I know very little about this area, but found these results of interest and worthy of further investigation:
Mounting evidence across disciplines shows that psychotherapy is more curative than antidepressants for mild-to-moderate depression and anxiety. Yet, few patients use it. This paper develops and estimates a structural model of dynamic decision-making to analyze mental health treatment choices in the context of depression and anxiety. The model incorporates myriad costs suggested in previous work as critical impediments to psychotherapy use. We also integrate links between mental health and labor outcomes to more fully capture the benefits of mental health improvements and the costs of psychotherapy. Finally, the model addresses measurement error in widely-used mental health variables. Using the estimated model, we find that mental health improvements are valuable, both directly through increased utility and indirectly through earnings. We also show that even though psychotherapy improves mental health, counterfactual policy changes, e.g., lowering the price or removing other costs, do very little to increase uptake. We highlight two conclusions. As patient reluctance to use psychotherapy is nearly impervious to a host of a priori reasonable policies, we need to look elsewhere to understand it (e.g., biases in beliefs about treatment effects, stigma, or other factors that are as yet unknown). More broadly, large benefits of psychotherapy estimated in randomized trials tell only half the story. If patients do not use the treatment outside of an experimental setting—and we fail to understand why or how to get them to—estimated treatment effects cannot be leveraged to improve population mental health or social welfare.
That is from a new NBER working paper by Christopher J. Cronin, Matthew P. Forsstrom, and Nicholas W. Papageorge.
The available data seems to meet the bar for an EUA.
I found this Adam Rogers Wired piece insightful and the best single treatment so far, and also interesting more generally on RCTs:
“Fifty thousand people have been given a treatment, and we cannot know whether it worked or not,” says Martin Landray, one of the leaders of the Randomised Evaluation of Covid-19 Therapies (or Recovery) Trial in England, a large-scale, multi-center, multi-drug randomized controlled trial that showed that the corticosteroid dexamethasone saved the lives of Covid-19 patients and the autoimmune drug hydroxychloroquine did not. (That 50,000 number was from a few weeks back, just after the plasma preprint came out.)
The main arguments against the decision from Trump/FDA seem to be “do RCTs” and “convalescent plasma isn’t shown to be so great.” But those points have it exactly backwards. Patients for trials are extremely scarce right now, and if convalescent plasma is not the highest probability big winner (and I suspect it isn’t), you won’t want to waste scarce patients on doing the RCT. Moreover, if you can’t get the RCT done with 98,000 or so patients, maybe you’re just not up to doing it period! (Please do think at the margin.) In the meantime, convalescent plasma does not seem to involve harms or risks, and it may offer some benefits. So why not let more people have easier access to it?
And might there be a tiny chance that American citizens demand stronger payment incentives for the relevant supplies here and also for other treatments?
If all people have is “do RCTs and CP isn’t shown to be so great,” I don’t think they have begun to engage with the arguments. And additionally politicizing the FDA is definitely a real cost to be reckoned with, but the Twitter noise I am seeing from public health experts seems oblivious to the fact that the FDA’s ex ante risk-averse stance was politicized to begin with (which is not necessarily a bad thing, but yes this is a basic fact — “politicization for me, but not for thee,” etc.).
Billions of dollars in federal funds earmarked for boosting nationwide Covid-19 testing remain unspent months after Congress made the money available, according to the U.S. Department of Health and Human Services.
In April, Congress allocated roughly $25 billion for federal agencies and states to expand testing, develop contact-tracing initiatives and broaden disease surveillance.
According to HHS data, only about 10% to 15% of that total has been drawn down, meaning the cash has been spent or committed to various efforts…
Of the $25 billion, some $10.25 billion was sent to states and U.S. territories in May to expand testing and develop contact-tracing programs at their discretion, but as of Aug. 14, just $121 million of that pool of funds had been drawn down.
Fast Grants it ain’t. Here is the full WSJ article by Scott Patterson and Sarah Krouse.
Sebastian Garren, to found John Paul II Preparatory School’s South Campus in St. Louis, a hybrid on-line and in-person educational alternative for K-12, also stressing Western history and the classics.
John Durant, for career development and writing, and explorations into notions of angels.
Krishaan Khubchand, 20 years old, studying law at Birkbeck, to study mega-projects and capital allocation, he is also a Progress Studies fellow.
Vignan Velivela. He started as a robotics engineer at Cruise Automation, is a member of the Explorers Club (wiki, BBC) for his work on the lightest planetary rover at Carnegie Mellon, worked on a peer-to-peer lending startup in India that was acqui-hired by PayTm, went to college (BITS Pilani) in India studying EE and Economics, and now is co-founder of AtoB.
Wasteland Ventures (no web page), to support their efforts in talent search and development.
And two Emergent Ventures anti-Covid prizes have been awarded to:
Witold Wiecek, Bayesian statistician and consultant, for his work on the Bayesian modeling of the COVID-19 epidemic, and the design of an optimal vaccine portfolio, in cooperation with the Accelerating Health Technologies team.
Arthur W. Baker, for his efforts on incentive design for vaccines, in cooperation with the Accelerating Health Technologies team.
Here are previous winners of Emergent Ventures grants and prizes.
That is the topic of my latest Bloomberg column, here is one excerpt:
…here is why I am not yet an unreconstructed economic optimist. Covid-19 cases have acquired a stigma, and that stigma is likely to persist above and beyond the dangers associated with the virus itself.
