T-Cell immune response (not to be confused with invulnerability) is hardly a new idea in public health. Yet what is striking is how long it took you to hear about it — from the mainstream at least — in the context of coronavirus.
If you go back to February, March, even April or dare I say May, you will not find too many mainstream public health commentators suggesting “there is some possibility of T-cell immunity playing a major role here. That could significantly ease the future casualties and economic burden of Covid-19.” David Wallace-Wells dates the beginning of the discussion to late May, and the “dark matter” hypothesis of Friston, though I believe earlier precursors will be found.
You didn’t even hear much of: “We really are not sure T-cell immunity is a factor. But it could be a factor with probability [fill in the blank], and it is worth keeping that in mind.”
Think about the underlying equilibrium that could lead to such a strange result.
if you do public health, your status incentives are to deliver warnings, not potential good news.
Your status incentives are always to hedge your bets, and to be reluctant to introduce new hypotheses.
Your status incentives are to steer talk away from the virus “simply continuing to rip,” even if you are quite opposed to that outcome. Other than hitting it with an immediate scold, you are not supposed to let that option climb on to the discussion table for too long.
Your status incentives are to discourage individuals from thinking that they might be have some pre-existing level of protection. That might lead them to behave more irresponsibly, and then you in turn would look less responsible.
Since public health commentators are so concerned with “doing good by us,” they fail to see that their altruistic (and status) motives in these matters mean they do not end up telling us the truth. Not the entire truth, and not upfront in a very prompt matter.
To be fair, I don’t recall seeing mainstream commentators making false claims about T-cell immunity, rather their filters end up being very selective ones and they bring it up only slowly. And because they smush together in their minds the actually quite distinct concepts of “doing good,” “status,” and “informing the public,” they genuinely have no idea that they are not entirely on the side of truth.
And they genuinely have no idea why so many smart people look to “the cranks” for advice and counsel.
And, to be clear, the commentary of “the cranks” in this area has plenty of problems of its own, even though in some ways they have turned out to be a more informative (as distinct from accurate) source on T-cell immunity.
Finally, to recap, we still are not sure how much overall social protection T-cell immunity will bring. Furthermore, we are pretty sure that not many places have a chance of current herd immunity from “a mix of previous Covid exposure plus pre-existing T-cell immunity.”
So I am not trying to induce you to overrate the T-cell immunity idea. I am trying to illuminate the biases of the filters at work in your everyday consumption of Covid-19 information. Those biases too, the mainstream commentators are not so keen to tell you about.
Following on my earlier analysis, ideally you want that super-spreaders are a fixed group who do not rotate much. That makes semi-effective herd immunity easier to reach in a region. So, in Bayesian terms, for a given super-spreader event, exactly which kind of story should you be rooting for?
Let’s say (hypothetically) that being a super-spreader has to do with your innate propensity to be infectious, as might be determined say by your genetic make-up. Then it is easier for the super-spreaders to acquire at least partial immunity, without a new group of super-spreaders rising up to take their place.
Alternatively, let’s say that being a super-spreader has to do with being in some relatively well-defined occupations and situations, such as singing in a church choir. That is a less optimistic prognosis, but still one of the better scenarios, as in principle it is possible to shut down many of those opportunities and thus block out the potential super-spreaders from doing their thing.
You should feel less good when you read of super-spreading events in very general public spaces, such as elevators, movie theaters, and office buildings. Those events, in Bayesian fashion, boost the probability that super-spreading is a generic ability, attached to a wide variety of fairly general situations. That raises the chance that, even after some super-spreaders acquire partial immunity, other super-spreaders will step in and play similar roles. Quite possibly all sorts of individuals — and not just those genetically endowed with super-powerful sneezes — are capable of super-spreading in small, enclosed public spaces.
You really do want those super-spreaders to be inelastic in supply.
That is the topic of my latest Bloomberg column. The evidence in favor of at least partial herd immunity continues to pile up, but still don’t get too cheery. One worry is that herd immunity might prove only temporary:
First, many herd immunity hypotheses invoke the idea of “superspreaders” — that a relatively small number of people account for a disproportionate amount of the contagion. Perhaps it is the bartenders, church choir singers and bus drivers who spread the virus to so many others early on in the pandemic. Now that those groups have been exposed to a high degree and have acquired immunity, it might be much harder to distribute the virus.
