Month: April 2020
Michael Kaan emails me:
Hi Tyler, I’m a healthcare professional in Canada and a long-time reader of your blog. For the past couple of years, observing the culture wars and various elections, I’ve noticed that child abuse is an extremely rare topic among the cultural left: the highly visible progressive segment that drives wokeness, is culturally powerful, etc. You know what their dominant concerns are. (On the right it’s basically non-existent.)While there’s nothing obviously wrong with their attention to sexual and racial discrimination, the energy put into it is disproportionate to the massive social cost of child abuse. Rates vary around the world, but in general it looks like about 30% of all children globally suffer some sort of serious maltreatment each year, often many times a year, repeated over multiple years.So one can easily estimate that billions of people have experienced this. In other words, more people have been abused as children than have experienced war, famine, or epidemics.
The impact and costs of this have been measured (low academic achievement, health problems, low earnings, drug and alcohol use, etc.), and child abuse is sometimes lethal. What puzzles me is why it has no legs politically. Among the woke crowd, if child abuse is mentioned it’s usually in terms of discrimination against girls or sexual minorities. But there are really no prominent voices actively campaigning to mitigate child abuse generally.Why is this? Is it overly complex? Is the phenomenon too widely dispersed demographically, so that an evil agent group isn’t easily identified? Does its persistence foreground chronic failures of the welfare state (if that’s the case)? Is it boring?
For a start, I would note that virtually everyone is again child abuse, so opposing it doesn’t make anyone significant look worse. But I am sure there is much more to it than that.
Rapid and accurate SARS-CoV-2 diagnostic testing is essential for controlling the ongoing COVID-19 pandemic. The current gold standard for COVID-19 diagnosis is real-time RT-PCR detection of SARS-CoV-2 from nasopharyngeal swabs. Low sensitivity, exposure risks to healthcare workers, and global shortages of swabs and personal protective equipment, however, necessitate the validation of new diagnostic approaches. Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies. To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards. When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection. Furthermore, we report less variability in self-sample collection of saliva. Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.
The FDA has also just approved an at-home test collected by nasal swab, a saliva test should not be far behind.
Hat tip: Cat in the Hat.
Somehow I missed this April 6 paper by Hall, Jones, and Klenow:
This short note develops a framework for thinking about the following question: What is the maximum amount of consumption that a utilitarian welfare function would be willing to trade off to avoid the deaths associated with COVID-19? Our baseline answer is 26%, or around 1/4 of one year’s consumption.
So what does that imply for optimal policy? Will we manage to lose both? Via Ivan Werning.
Our models demonstrate that while social distancing measures clearly do flatten the curve, strategic reduction of contact can strongly increase their efficiency, introducing the possibility of allowing some social contact while keeping risks low. Limiting interaction to a few repeated contacts emerges as the most effective strategy. Maintaining similarity across contacts and the strengthening of communities via triadic strategies are also highly effective. This approach provides empirical evidence which adds nuanced policy advice for effective social distancing that can mitigate adverse consequences of social isolation.
That is from a new paper by Per Block, et.al. I do not consider this a confirmed result, but it is consistent with how my intuitions have been developing, and the success in containing Covid-19 on various smallish islands.
Here is the audio and transcript, here is part of the summary:
He joined Tyler to discuss whether the world as a whole is becoming harder to predict, whether Goldman Sachs traders can beat forecasters, what inferences we can draw from analyzing the speech of politicians, the importance of interdisciplinary teams, the qualities he looks for in leaders, the reasons he’s skeptical machine learning will outcompete his research team, the year he thinks the ascent of the West became inevitable, how research on counterfactuals can be applied to modern debates, why people with second cultures tend to make better forecasters, how to become more fox-like, and more.
Here is one excerpt:
COWEN: If you could take just a bit of time away from your research and play in your own tournaments, are you as good as your own best superforecasters?
TETLOCK: I don’t think so. I don’t think I have the patience or the temperament for doing it. I did give it a try in the second year of the first set of forecasting tournaments back in 2012, and I monitored the aggregates. We had an aggregation algorithm that was performing very well at the time, and it was outperforming 99.8 percent of the forecasters from whom the composite was derived.
