Category: Current Affairs
Earlier I suggested that that we offer unemployed people jobs that could be done from home:
A 21st century jobs program would pay people to stay home and isolate, support people without work, and produce some useful output all at the same time.
Writing at Brookings, Apurva Sanghi and Michal Lokshin provide some more ideas:
Another high-potential area is document digitization: Only 10 percent of the world’s books are digitized. Even with the current level of optical character recognition (OCR) technology, for a book to be digitized, an independent person needs to check it for errors, problems with tables and images, tagging, and oversee the look of the resulting text. Handwritten documents, images, and tables, even in printed books, require manual processing, proofreading, careful checking, and quality control. A person would receive scanned images of, let’s say, old letters to decipher and type into the electronic document. Comparing the results of several independent people working on the same document would assure the quality of transcription.
I want to add one more item to the list: contact tracing. In addition to tracing apps, we are going to need hundreds of thousands of people doing contact tracing and most of it can be done with email and phone from home. Two birds, one stone.
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?
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.
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.
This is from my email, highly recommended, and I will not apply further indentation:
“Although there’s a lot of pre-peer-reviewed and strongly-incorrect work out there, I’ll single out Kevin Systrom’s rt.live as being deeply problematic. Estimating R from noisy real-world data when you don’t know the underlying model is fundamentally difficult, but a minimal baseline capability is to get sign(R-1) right (at least when |R-1| isn’t small), and rt.live is going to often be badly (and confidently) wrong about that because it fails to account for how the confirmed count data it’s based on is noisy enough to be mostly garbage. (Many serious modelers have given up on case counts and just model death counts.) For an obvious example, consider their graph for WA: it’s deeply implausible on its face that WA had R=.24 on 10 April and R=1.4 on 17 April. (In an epidemiological model with fixed waiting times, the implication would be that infectious people started interacting with non-infectious people five times as often over the course of a week with no policy changes.) Digging into the data and the math, you can see that a few days of falling case counts will make the system confident of a very low R, and a few days of rising counts will make it confident of a very high one, but we know from other sources that both can and do happen due to changes in test and test processing availability. (There are additional serious methodological problems with rt.live, but trying to nowcast R from observed case counts is already garbage-in so will be garbage-out.)
However, folks are (understandably, given the difficulty and the rush) missing a lot of harder stuff too. You linked a study and wrote “Good and extensive west coast Kaiser data set, and further evidence that R doesn’t fall nearly as much as you might wish for.” We read the study tonight, and the data set seems great and important, but we don’t buy the claims about R at all — we think there are major statistical issues. (I could go into it if you want, although it’s fairly subtle, and of course there’s some chance that *we’re* wrong…)
Ultimately, the models and statistics in the field aren’t designed to handle rapidly changing R, and everything is made much worse by the massive inconsistencies in the observed data. R itself is a surprisingly subtle concept (especially in changing systems): for instance, rt.live uses a simple relationship between R and the observed rate of growth, but their claimed relationship only holds for the simplest SIR model (not epidemiologically plausible at all for COVID-19), and it has as an input the median serial interval, which is also substantially uncertain for COVID-19 (they treat it as a known constant). These things make it easy to badly missestimate R. Usually these errors pull or push R away from 1 — rt.live would at least get sign(R – 1) right if their data weren’t garbage and they fixed other statistical problems — but of course getting sign(R – 1) right is a low bar, it’s just figuring out whether what you’re observing is growing or shrinking. Many folks would actually be better off not trying to forecast R and just looking carefully at whether they believe the thing they’re observing is growing or shrinking and how quickly.
All that said, the growing (not total, but mostly shared) consensus among both folks I’ve talked to inside Google and with academic epidemiologists who are thinking hard about this is:
- Lockdowns, including Western-style lockdowns, very likely drive R substantially below 1 (say .7 or lower), even without perfect compliance. Best evidence is the daily death graphs from Italy, Spain, and probably France (their data’s a mess): those were some non-perfect lockdowns (compared to China), and you see a clear peak followed by a clear decline after basically one time constant (people who died at peak were getting infected right around the lockdown). If R was > 1 you’d see exponential growth up to herd immunity, if R was 0.9 you’d see a much bigger and later peak (there’s a lot of momentum in these systems). This is good news if true (and we think it’s probably true), since it means there’s at least some room to relax policy while keeping things under control. Another implication is the “first wave” is going to end over the next month-ish, as IHME and UTexas (my preferred public deaths forecaster; they don’t do R) predict.