That logic makes some sense except for one issue: namely, that the identities of potential superspreaders can change over time. For instance, perhaps choir singers were superspreaders earlier in the winter, but with most choral singing shut down, maybe TSA security guards are the new superspreaders. After all, air travel has been rising steadily. Or the onset of winter and colder weather might make waiters a new set of superspreaders, as more people dine inside.
In other words, herd immunity might be a temporary state of affairs. The very economic and social changes brought by the virus may induce a rotation of potential superspreaders, thereby undoing some of the acquired protection.
In other words, the fight never quite ends. Here is another and possibly larger worry:
Another problem is global in nature and could prove very severe indeed. One possible motivation for the herd immunity hypothesis is that a significant chunk of the population already had been exposed to related coronaviruses, thereby giving it partial immunity to Covid-19. In essence, that “reservoir” of protected individuals has helped to slow or stop the spread of the virus sooner than might have been expected.
There is a catch, however. If true, that hypothesis means that the virus spreads all the more rapidly among groups with little or no protection. (Technically, if R = 2.5, but say 50% of the core population has protection, there is an R of something like 5 for the unprotected population, to get the aggregate R to 2.5.) So if some parts of the world enjoy less protection from cross-immunities, Covid-19 is likely to ravage them all the more — and very rapidly at that.
Again, this is all in the realm of the hypothetical. But that scenario might help explain the severe Covid-19 toll in much of Latin America, and possibly in India and South Africa. Herd immunity, as a general concept, could mean a more dangerous virus for some areas and population subgroups.
There are further arguments at the link.
What determines the success of a COVID-19 Test & Trace policy? We use an SEIR agent-based model on a graph, with realistic epidemiological parameters. Simulating variations in certain parameters of Testing & Tracing, we find that important determinants of successful containment are: (i) the time from symptom onset until a patient is self-isolated and tested, and (ii) the share of contacts of a positive patient who are successfully traced. Comparatively less important is (iii) the time of test analysis and contact tracing. When the share of contacts successfully traced is higher, the Test & Trace Time rises somewhat in importance. These results are robust to a wide range of values for how infectious presymptomatic patients are, to the amount of asymptomatic patients, to the network degree distribution and to base epidemic growth rate. We also provide mathematical arguments for why these simulation results hold in more general settings. Since real world Test & Trace systems and policies could affect all three parameters, Symptom Onset to Test Time should be considered, alongside test turnaround time and contact tracing coverage, as a key determinant of Test & Trace success.
That is from a new paper by Ofir Reich.
But let’s start with the UK:
The number of people in hospital with Covid-19 has fallen 96% since the peak of the pandemic, official data reveals.
Hospital staff are now treating just 700 coronavirus patients a day in England, compared to about 17,000 a day during the middle of April, according to NHS England.
Last week, some hospitals did not have a single coronavirus patient on their wards, with one top doctor suggesting that Britain is “almost reaching herd immunity”.
In a further sign of good news, the virus death toll in hospitals has also plummeted. On April 10, the day the highest number of deaths was announced to the nation, NHS England said 866 people had died. On Thursday last week, there were just five hospital deaths across the entire country. It represents a fall of more than 99% from the height of fatalities during the crisis.
Note that the pubs and many other venues have been open for over a month, and social distancing protections in the UK remain relatively weak, nor has individual or political behavior in the country been especially responsible.
Here is the Times of London piece (gated).
We exploit changes in U.S. visa policies for nurses to measure brain drain versus gain. Combining data on all migrant departures and postsecondary institutions in the Philippines, we show that nursing enrollment and graduation increased substantially in response to greater U.S. demand for nurses. The supply of nursing programs expanded to accommodate this increase. Nurse quality, measured by licensure exam pass rates, declined. Despite this, for each nurse migrant, 10 additional nurses were licensed. New nurses switched from other degree types, but graduated at higher rates than they would have otherwise, thus increasing the human capital stock in the Philippines.