If, say, 20 Covid-19 cases were identified within a high school today, there is a very real risk that those infected students would carry the virus home and infect older and more vulnerable people. There is also a small risk that the students would sustain damage themselves. Not surprisingly, most schools won’t reopen because they cannot deal with the burden — institutionally, legally or otherwise — of being connected to significant numbers of Covid-19 cases.
The question is how this stigma ends. If rates of death and possible long-term damage fell to half of their current levels? One-third? Less? I strongly suspect these same schools still would be unwilling to reopen, due in part to phantom risk.
If rates of death and damage fell to one-fifth of their current level, perhaps, there would be more reopenings — but there would still be a lot of reluctance to restore previous levels of attendance. How about one-tenth the level of mortality? It is hard to say when people will feel comfortable once again.
Along these lines, as long as clusters of reported cases are possible — regardless of mortality rates — many high-rise office buildings will not let workers and visitors simply pile into their elevators.
Many sites likely to experience identifiable, traceable clusters of cases will keep their doors shut, or open them on only a very limited basis. Further declines in the mortality rate won’t help much, because “37 Covid-19 Cases Identified at UC-Berkeley” is enough of a headline to create reputational risk and an institutional response. Even if everyone makes a speedy recovery, that won’t get the same kind of media coverage.
There is much more at the link, and my point will grow increasingly relevant, first of all in NYC and environs (partial herd immunity there, at least for the time being).
We compare COVID-19 case loads and mortality across geographic areas that hosted more vs fewer NHL hockey games, NBA basketball games, and NCAA basketball games during the early months of 2020, before any large outbreaks. We find that hosting one additional NHL/NBA game leads to an additional 783 COVID-19 cases during March-mid May and an additional 52 deaths. Similarly, we find that hosting an additional NCAA Division 1 men’s basketball games results in an additional 31 cases and an additional 2.4 deaths. Back of the envelope calculations suggest that the per-game fatality costs exceed consumption benefits by a wide margin.
That is from Coady Wing, Daniel H. Simon, and Patrick Carlin. I think we have not a good enough model of the heterogeneities of prevalence across regions for those to be reliable estimates. Still, I am happy to see more work on the question of what in particular causes Covid cases, and also whether sporting events play a significant role.
Malaysia has detected a strain of the new coronavirus that’s been found to be 10 times more infectious.
The mutation called D614G was found in at least three of the 45 cases in a cluster that started from a restaurant owner returning from India and breaching his 14-day home quarantine. The man has since been sentenced to five months in prison and fined. The strain was also found in another cluster involving people returning from the Philippines…
The mutation has become the predominant variant in Europe and the U.S., with the World Health Organization saying there’s no evidence the strain leads to a more severe disease.
Here is the Bloomberg story, please consider this subject to further revision!
The FDA has just approved a new and important Covid-19 test:
“Wide-spread testing is critical for our control efforts. We simplified the test so that it only costs a couple of dollars for reagents, and we expect that labs will only charge about $10 per sample. If cheap alternatives like SalivaDirect can be implemented across the country, we may finally get a handle on this pandemic, even before a vaccine,” said Grubaugh.
One of the team’s goals was to eliminate the expensive saliva collection tubes that other companies use to preserve the virus for detection. In a separate study led by Wyllie and the team at the Yale School of Public Health, and recently published on medRxiv, they found that SARS-CoV-2 is stable in saliva for prolonged periods at warm temperatures, and that preservatives or specialized tubes are not necessary for collection of saliva.
Of course this part warmed my heart (doubly):
The related research was funded by the NBA, National Basketball Players Association, and a Fast Grant from the Emergent Ventures at the Mercatus Center, George Mason University.
The NBA had the wisdom to use its unique “bubble” to run multiple tests on players at once, to see how reliable the less-known tests would be. This WSJ article — “Experts say it could be key to increasing the nation’s testing capacity” — has the entire NBA back story. At an estimated $10 a pop, this could especially be a game-changer for poorer nations. Furthermore, it has the potential to make pooled testing much easier as well.
Here is an excerpt from the research pre-print:
The critical component of our approach is to use saliva instead of respiratory swabs, which enables non-invasive frequent sampling and reduces the need for trained healthcare professionals during collection. Furthermore, we simplified our diagnostic test by (1) not requiring nucleic acid preservatives at sample collection, (2) replacing nucleic acid extraction with a simple proteinase K and heat treatment step, and (3) testing specimens with a dualplex quantitative reverse transcription PCR (RT-qPCR) assay. We validated SalivaDirect with reagents and instruments from multiple vendors to minimize the risk for supply chain issues. Regardless of our tested combination of reagents and instruments from different vendors, we found that SalivaDirect is highly sensitive with a limit of detection of 6-12 SARS-CoV-2 copies/μL.
No need to worry and fuss about RNA extraction now. Here is the best simple explanation of the whole thing.
The researchers are not seeking to commercialize their advance, rather they are making it available for the general benefit of mankind. Here is Nathan Grubaugh on Twitter. Here is Anne Wyllie, also a Kiwi and a Kevin Garnett fan. A further implication of course is that the NBA bubble is not “just sports,” but also has boosted innovation by enabling data collection.
All good news of course, and Fast at that. And this:
“This could be one the first major game changers in fighting the pandemic,” tweeted Andy Slavitt, a former acting administrator of the Centers for Medicare and Medicaid Services in the Obama administration, who expects testing capacity to be expanded significantly. “Rarely am I this enthusiastic… They are turning testing from a bespoke suit to a low-cost commodity.”
And here is coverage from Zach Lowe. I am very pleased with the course of Fast Grants more generally, and you will be hearing more about it in the future.
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: