to why the Israeli reports of COVID vaccine inefficacy are false. First, he shows the kind of raw data we’ve been hearing about for weeks: almost 60% of the country’s severe cases are in vaccinated individuals. This is the origin of the claim that the vaccines don’t work.
Next, Morris notes that 80% of the Israeli population 12 years and older are vaccinated (predominantly if not exclusively with the Pfizer vaccine). This causes a distortion that can be controlled by normalizing the case counts relative to the total populations of the vaccinated and unvaccinated subgroups. Doing so shows that the unvaccinated are 3.1 times more likely to have contracted a severe case than the vaccinated. Said a different way, this shows that the vaccines are 67.5% effective in preventing severe disease. But that’s not the full story!
Morris goes on to show case rates in different age strata. For those older than 50 (over 90% of whom are vaccinated and who have more co-morbidities), there are 23.6 times more severe cases among the unvaccinated than the vaccinated. That yields an efficacy rate of 85.2%. Vaccine efficacy is even better in the younger age group: 91.8%.
These statistics pertain to the Delta variant. However, it’s true they are lower than the 95% efficacy rate achieved in the Pfizer trials. Is Pfizer’s efficacy beginning to fade? That’s possible, but this is just one set of results and declining efficacy has not been proven. Israel’s vaccination program got off to a fast start, so the vaccinated population has had more time for efficacy to decay than in most countries. And as I discussed in an earlier post, there are reasons to think that the vaccines are still highly protective after a minimum of seven months.
IIn the meantime, the Biden Administration has recommended that booster shots be delivered eight months after original vaccinations. There is empirical evidence that boosters of similar mRNA vaccine (Pfizer and Moderna) might not be a sound approach, both due to side effects and because additional doses might reduce the “breadth” of the antibody response. We’ll soon know whether the first two jabs are effective after eight months, and my bet is that will be the case.
Is Delta Cresting?
Meanwhile, the course of this summer’s Delta wave appears to be turning a corner. The surge in cases has a seasonal component, mimicking the summer 2020 wave as well as the typical Hope-Simpson pattern, in which large viral waves peak in mid-winter but more muted waves occur in low- to mid-latitudes during the summer months.
Therefore, we might expect to see a late-summer decline in new cases. There are now 21 states with COVID estimated reproduction rates less than one (this might change by the time you see the charts at the link). In other words, each new infected person transmits to an average of less than one other person, which shows that case growth may be near or beyond a peak. Another 16 states have reproduction rates approaching or very close to one. This is promising.
“Mask supporters often claim that we have no choice but to rely on observational studies instead of RCTs [randomized control trials], because RCTs cannot tell us whether masks work or not. But what they really mean is that they don’t like what the RCTs show.”
Oh, how well I remember the “follow-the-science” crowd insisting last year that only RCTs could be trusted when it came to evaluating certain COVID treatments. In any case, the observational studies on masks are quite mixed and by no means offer unequivocal support for masking.
The vaccines are still effective. Data purporting to show otherwise fails to account for the most obvious of confounding influences: vaccination rates and age effects. In fact, the Biden Administration has made a rather arbitrary decision about the durability of vaccine effects by recommending booster shots after eight months. The highly transmissible Delta variant has struck quickly but the wave now shows signs of cresting, though that is no guarantee for the fall and winter season. However, Delta cases have been much less severe on average than earlier variants. Masks did nothing to protect us from those waves, and they won’t protect us now. I, for one, won’t wear one if I can avoid it.
The barrage of precautionary COVID missives continues, and with a familiar “follow-the-money” twist. The CEOs of both Pfizer and Moderna say that booster shots are likely to be needed a year after initial administration of their COVID vaccines, and almost certainly every year thereafter. Of course, this message is for those who felt compelled to be vaccinated in the first place, whether out of concern for their own health, high-minded community spirit, fear of social ostracism, or fear of possible vaccine passport requirements. It’s probably also intended for those who acquired immunity through infection.
There are reasons to believe, however, that such a booster is unnecessary. This case was made a few days ago in a series of tweets by Dr. Monica Ghandi, an infectious disease expert and Professor of Medicine at UCSF. Ghandi says immunity from an infection or a vaccine can be expected to last much longer than a year, despite the diminished presence of antibodies. That’s because the immune system relies on other mechanisms to signal and produce new antibodies against specific pathogens when called upon.