- Cases are of course massively undercounted, but the weight of evidence is that they’re *probably* not *so* massively undercounted that we’re anywhere near herd immunity (though this would of course be great news). Looking at Iceland, Diamond Princess, the other studies, the flaws in the Stanford study, we’re very likely still at < ~2-3% infected in the US. (25% in large parts of NYC wouldn’t be a shock though).
Anyways, I guess my single biggest point is that if you see a result that says something about R, there’s a very good chance it’s just mathematically broken or observationally broken and isn’t actually saying that thing at all.”
That is all from Rif A. Saurous, Research Director at Google, currently working on COVID-19 modeling.
Currently it seems to me that those are the smartest and best informed views “out there,” so at least for now they are my views too.
Under Lockdown Socialism:
–you can stay in your residence, but paying rent or paying your mortgage is optional.
–you can obtain groceries and shop on line, but having a job is optional.
–other people work at farms, factories, and distribution services to make sure that you have food on the table, but you can sit at home waiting for a vaccine.
–people still work in nursing homes that have lost so many patients that they no longer have enough revenue to make payroll.
–professors and teachers are paid even though schools are shut down.
–police protect your property even though they are at risk for catching the virus and criminals are being set free.
–state and local governments will continue paying employees even though sales tax revenue has collapsed.
–if you own a small business, you don’t need revenue, because the government will keep sending checks.
–if you own shares in an airline, a bank, or other fragile corporations, don’t worry, the Treasury will work something out.
This might not be sustainable.
That is from Arnold Kling. Too many of our elites are a little shy about pushing this message out there.
Here is a new paper by Seth Benzell, Avinash Collis, and Christos Nicolaides:
To prevent the spread of COVID-19, some types of stores and gathering places have been shut down while others remain open. The decision to shut down one type of location and leave another open constitutes a judgement about the relative danger and benefits of those locations. Using location data from a large sample of smartphones, nationally representative consumer preference surveys, and government statistics, we measure the relative transmission risk benefit and social cost of closing 30 different location categories in the US. Our categories include types of shops, schools, entertainments, and public spaces. We rank categories by those which should face stricter regulation via dominance across eight dimensions of risk and importance and through composite indexes. We find that from February to March, there were larger declines in visits to locations that our measures imply should be closed first. We hope this analysis will help policymakers decide how to reopen their economies.
Here is a summary picture:
MOMA take note!
Obviously such rankings are somewhat speculative, but sooner or later some kind of disaggregated road map like this is going to be necessary. And I would say sooner.
Here is Johan Giesecke, Swedish epidemiologist, interviewed by Freddie Sayers for a little over half an hour, one of the most interesting set pieces I have heard this year. “I’m going to tell you what I really think. I don’t usually do that.” He also gives his account of what Sweden did right and wrong, and he argues that more Swedes than Norwegians have died because a) Sweden has much larger nursing homes, and b) Swedish immigrants. How he puts matters is of great interest as well.
It is a good exercise to figure out exactly where and why his claims might be wrong. For a start, I don’t think his extreme claims about fatality and infection rates can be true, even if you agree with much of what he says.
And from the excellent Samir Varma here is a new Bloomberg article about the Swedes claiming success.
Here is part of the list of winners, there are more to follow soon, and I am happy to cite Mercatus Center, George Mason University as home to the project.
I would again like to thank everyone who helped to make this possible, most of all those who have offered very generous financial support.
To date Fast Grants has made 67 awards to support biomedical research. Fast Grants did not exist as recently as twelve days ago and it already has distributed more than $12 million.
Here is the short essay, opening excerpt:
Every Western institution was unprepared for the coronavirus pandemic, despite many prior warnings. This monumental failure of institutional effectiveness will reverberate for the rest of the decade, but it’s not too early to ask why, and what we need to do about it.
Many of us would like to pin the cause on one political party or another, on one government or another. But the harsh reality is that it all failed — no Western country, or state, or city was prepared — and despite hard work and often extraordinary sacrifice by many people within these institutions. So the problem runs deeper than your favorite political opponent or your home nation.
Part of the problem is clearly foresight, a failure of imagination. But the other part of the problem is what we didn’t *do* in advance, and what we’re failing to do now. And that is a failure of action, and specifically our widespread inability to *build*.
We see this today with the things we urgently need but don’t have. We don’t have enough coronavirus tests, or test materials — including, amazingly, cotton swabs and common reagents. We don’t have enough ventilators, negative pressure rooms, and ICU beds. And we don’t have enough surgical masks, eye shields, and medical gowns — as I write this, New York City has put out a desperate call for rain ponchos to be used as medical gowns. Rain ponchos! In 2020! In America!