If I simply had predicted what the composite said at each point in time in that tournament, I would have been a super superforecaster. I would have been better than 99.8 percent of the superforecasters. So, even though I knew that it was unlikely that I could outperform the composite, I did research some questions where I thought the composite was excessively aggressive, and I tried to second guess it.
The net result of my efforts — instead of finishing in the top 0.02 percent or whatever, I think I finished in the middle of the superforecaster pack. That doesn’t mean I’m a superforecaster. It just means that when I tried to make a forecast better than the composite, I degraded the accuracy significantly.
COWEN: But what do you think is the kind of patience you’re lacking? Because if I look at your career, you’ve been working on these databases on this topic for what? Over 30 years. That’s incredible patience, right? More patience than most of your superforecasters have shown. Is there some dis-aggregated notion of patience where they have it and you don’t?
TETLOCK: [laughs] Yeah, they have a skill set. In the most recent tournaments, we’ve been working on with them, this becomes even more evident — their willingness to delve into the details of really pretty obscure problems for very minimal compensation is quite extraordinary. They are intrinsically cognitively motivated in a way that is quite remarkable. How am I different from that?
I guess I have a little bit of attention deficit disorder, and my attention tends to roam. I’ve not just worked on forecasting tournaments. I’ve been fairly persistent in pursuing this topic since the mid 1980s. Even before Gorbachev became general party secretary, I was doing a little bit of this. But I’ve been doing a lot of other things as well on the side. My attention tends to roam. I’m interested in taboo tradeoffs. I’m interested in accountability. There’re various things I’ve studied that don’t quite fall in this rubric.
COWEN: Doesn’t that make you more of a fox though? You know something about many different areas. I could ask you about antebellum American discourse before the Civil War, and you would know who had the smart arguments and who didn’t. Right?
…I had a very interesting correspondence with William Safire in the 1980s about forecasting tournaments. We could talk a little about it later. The upshot of this is that young people who are upwardly mobile see forecasting tournaments as an opportunity to rise. Old people like me and aging baby-boomer types who occupy relatively high status inside organizations see forecasting tournaments as a way to lose.
If I’m a senior analyst inside an intelligence agency, and say I’m on the National Intelligence Council, and I’m an expert on China and the go-to guy for the president on China, and some upstart R&D operation called IARPA says, “Hey, we’re going to run these forecasting tournaments in which we assess how well the analytic community can put probabilities on what Xi Jinping is going to do next.”
And I’ll be on a level playing field, competing against 25-year-olds, and I’m a 65-year-old, how am I likely to react to this proposal, to this new method of doing business? It doesn’t take a lot of empathy or bureaucratic imagination to suppose I’m going to try to nix this thing.
COWEN: Which nation’s government in the world do you think listens to you the most? You may not know, right?
7. Salim Furth blames the automobile, not the NYC subway. And here is criticism of the subway result from a blogger. Reading both my judgment is that the subway result does not hold up.
10. “Wash Your Hands,” Roaring Lion, Trinidad calypso.
We look at demographic mobility responses to Covid in NYC using mobile phone GPS, finding – wealthy flee the city – different sheltering response among demographic groups in the city – helps account for disparities in health outcomes
Searches for moving to NYC suburbs are up almost 250% compared to the same period in 2019.
Story here. Of course maybe those are the same people who in 2016 promised to move to Canada.
That is the topic of my Bloomberg column, here is one bit:
Whether or not that reaction is rational, it is easy to imagine the public being fearful about the potential of immigration to contribute to a pandemic resurgence. It does seem that regions able to restrict in-migration relatively easily — such as New Zealand, Iceland and Hawaii — have had less severe Covid-19 problems. New York City, which takes in people from around the world, has had America’s most severe outbreak. And the recent appearance of a second wave of Covid-19 in Singapore has been connected to ongoing migration there.
I have never thought the federal government would build Trump’s wall on the U.S.-Mexico border. But now I wonder whether it may well happen — perhaps in electronic form.
In addition to these effects, many migrants currently living in the U.S. might go back home. Say you are from southern India and live in Atlanta, and typically your parents or grandparents come to visit once a year. That is now much harder for them to do, and will be for the foreseeable future. India also might make it more difficult for Indian-Americans to return to visit their relatives, perhaps demanding an immunity certificate for entry. Many of these current migrants will end up returning home to live in their native countries.
