Category: Uncategorized
The Roadmap to Pandemic Resilience
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.
Economists survey epidemiological models
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.
Assorted non-Covid links
1. Bad trade and the loss of variety.
2. Can money buy happiness revisited: the new take is to hire a happiness agent.
3. Do people have a bias for low-deductible insurance? (yes, partly for peace of mind reasons)
4. Weird Phillips curve behavior has to do with costs, not degree of tightness in the labor market.
5. New results on Harvard discrimination against Asian-Americans. “Asian Americans are substantially stronger than whites on the observables associated with admissions…the richness of the data yields a model that predicts admissions extremely well. Our preferred model shows that AsianAmericans would be admitted at a rate 19% higher absent this penalty.”
6. New Devon Zuegel podcast with Alain and Marie-Agnes Bertaud.
Monday assorted links
2. An extensive and pretty devastating article on the testing fail of the CDC. Again, our regulatory state has been failing us. And coverage from the NYT.
3. At the margin: “Results show that informants were given approximately 70 East German marks worth of rewards more per year in the areas that had access to WGTV, as compared with areas with no reception—ironically an amount roughly equivalent to the cost of an annual East German TV subscription.”
4. “Bars and Restaurants Peel Cash From Walls to Help Idled Workers” (NYT).
5. Scott Sumner watch the islands. This piece seems to imply that in-migration is a major source of heterogeneity. I’ve also been receiving some emails from Xavier suggested tourist inflow is a major cause of heterogeneity, due to an ever fresh supply of hard to trace cases. No rigorous test yet of that one, but it is certainly in the running as a hypothesis. And if true, it suggests many parts of Africa may not be hit that hard.
6. Karlson, Stern, and Klein on Sweden.
7. South Africa and HIV/AIDS: will the latter have been good training for Covid-19? (Economist)
8. The danger of “herd immunity overshoot.”
9. Singapore government and the Virus Vanguard.
10. Beloit University moves to more flexible two-course module system. For now at least.
More simple economics of a pandemic
The difference in value to society of getting a vaccine in May 2021 vs March 2022 is huge, but the difference in private profits is not
That is from Luis Pedro Coelho. And thus there is a great import to accelerating speed, at least in some critical matters.
Brad DeLong makes the point that if you have some downward nominal (or real?) rigidities, you should allow prices to rise in the expanding sectors all the more.
So many of the most important points of economics can be expressed succintly, which makes it well-suited for both blogs and Twitter.
That’s all!
Sunday assorted links
1. Profile of Amy Finkelstein.
2. 1/3 test positive in a semi-random Chelsea, Mass. sample. And “Notably, 43.2% (95% CI 32.2-54.7%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic.” That is from northern Italy. and a further critique of the Santa Clara study.
3. Droplet more significant transmission than aerosol?
5. “According to the Navy, the classroom antics had a darker side.”
6. The new Magnus Carlsen tournament. And new @pmarca book recommendations.
7. NYT survey piece on heterogeneities.
8. Good and extensive west coast Kaiser data set, and further evidence that R doesn’t fall nearly as much as you might wish for. If you are advocating an extended lockdown, you really need to think this one through and present your reasoning. So far I don’t see enough people doing that, nothing close, including the economists maybe even especially the economists. Right now this is one of the biggest deficiencies in the debate.
9. Countries that have banned alcohol as part of their Covid-19 response. It is striking to me how accepting the American coastal intelligentsia is of a strict lockdown, yet a permanent ban on alcohol is to them an unacceptable idea, curtailing basic liberties and impractical.
10. “…stay-at-home orders caused people to stay at home: county-level measures of mobility declined by between 9% and 13% by the day after the stay-at-home order went into effect.” And: “We show that COVID-19 as a whole reduced consumer spending in a panel of over 1 million small businesses by 40% year-over-year. Conversely, COVID-19 did not affect aggregate consumer spending at 3,600 large businesses.3…Consumer spending at the brick-and mortar stores of large firms fell by 9%, but online transactions at these large firms increased by 56%.”
11. All this debt during a global recession is in fact dangerous.
12. William Hanage and Helen Jenkins at WaPo cover the IHME model with some seriousness. Good piece, but we should have been debating this six weeks ago or more.