I am not convinced by the humidity hypothesis, as I don’t see it having much macro explanatory power globally, but I find the questions very important. On New York City, I tend to blame all those cramped indoor spaces combined with bad ventilation systems, but that too is an unconfirmed hypothesis. Anyway, here are the words of Daniel Hess:
That is the topic of my latest Bloomberg column, here is one excerpt:
Then there is the Swedish experiment, which has been the subject of a raging controversy. Here again, most moralizing is premature, even though the Swedes did make some clear mistakes, such as not protecting their nursing homes well enough. Sweden had a high level of early deaths, but both cases and deaths have since fallen to a very low level, even though Sweden never locked down. In the meantime, the Swedish economy has been among the least badly hit in Europe.
If the rest of Europe is badly hit by a second or third wave, and Sweden is not, Swedish policy suddenly will look much better. Alternatively, if Sweden experiences a second wave of infections as big as or bigger than those of its neighbors, it will look far worse.
In other words, it is too soon to tell. I love to moralize about the moralizers! Which I do more of at the link.
Despite the lack of effective treatments or preventive strategies, the dementia epidemic is on the wane in the United States and Europe, scientists reported on Monday.
The risk for a person to develop dementia over a lifetime is now 13 percent lower than it was in 2010. Incidence rates at every age have steadily declined over the past quarter-century. If the trend continues, the paper’s authors note, there will be 15 million fewer people in Europe and the United States with dementia than there are now…
Researchers at Harvard University in Cambridge, Mass., reviewed data from seven large studies with a total of 49,202 individuals. The studies followed men and women aged 65 and older for at least 15 years, and included in-person exams and, in many cases, genetic data, brain scans and information on participants’ risk factors for cardiovascular disease.
The data also include a separate assessment of Alzheimer’s disease. Its incidence, too, has steadily fallen, at a rate of 16 percent per decade, the researchers found. Their study was published in the journal Neurology.
In 1995, a 75-year-old man had about a 25 percent chance of developing dementia in his remaining lifetime. Now that man’s chance declined to 18 percent, said Dr. Albert Hofman, chairman of the department of epidemiology at the Harvard School of Public Health and the lead author of the new paper.
Interestingly, this decline seems to be confined to Europe and the United States. Here is the Gina Kolata piece in the NYT.
“We must accept the new reality: The virus is widespread on Oahu,” said Anderson, noting that it’s becoming increasingly difficult for contact tracers to pinpoint the source of infection as the virus grows more and more prevalent.
Hawaii now has seen 2,448 positive cases of coronavirus since state health officials started reporting testing results in March. More than 500 of those cases were reported in the last seven days, and most of them are on Oahu.
Historically, about 1% to 2% of tests conducted in Hawaii have come back with positive COVID-19 results. But in recent days that percentage has crept up close to 5%.
“Any time it gets over 5%, there’s reason for concern,” Anderson said. “Some of the states where they’re having large outbreaks have high rates of over 10%. And, obviously, we don’t want to be there.”
The growing prevalence of COVID-19 in Hawaii could jeopardize the state’s ability to reopen public schools, bring college students back to campus and invite visitors to return to Hawaii, said Hawaii Gov. David Ige.
Here is the full story. Of course it is much better to have cases now — when superior treatments are available — than back in March or April. Still, the containment strategies that are supposed to work for the most part…do not in fact work.
Most major questions in ethics are unsettled, though of course I have my own views, as do many other people. I take that unsettledness as a fairly fundamental truth, I have been studying these matters for decades, and I even have several published articles in the top-ranked journal Ethics.
Now, if you take a whole group of people, give them medical licenses, teach them all more or less the same thing in graduate school, but not much other philosophy, and call it “medical ethics“…you have not actually gone much further. Arguably you have retrogressed.
So when I hear people appeal to “medical ethics,” my intellectual warning bells go off. To be sure, often I agree with those people, if only because I think contemporary American institutions often are not very flexible or able to execute effectively on innovations. For instance, I didn’t think America could make a go at Robin Hanson’s variolation proposal, and so I opposed it. “Medical ethics” seems to give the same instruction, though with less of a concrete institutional argument.
Still, the Lieutenant Colombo in me is bothered. What about other nations? Should we ever wish that they serve themselves up as medical ethics-violating guinea pigs, for the greater global good?
Medical ethics usually says no, or tries to avoid grappling with that question too directly. But I wonder.
How about that Russian vaccine they will be trying in October?