But not all immigration will vanish:
n spite of all those possible restrictions, the pandemic itself may offer new reasons to embrace some forms of migration, if only to help Western economies continue to function. Many jobs are now more dangerous than before, because they involve face-to-face contact and time spent in enclosed spaces. Such professions as nursing and dental assistants, for example, already attracted many immigrants even before Covid-19. Working on farms may yet become more perilous if the virus strikes farm worker communities. New migrants from poorer countries will be willing to take on these risks — for extra income of course — but most U.S. citizens won’t go near them.
The reality may be an uptick in some forms of migration, mostly for relatively hazardous jobs.
In any case, the immigration debate two or three years from now will seem virtually unrecognizable, compared to what we had been expecting.
New York City’s multitentacled subway system was a major disseminator – if not the principal transmission vehicle – of coronavirus infection during the initial takeoff of the massive epidemic that became evident throughout the city during March 2020. The near shutoff of subway ridership in Manhattan – down by over 90 percent at the end of March – correlates strongly with the substantial increase in the doubling time of new cases in this borough. Maps of subway station turnstile entries, superimposed upon zip code-level maps of reported coronavirus incidence, are strongly consistent with subway-facilitated disease propagation. Local train lines appear to have a higher propensity to transmit infection than express lines. Reciprocal seeding of infection appears to be the best explanation for the emergence of a single hotspot in Midtown West in Manhattan. Bus hubs may have served as secondary transmission routes out to the periphery of the city.
That is from a new NBER working paper by Jeffrey E. Harris.
Using daily state-level coronavirus data and a synthetic control research design, we find that California’s statewide SIPO reduced COVID-19 cases by 152,443 to 230,113 and COVID-19 deaths by 1,940 to 4,951 during the first three weeks following its enactment. Conservative back of the envelope calculations suggest that there were approximately 2 to 4 job losses per coronavirus case averted and 108 to 275 jobs losses per life saved during this short-run post-treatment period.
That is from a new NBER working paper by Friedson, McNichols, Sabia, and Dave. As you probably know from now, I am reluctant to take “how well have we done with death so far” estimates at face value, but there you go. You now have your California estimate of the day.
6. How the Belgians count Covid-19 deaths. I call that one big nursing home fail, and I don’t just mean for Belgium.
8. Claims about heterogeneous strains — please use with extreme caution, I do not consider this verified, though it could be very important if true.
9. Study of France — only about 6% infected, other numbers too.
You may recall that some time ago MR posted an anonymous account of how the coronavirus problem actually was much worse in Japan than was being admitted by the Japanese government and broader establishment. It is now clear that this Cassandra was correct.
I can now reveal to you the full story of that posting behind the first link, including my role in it. Here is the opening excerpt:
By March 22nd, I strongly suspected there was a widespread coronavirus epidemic in Japan. This was not widely believed at the time. I, working with others, conducted an independent research project. By March 25th we had sufficient certainty to act. We projected that the default course of the epidemic would lead to a public health crisis.
We attempted to disseminate the results to appropriate parties, out of a sense of civic duty. We initially did this privately attached to our identities and publicly but anonymously to maximize the likelihood of being effective and minimize risks to the response effort and to the team. We were successful in accelerating the work of others.
The situation is, as of this writing, still very serious. In retrospect, our pre-registered results were largely correct. I am coming forward with them because the methods we used, and the fact that they arrived at a result correct enough to act upon prior to formal confirmation, may accelerate future work and future responses here and elsewhere.
I am an American. I speak Japanese and live in Tokyo. I have spent my entire adult life in Japan. I have no medical nor epidemiology background. My professional background is as a software engineer and entrepreneur. I presently work in technology. This project was on my own initiative and in my personal capacity.
I am honored to have played a modest role in this story, though full credit goes elsewhere, do read the whole thing. Hashing plays a key role in the longer narrative.