So-called B cells actually produce antibodies. Another cell-type known as T cells act to signal or instruct B cells to do so, but so-called “killer” T cells destroy cells in the body that have already been infected. Dr. Ghandi’s point is that both B and T cells tend to have very long memories and are capable of conferring immunity for many years.
While our experience with COVID-19 is short, long-lasting immunity has been proven against measles for up to 34 years, and for other SARS-type viruses for at least 17 years. Dr. Ghandi links to research showing that survivors of the 1918 flu pandemic were found to have active B cells against the virus 90 years later! The COVID vaccines cause the body to produce both B and T cells, and the T cells are protective against COVID variants.
A last point made by Dr. Ghandi is intended to dispel doubts some might harbor due to the relatively ineffectual nature of annual flu vaccines. The flu mutates much more aggressively than COVID, so the design of each year’s flu vaccine involves a limited and uncertain choice among recent strains. COVID mutates, but in a more stable way, so that vaccines and adaptive immunity tend to retain their effectiveness.
While I’m sure the pharmaceutical companies believe in the benefits of their vaccines, there are undoubtedly other motives behind the push for boosters. There is money to be made, and much of that money will be paid by governments eager to jump on the precautionary bandwagon, and who are likely to be very insensitive to price. In fact, the vaccine producers might well have encouraged those pushing vaccine passports to include annual booster requirements. This would be another unwelcome imposition. The very discussion of boosters gives government officials more running room for other draconian but ultimately ineffective mandates on behavior. And the booster recommendation gives additional cover to public health “experts” who refuse to acknowledge real tradeoffs between the stringency of non-pharmaceutical interventions, economic well being, and other dimensions of public health.
Super-spreading events are gatherings at which one or more attendees are already harboring an infection and manage to transmit it to a number of others. These people, in turn, spread it to their close contacts, possibly at the same event. Super-spreading has dominated the transmission of COVID-19. These transmissions have almost always taken place indoors in spaces with limited ventilation, and they have usually involved close or prolonged contact. In addition, super-spreading originates with a small subset of infected individuals. That’s essentially what the chart above shows. It ranks individual subjects by their exhaled quantity of aerosolized particles per liter of air.
For more than a year, we’ve also known that obesity and age are associated with more severe COVID infections. Now, it’s startling to learn that obese and/or older, infected individuals are more prone to transmitting virus: this study found that a high body mass index (BMI) is associated with significantly greater quantities of exhaled aerosol, and that age has a similarly strong association. So called BMI-years, or age x BMI, has an extremely powerful association with the exhalation of aerosol-borne particles. The authors, David A. Edwards, et al, believe this is a consequence of the properties of mucus produced by different individuals in response to infections and how their lungs and airways handle it. The authors say:
“Our findings indicate that the capacity of airway lining mucus to resist breakup on breathing varies significantly between individuals, with a trend to increasing with the advance of COVID-19 infection and body mass index multiplied by age (i.e., BMI-years). Understanding the source and variance of respiratory droplet generation, and controlling it via the stabilization of airway lining mucus surfaces, may lead to effective approaches to reducing COVID-19 infection and transmission. … ”
“Surfactant and mucin compositional and structural changes, driven, in part, by physiological alterations of the human condition—including diet (10), aging (11), and COVID-19 infection itself (12)—may therefore be anticipated to alter droplet generation and droplet size (7) during acts of breathing.”
So there is substantial variation in the exhalation of aerosol-borne particles across individuals. In the study, less than 20% of healthy subjects produced more than 156 particles per liter of air, accounting for 80% of the exhaled particles. This defined their so-called “super-spreader” cohort. The association of BMI-years and exhaled particles was less pronounced but still positive within the “low-spreader” cohort.
Edwards, et al speculate that these fine droplets might help explain the greater severity of COVID infections among the elderly and obese. Not only does the breakup of mucus into tiny droplets cause these individuals to exhale aerosols more profusely, it probably also leads to deep penetration into their lung tissue.
This knowledge might be broadly applicable to infectious diseases, and SARS viruses in particular. The elderly know they are vulnerable. It’s not clear that the obese have viewed themselves as vulnerable, but they should, even in the age of “body positivity“. And not only are they vulnerable: they appear to pose an elevated hazard to others. I came across a couple of sardonic comments that got right to the apparent elephant in the room: “Instead of a mask mandate, how about a push-up mandate?”; and “Instead of a vaccine passport, how about a BMI passport?”
The debate about how to care for the most vulnerable is ongoing, but the mere mention of regularities like those identified by the study might lead to proposals for coercive policies. But first, a few practical points to bear in mind: 1) while the study identifies a major risk factor for transmission, it must be replicated by others, and there must be research into the underlying reasons for the phenomenon; 2) while the obese and seniors may be more likely to super-spread, not all of them are super-spreaders; and 3) as a matter of policy, how would “super-spreaders” be defined? What would be the cutoff BMIs at various ages? No matter what was decided, restrictive policies predicated on mere statistical associations would involve gross injustices to a large number of individuals.
With the degree of acquired immunity already in the population and fairly widespread voluntary vaccination (since alarmists have scared the bejeezus out of everyone), the whole issue might seem moot. It’s not, however, because COVID-19 is likely to become endemic, the immunities of some individuals might erode more quickly than expected, new and more dangerous variants might arise, and new SARS viruses are likely to emerge with time.
In a pandemic, however, and even without knowing who is infected, it is ethically barbaric to probabilistically isolate classes of individuals, whether based on age, BMI, or anything other than contagious status. The social cost is simply unacceptable. Instead, public health authorities should provide information to those at high risk, facilitate vaccination for those who desire it, and promote rapid, at-home tests. This is essentially a deregulatory agenda relative to the mindless lockdown approaches favored by so many public health experts.
Everyone must balance their own personal risks and rewards. Based on the study of exhaled particles discussed above, some might shun the obese and seniors until the threat has passed. Some of the obese and elderly might shun each other. That might be another regrettable dimension of the costs of a pandemic. On the other hand, perhaps more of us will respond to the unquestionably positive incentives for weight loss, of which we’re almost all aware.
The CDC’s new study on dining out and mask mandates is a sham. On its face, the effects reported are small. And while it’s true most of the reported effects are statistically significant, the CDC acknowledges a number of factors that might well have confounded the results. This study should remind us of the infinite number of spurious and “significant” correlations in the world. Here, the timing of the mandates (or their removal) relative to purported effects and seasonal waves is highly suspicious, and as always, attributing causality on the basis of correlation is problematic.
On one hand, the CDC’s results are contrary to plentiful evidence that mandates are ineffective; on the other hand, the results are contrary to earlier CDC “guidance” that masks and limits on indoor dining are “highly effective”. Nevertheless, the latest report has massive propaganda value to the CDC. The media lapped up the story and provided cover for Democrats eager to pass the COVID (C19) relief package. Likewise, the Biden Administration is apparently committed to the narrative of an ongoing crisis as cover for continued attempts to shame political opponents in states that have elected to “reopen” or remain open.
Right off the bat, the study’s authors assert that the primary mode of transmission of C19 is from respiratory droplets. This is false. We know that aerosols are the main culprit in transmission, against which cloth masks are largely ineffective.
Be that as it may, let’s first consider the findings on dining. There was no statistically significant effect on the growth rate of cases or deaths up to 40 days after restrictions were lifted, according to the report. In fact, case growth declined slightly. There was, however, a small but statistically significant increase after 40 days. The fact that deaths seemed to “respond” faster and with greater magnitude than cases makes no sense and suggests that the results might be spurious.
The CDC offers possible explanations the long delay in the purported impact, such as the time required by restaurants to resume operations and early caution on the part of diners. These are speculative, of course. More pertinent is the fact that the data did not distinguish between indoor and outdoor dining, nor did it account for other differences in regulation such as rules on physical distancing, intra-county variation in local government mandates, and compliance levels.
Finally, the measurement of effects covered 100 days after the policy change, but this window spans different stages of the pandemic. There were three waves of infections during 2020, which correspond to the classic Hope-Simpson pattern of virus seasonality. One was near year-end, but as each of the first two waves tapered (April-May, August-September), it should be no surprise that many restrictions were lifted. Within two months, however, new waves had begun. Karl Dierenbach notes that most of the reopenings occurred in May. Here’s how he explains the pattern:
“The map on the left shows counties where there was no on-premises dining (pink) in restaurants as of the beginning of May (4/30). … The map on the right shows that by the end of May, almost the entire country moved to allow some on-premises dining (green).”