13. Andrew Gelman on the Santa Clara study (brutal).
Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
That is a recent paper by Manski and Molinari, top people with econometrics. Here is the abstract:
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
Here is a very good tweet storm on their methods, excerpt: “What I love about this paper is its humility in the face of uncertainty.” And: “…rather than trying to get exact answers using strong assumptions about who opts-in for testing, the characteristics of the tests themselves, etc, they start with what we can credibly know about each to build bounds on each of these quantities of interest.”
I genuinely cannot give a coherent account of “what is going on” with Covid-19 data issues and prevalence. But at this point I think it is safe to say that the mainstream story we have been living with for some number of weeks now just isn’t holding up.
For the pointer I thank David Joslin.
Saturday assorted links
1. How to take care of your green onions.
2. New U. Texas mortality projections model. Here is an accompanying paper.
3. Spit tests seem to work pretty well relative to swabs.
5. Precht designs Parc de la Distance for outdoor social distancing.
6. “We are the neurotypicals of the COVID-19 world.”
8. A history of unemployment insurance.
9. “Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks,” by Glen Weyl, et.al.
10. They are the Chinese restaurant road trippers.
11. Measured effective reproduction numbers for various states (please note this comes with the usual data problems!). Will social distancing get these much below 0.8? I found this link to be quite pessimistic in its implications.
What I’ve been reading
1. Elizabeth A. Fenn, Pox Americana: The Great Smallpox Epidemic of 1775-82, quite a good book.
2. Louis Galambos with Jane Eliot Sewell, Networks of Innovation: Vaccine Development at Merck, Sharp and Dohme, and Mulford, 1895-1995. Imagine a book with both Vannevar Bush and Maurice Hilleman as leading and indeed intersecting characters. How is this for a sentence?: “Hilleman had spent his boyhood on a farm on which the German-American tradition was to “work like hell and live by the tenets of Martin Luther.””
3. John Duffy, The Sanitarians: A History of American Public Health. A little boring, and not conceptual enough, but is anything on this topic entirely boring at the current moment in time? Nonetheless this is a very useful overview and survey of public health issues in American history, and so I do not hesitate to recommend it.
4. Robert P. Saldin and Steven M. Teles, Never Trump: The Revolt of the Conservative Elites. Remarkably fair-minded and substantive, here is my blurb: “”Who are the Never Trumpers, what do they want, and what are their stories? Robert P. Saldin and Steven Teles have produced the go-to work on a movement that will likely prove of enduring influence in American politics.” Here is a relevant Atlantic article by Saldin and Teles. Recommended.
5. Anne Enright, Actress: A Novel. A subtle Irish story of a woman telling the tale of her now-departed famous, charismatic mother and her career in the theater. Unpeels like an onion as you read it, and reveals successively deeper layers of the story, it would make my “favorite fiction of the year” list pretty much any year. But please note it has not have the “upfront attention-grabbing style” that many of us have been trained to enjoy.
Peer Review of “COVID-19 Antibody Seroprevalence in Santa Clara County, California”
Here are 2300 words from Balaji, self-recommending. Here is the original piece. Balaji starts with:
The high reported positive rate in this serosurvey may be explained by the false positive rate of the test and/or by sample recruitment issues.
I look forward to posting more on this.
Ahem
A widely followed model for projecting Covid-19 deaths in the U.S. is producing results that have been bouncing up and down like an unpredictable fever, and now epidemiologists are criticizing it as flawed and misleading for both the public and policy makers. In particular, they warn against relying on it as the basis for government decision-making, including on “re-opening America.”
“It’s not a model that most of us in the infectious disease epidemiology field think is well suited” to projecting Covid-19 deaths, epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health told reporters this week, referring to projections by the Institute for Health Metrics and Evaluation at the University of Washington.
Others experts, including some colleagues of the model-makers, are even harsher. “That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool,” said epidemiologist Ruth Etzioni of the Fred Hutchinson Cancer Center, home to several of the researchers who created the model, and who has served on a search committee for IHME. “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.”
…The chief reason the IHME projections worry some experts, Etzioni said, is that “the fact that they overshot” — initially projecting up to 240,000 U.S. deaths, compared with fewer than 70,000 now — “will be used to suggest that the government response prevented an even greater catastrophe, when in fact the predictions were shaky in the first place.”
Here is the full story, from StatNews, by Sharon Begley with assistance from Helen Branswell, two very good and knowledgeable sources. Via Matt Yglesias.