Led by Danielle Allen and Glen Weyl, the Safra Center for Ethics at Harvard has put out a Roadmap to Pandemic Resilience (I am a co-author along with others). It’s the most detailed plan I have yet seen on how to ramp up testing and combine with contact tracing and supported isolation to beat the virus.
One of the most useful parts of the roadmap is that choke points have been identified and solutions proposed. Three testing choke points, for example, are that nasal swaps make people sneeze which means that health care workers collecting the sample need PPE. A saliva test, such as the one just approved, could solve this problem. In addition, as I argued earlier, we need to permit home test kits especially as self-swab from near nasal appears to be just as accurate as nasal swabs taken by a nurse. Second, once collected, the swab material is classified as a bio-hazard which requires serious transport and storage safety requirements. A inactivation buffer, however, could kill the virus without killing the RNA necessary for testing and thus reduce the need for bio-safety techniques in transportation which would make testing faster and cheaper. Finally, labs are working on reducing the reagents needed for the tests.
Understanding the choke points is a big step towards increasing the quantity of tests.
The authors are Christopher Avery, William Bossert, Adam Clark, Glenn Ellison, Sara Fisher Ellison, the paper is very good but the abstract is uninformative. Here is one excerpt:
A notable shortcoming of the basic SIR model is that it does not allow for heterogeneity in state frequencies and rate constants. We discuss several different sources of heterogeneity in more detail in Section 2.
The most important and challenging heterogeneity in practice is that individual behavior varies over time. In particular, the spread of disease likely induces individuals to make private decisions to limit contacts with other people. Thus, estimates from scenarios that assume unchecked exponential spread of disease, such as the reported figures from the Imperial College model of 500,000 deaths in the UK and 2.2 million in the United States, do not correspond to the behavioral responses one expects in practice. Further, these gradual increases in “social-distancing” that can be expected over the courses of an epidemic change dynamics in a continuous fashion and thus blur the distinctions between mechanistic and phenomenological models.13 Each type of model can be reasonably well calibrated to an initial period of spread of disease, but further assumptions, often necessarily ad hoc in nature, are needed to extend either type of model to later phases of an epidemic.
I recommend the whole paper.
One thing that some people fail to realize is the following: This disease will have about the same fraction of population infected plus recovered In the post-lockdown equilibrium regardless of the policy path that gets us there. However, that does not mean that the number of dead is the same for all policies, because the infection fatality rate is so heterogenous with this disease.
The (often unrecognized) elephant in the room is that one set of policies may sort the least vulnerable population to be infected first while another set of policies may sort the most vulnerable population to be infected last. Protecting the most vulnerable effectively while infecting the least vulnerable quickly could theoretically save almost everyone for this particular disease.
Since the old and sick people often live in relative “lockdown” even at normal times, the general lockdown does the opposite of beneficial sorting by slowing down infections among the least vulnerable. The general lockdown kills more people over the whole epidemic by tilting the sorting in an unfavorable direction.
The hospital crowding is in my opinion a relatively unimportant issue compared to this because there is no effective “silver bullet” therapy for the disease.
That one is anonymous! And from another reader:
A lot people are citing a paper that looks at the impact of general lockdowns on ultimate deaths (over 24 month window) during the 1918 Spanish flue epidemic in the US. It’s important to understand that the 1918 epidemic and 2020 epidemic have a sorting effects that go in the opposite direction.
The 1918 disease was most dangerous to people with strong immune systems (young adults), and those people were also the ones that were most active in society and had most interpersonal contacts. Absent any general lockdown, those people were infected first and didn’t benefit from the long-run equilibrium of “herd immunity.” The general lockdowns during the 1918 disease epidemic reduced these vulnerable people’s infection probability relatively more than that of the less vulnerable people. This improved sorting and thereby saved lives.
The 2020 disease works in the opposite way. It is the most dangerous to old, sick people with weakest immune systems. Those people are relatively inactive at normal times and don’t have a large number of social contacts. The general lockdown increases those vulnerable people’s relative infection probability, because their routine doesn’t change much while less vulnerable people social distance. This adverse sorting due to general lockdowns causes more deaths, in theory at least.
In my opinion, the 1918 lockdown evidence should be interpreted as evidence of the importance of sorting, not evidence that general lockdowns are the right thing to do now.