“In the 100 days after May 1, cases nationwide fell slightly, then began to rise, and then plateaued.”
“And what did the CDC find happened after restaurants were allowed (changing mostly in May) to have on-premises dining? … Surprise! The CDC found that cases fell slightly, then began to rise, and then plateaued.”
The summer “mini-wave” is typical of mid- and tropical-latitude seasonality. Thus, the CDC’s findings with respect to dining restrictions are likely an artifact of the strong seasonality of the virus, rather than having anything to do with the lifting of restrictions between waves.
What about the imposition of mask mandates? The CDC’s findings show a much faster response in this case, with statistically significant changes in growth during the first 20 days. Another indicator of spurious correlation is that the growth response of deaths did not lag that of cases, but in fact deaths have reliably lagged cases by over 18 days during the pandemic. Again, the CDC’s caveats apply equally to its findings on masks. A large share of individuals adopted mask use voluntarily before mandates were imposed, so it’s not even clear that the mandates contributed much to the practice.
It’s a stretch to believe that mask mandates would have had an immediate, incremental effect on the growth of cases and deaths, given probable lags in compliance, exposure, and onset of symptoms. Moreover, a number of mask mandates in 2020 were imposed near the very peak of the seasonal waves. Little wonder that the growth rates of cases and deaths declined shortly thereafter.
We’ve known for a long time that masks do little to stop the spread of viral particles. They become airborne as aerosols which easily penetrate the kind of cloth masks worn by most members of the public, to say nothing of making contact with their eyes. The table below contains citations to research over the past 10 years uniformly rejecting the hypothesis of a significant protective effect against influenza from masks. There is no reason to believe that they would be more effective in preventing C19 infections.
The CDC’s report on dining restrictions and mask mandates is a weak analysis. They wish to emphasize their faith in non-pharmaceutical interventions (NPIs) to minimize risks. They do so at a time when the vaccinated share of the most vulnerable population, the elderly, has climbed above 50% and is increasing steadily. Thus, risks are falling dramatically, so it’s past time to weigh the costs and benefits of NPIs more realistically. The timing of the report also seemed suspicious, coming as it did in the heat of the battle over the $1.9 trillion COVID relief bill, which subsequently passed.
It’s also a good time to note that zero risk, including “Zero COVID”, is not a realistic or worthwhile goal under any reasonable comparison of costs and benefits. Furthermore, NPIs have proven weak generally (also see here); claims to the contrary should always make us wary.
The CDC choked on a new analysis estimating COVID-19’s impact on U.S. life expectancy as of year-end 2020: they reported a decline of a full year, which is ridiculous on its face! As explained by Peter B. Bach in STAT News, the agency assumed that excess deaths attributed to COVID in 2020 would continue as a permanentaddition to deaths going forward. Please forgive my skepticism, but isn’t this too basic to qualify as an analytical error by an agency that subjects its reports to thorough vetting? Or might this have been a deliberate manipulation intended to convince the public that COVID will be an ongoing public health crisis. Of course the media has picked it up; even Zero Hedgereported it uncritically!
Bach does a quick calculation based on 400,000 excess deaths attributed to COVID in 2020 and 12 life-years lost by the average victim. I believe the first assumption is on the high side, and I say “attributed to COVID” as a reminder that the CDC’s guidance for completing death certificates was altered in the spring of 2020 specifically for COVID and not other causes of death. Furthermore, if our objective is to assess the impact of the virus itself, under no circumstances should excess deaths induced by misguided lockdown policies enter the calculation (though Bach entertains the possibility). Bach arrives at a reduction in average life of 5.3 days! Of course, that’s not intended to be a projection, but it is a reasonable estimate of COVID’s impact on average lives in 2020.