To be clear, I won’t personally try it, and I don’t want the FDA to approve it for use in the United States. But am I rooting for the Russians to try it this fall? You betcha. (Am I sure that is the correct ethical view? No! But I know the critics should not be sure either.) I am happy to revise my views as further information comes in, but I see a good chance that the attempt improves expected global welfare, and I think that is very often (but not always) a standard with strong and indeed decisive relevance. And all the new results on cross-immunities imply that some pretty simple vaccines can have at least partial effectiveness.
Why exactly is “medical ethics” so sure this Russian vaccine is wrong other than that it violates “medical ethics”? All relevant scenarios involve risk to millions of innocents, and I have not heard that Russians will be forced to take the vaccine. The global benefits could be considerable, and I do note that the Russian vaccine scenario is the one that potentially spends down the reputational capital of various medical establishments.
Trying a not yet fully tested vaccine still seems wrong to many medical ethicists, even if the volunteers are compensated so they are better off in ex ante terms, as in some versions of Human Challenge Trials, an idea that (seemingly) has been elevated from “violating medical ethics” to a mere “problematic.” Medical ethics claims priority over the ex ante Pareto principle, but I say we are back to the unsettled ethics questions on that one, but if anything with the truth leaning against medical ethics.
I find it especially strange when “medical ethics” is cited — often without further argumentation or explanation — on Twitter and other forms of social media as a kind of moral authority. It then seems especially glaringly obvious that the moral consensus was never there in the first place, and that there is a gross and indeed now embarrassing unawareness of that underlying social fact. It feels like citing Kant to the raccoon trying to claw through your roof.
I think medical ethics would not like this critique of medical ethics. Yet I will be watching the Russian vaccine experiment closely.
Addendum: There is also biomedical ethics, but that would require a blog post of its own. It is much more closely integrated with standard ethical philosophy, though it does not resolve any of the fundamental philosophical uncertainties.
We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections with 15 other facilities. Controlling for demographic and other factors, a home’s staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Results suggest that eliminating staff linkages between nursing homes could reduce COVID-19 infections in nursing homes by 44 percent.
That is from a new NBER working paper by M. Keith Chen, Judith A. Chevalier, and Elisa F. Long, and I am going to nominate this as one of the very best and most important papers of the year.
Health care workers may be less susceptible to COVID-19 infection than people in the communities they serve, according to surprising early data from an ongoing study at Hoag Memorial Hospital Presbyterian.
Of some 3,000 workers tested in May and June, only 1% had antibodies to the novel coronavirus in their blood, despite the fact that the Newport Beach hospital has cared for hundreds of COVID-19 patients.
That 1% is far lower than what has been found in wider communities. Some 4-6% of residents in Los Angeles, Santa Clara and Riverside counties had COVID antibodies when surveillance testing was done there over recent weeks and months.
“This is what surprises some people,” said Dr. Michael Brant-Zawadzki, principal investigator. “Despite the headlines you see saying health care workers are at higher risk of contracting the disease, we haven’t seen that. In fact, we’re seeing the reverse of that. The question is, why?”
It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events, such as individual’s extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, heterogeneity can also arise through long-term, or persistent variations in individual susceptibility or infectivity. Here, we show how to incorporate persistent heterogeneity into a wide class of epidemiological models, and derive a non-linear dependence of the effective reproduction number R_e on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of overdispersion: (1) It results in a major modification of the early epidemic dynamics; (2) It significantly suppresses the herd immunity threshold; (3) It significantly reduces the final size of the epidemic. We estimate social and biological contributions to persistent heterogeneity using data on real-life face-to-face contact networks and age variation of the incidence rate during the COVID-19 epidemic, and show that empirical data from the COVID-19 epidemic in New York City (NYC) and Chicago and all 50 US states provide a consistent characterization of the level of persistent heterogeneity. Our estimates suggest that the hardest-hit areas, such as NYC, are close to the persistent heterogeneity herd immunity threshold following the first wave of the epidemic, thereby limiting the spread of infection to other regions during a potential second wave of the epidemic. Our work implies that general considerations of persistent heterogeneity in addition to overdispersion act to limit the scale of pandemics.
Here is the full paper by Alexei Tkachenko, et.al., via the excellent Alan Goldhammer. These models are looking much better than the ones that were more popular in the earlier months of the pandemic (yes, yes I know epidemiologists have been studying heterogeneity for a long time, etc.).