To be clear, I am (and always have been) fully aware that there are more nuanced epidemiological models “sitting on on the shelf,” just as is true for macroeconomics and many other areas. But I ask you, where are the numerous cases of leading epidemiologists screaming bloody murder to the press, or on their blogs, or in any other manner, that the most commonly used model for this all-important policy analysis is deeply wrong and in some regards close to a fraud? Yes I know you can point to a few tweets from the more serious people, but where has the profession as a whole been? Who organized the protest letter and petition to The Wall Street Journal?
And to be clear, I have heard this model cited and discussed in many (off the record) policy discussions, this is not just something you can pin on the Trump administration narrowly construed (though they are at fault as well).
Friday assorted links
1. Trying to re-open a restaurant in Wuhan (Bloomberg). And why is there less panic in China? And the coronavirus culture that is Maryland.
2. Covid-19 occupational risk scores (not sure how much information those are based on).
3. Patrick Wolff and Jason Tepperman on macroprudential business interruption insurance.
4. Why isn’t India doing worse?
5. The virus chasers of Massachusetts (NYT).
6. Profile of Alex Berenson, contrarian.
7. Positive Remdesivir results. And criticism of said report.
8. Is the golden age of TV over, and if so why? (FT)
9. The “implicit temporary deregulation” of OSHA — good or bad? What would gdp be if OSHA were right now exercising its intended powers?
10. How the wealthy are making do without staff, including a cameo by Martha Stewart (WSJ).
11. Some skeptical results on BCG vaccines.
12. New estimates on population prevalence, much higher than reported cases.
13. ““Just realised my soap wasn’t working because it’s literally a block of cheese,” she wrote.”
What should I ask Glen Weyl?
I will be doing a Conversation with him, mostly about his ideas on Covid-19 response and testing, though we will cover other topics as well. So what should I ask him?
“Social Distancing is Working so Well!”
How do you feel about that statement? I take this as one psychometric test.
If your reaction is: “My goodness, these are tragic times but it is splendid and noble how we all can come together and sacrifice for a common endeavor!”…well…
…you have failed my test and I will suspect a wee bit of mood affiliation. Most likely it is bad news if the relative safety (for some) of the current moment comes from social distancing. Because at some point social distancing must end, or at least be significantly curtailed, and then a higher danger level may well reemerge.
Possibly you have inside information that a cure will be ready next week, but somehow I doubt it. You are happy because you like something about the process.
Alternatively, if you hear “social distancing is working so well!” and immediately feel a deep sense of foreboding, and begin to calculate whether good short-term results are correlated with better or worse long-term results. And then you calculate how how long the distancing can last for, due to governmental budget constraints, and then try to figure out what kinds of progress we might make in the meantime while the distancing lasts, and then start worrying about how reliant on social distancing we are becoming…
…But then you undertake a second-order calculation about how the greater danger spurred by the forthcoming decline in social distancing also might spur innovation…
And then you think “would it not be better if the current progress came from a more sustainable source, what might that be, how about faster than expected herd immunity amongst a relatively small group of heterogeneous super-spreaders, now what is the chance of that?”…
…and finish your analysis confused…
Then you are my kind of weirdo.
We are living in a time of psychometric tests.
Devon Zuegel on togetherness
When we come out of this, I expect people will value in-person even more, but not in the sense that we’ll spend more time with each other in-person but rather that we put it on more of a pedestal. It’ll be treated with a lot more reverence and importance, which means it’ll be a strong signal to choose to spend time physically with someone when you could more easily spend time virtually. It’ll feel more important but also more scarce and special.
I’ve seen a preview of this with my team, which was already almost all remote before this anyways. We spend less time physically with each other since we’re all working from different cities, but when we are together we are more focused on “making the most of it”. The one week every six months we’re together, we’re very careful to use the time to get to know each other, choosing to deprioritize real work those weeks. This means that the way we relate to each other is very intentional and self-conscious, rather than more of an organic growth of the relationship that happens by just having each other ambiently in the background all the time. It works well, and over the course of working with them over time I’ve come to feel extremely close to them. But it means that everything had to be much more explicit, and that we’ve had to do extra work to develop our own new norms rather than getting to default to what’s normal. That said, this has major perks too, because we get to reinvent as we go along!
Those are two paragraph from my email.