The CDC’s projection essentially freezes death rates at each age at their 2020 values. We will certainly see more COVID deaths in 2021, and the virus is likely to become endemic. Even with higher levels of acquired immunity and widespread vaccinations, there will almost certainly be some ongoing deaths attributable to COVID, but they are likely to be at levels that will blend into a resumption of the long decline in mortality rates, especially if COVID continues to displace the flu in its “ecological niche”. I include the chart at the top to emphasize the long-term improvement in mortality (though the chart shows only a partial year for 2020, and there has been some flattening or slight backsliding over the past five years or so). As Bach says:
“Researchers have regularly demonstrated that life expectancy projections are overly sensitive to evanescent events like pandemics and wars, resulting in considerably overestimated declines. … And yet the CDC published a result that, if anything, would convey to the public an exaggerated toll that Covid-19 took on longevity in 2020. That’s a problem.”
There were excess deaths from other causes in 2020, which Bach acknowledges. Perhaps 100,000 or more could be attributed to lockdowns and their consequences like economically-induced stress, depression, suicide, overdoses, and medical care deferred or never sought. The Zero Hedge article mentioned above discusses findings that lockdowns and their consequences, such as unemployment spells and lost education, will have ongoing negative effects on health and mortality for many years. The net effect on life expectancy might be as large as 11 to 12 days. Again, however, I draw a distinction between deaths caused by the disease and deaths caused by policy mistakes.
The CDC’s estimate should not be taken seriously when, as Kyle Smith says, there is every indication that the battle against COVID is coming to a successful conclusion. Public health experts have not acquitted themselves well during the pandemic, and the CDC’s life expectancy number only reinforces that impression. Here is Smith:
“We have learned a lot about how the virus works, and how it doesn’t: Outdoor transmission, for the most part, hardly ever happens. Kids are at very low risk, especially younger children. Baseball games, barbecues, and summer camps should be fine. Some pre-COVID activities now carry a different risk profile — notably anything that packs crowds together indoors, so Broadway theater, rock concerts, and the like will be just about the last category of activity to return to normal.”
But return to normal we should, and yet the CDC seems determined to poop on the victory party!
It’s been said that many of the so-called “heroes” of the COVID pandemic who’ve been celebrated by the media are actually villains, and perhaps Governor Andrew Cuomo of New York should top the list. He saw to it that retirement homes were seeded with infected patients by ordering them returned their care homes rather than admitted to hospitals. Deaths in these facilities mounted, and they mounted faster than Cuomo’s administration was willing to admit. But the media and even Democrat state legislators have begun to take note, which is practically a miracle!
It seems equally true that some vilified by the media for their COVID response are actually heroes. Governor Ron DeSantis of Florida might deserve top honors here. Having spent the last month in Florida, I can attest that the business and social environment here is quite open compared to my home state (despite the presence of a few freaked out northerners who can’t quite fathom how stupid they look wearing masks on the beach). Florida’s infections, hospitalizations, and deaths have been lower than in California, New York, and many other states where lockdown measures have been stringent. (The first chart below is just a little busy…)
This approach to saving lives is obvious, yet critics at outlets like NBC News insist that DeSantis must be pandering to the senior population in Florida. Well, one wouldn’t want to be responsive to voters who happen to face high mortality risks, right? Others such as horror writer Stephen Kinghave jumped onboard to offer their bumbling public health expertise as well.
There were many experts and the usual collection of numbskulls on social media who were wrong about Florida. DeSantis handled the pandemic as it should have been handled elsewhere. But the propaganda to the contrary goes unabated. For example, this article is pathetic. Can these people be serious? Or are they really that stupid? This goes for the Biden Administration as well, which had entertained the notion of imposing federal travel restrictions on Florida!
The political attacks on Florida and its governor reveal the extent to which opponents wish to ignore the evidence in plain sight. The data on COVID outcomes put the lie to the narrative of a public health emergency requiring massive restrictions on personal liberty. We know those policies are powerless to control the course of the contagion. The pandemic, however, was the key to convincing the public to accept a more authoritarian role for government. It’s a blessing that not everyone bought in, and that there are places like Florida where you can still go about your business in approximate normalcy.
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.
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.)
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.
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.
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.
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.
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 ratesmount 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-morbiditiesand 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 vaccinationsmay 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.
In advanced civilizations the period loosely called Alexandrian is usually associated with flexible morals, perfunctory religion, populist standards and cosmopolitan tastes, feminism, exotic cults, and the rapid turnover of high and low fads---in short, a falling away (which is all that decadence means) from the strictness of traditional rules, embodied in character and inforced from within. -- Jacques Barzun