Category: Current Affairs
There is a new paper by Ivan Korolev:
This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I demonstrate using the data from Iceland that auxiliary information from random tests can be used to calibrate the initial parameters of the model and reduce the range of possible forecasts about the future number of deaths. Finally, I show that the basic reproduction number R0 can be identified from the data, conditional on the clinical parameters. I then estimate it for the US and several other countries, allowing for possible underreporting of the number of cases. The resulting estimates of R0 are heterogeneous across countries: they are 2-3 times higher for Western countries than for Asian countries. I demonstrate that if one fails to take underreporting into account and estimates R0 from the cases data, the resulting estimate of R0 will be biased downward and the model will fail to fit the observed data.
And here is a further paper on the IMHE model, by statisticians from CTDS, Northwestern University and the University of Texas, excerpt from the opener:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
Again, I am very happy to present counter evidence to these arguments. I readily admit this is outside my area of expertise, but I have read through the paper and it is not much more than a few pages of recording numbers and comparing them to the actual outcomes (you will note the model predicts New York fairly well, and thus the predictions are of a “train wreck” nature).
Let me just repeat the two central findings again:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
So now really is the time to be asking tough questions about epidemiology, and yes, epidemiologists. I would very gladly publish and “signal boost” the best positive response possible.
And just to be clear (again), I fully support current lockdown efforts (best choice until we have more data and also a better theory), I don’t want Fauci to be fired, and I don’t think economists are necessarily better forecasters. I do feel I am not getting straight answers.
These estimates are from the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine. More details here.
Some 72% of Americans polled said they would not attend if sporting events resumed without a vaccine for the coronavirus. The poll, which had a fairly small sample size of 762 respondents, was released Thursday by Seton Hall University’s Stillman School of Business.
When polling respondents who identified as sports fans, 61% said they would not go to a game without a vaccine. The margin of error is plus-or-minus 3.6%.
Only 12% of all respondents said they would go to games if social distancing could be maintained, which would likely lead to a highly reduced number of fans, staff and media at games.
I doubt if that poll is extremely scientific, but the key fact here is that people go to NBA games, and most other public entertainments, in groups. Fast forward a bit and see how the group negotiations will go. Of a foursome, maybe three people would go to the game and one would not. That group is likely to end up doing something else altogether different, without 19,000 other cheering fans screaming and breathing into their faces.
If half the people say they will go, that does not mean you get half the people. It means you hardly get anybody.
By the way, what percentage of the American population will refuse or otherwise evade this vaccine, assuming we come up with one of course?
Here is the ESPN story link.
I have had fringe contact with more epidemiology than usual as of late, for obvious reasons, and I do understand this is only one corner of the discipline. I don’t mean this as a complaint dump, because most of economics suffers from similar problems, but here are a few limitations I see in the mainline epidemiological models put before us:
1. They do not sufficiently grasp that long-run elasticities of adjustment are more powerful than short-run elasticites. In the short run you socially distance, but in the long run you learn which methods of social distance protect you the most. Or you move from doing “half home delivery of food” to “full home delivery of food” once you get that extra credit card or learn the best sites. In this regard the epidemiological models end up being too pessimistic, and it seems that “the natural disaster economist complaints about the epidemiologists” (yes there is such a thing) are largely correct on this count. On this question economic models really do better, though not the models of everybody.
2. They do not sufficiently incorporate public choice considerations. An epidemic path, for instance, may be politically infeasible, which leads to adjustments along the way, and very often those adjustments are stupid policy moves from impatient politicians. This is not built into the models I am seeing, nor are such factors built into most economic macro models, even though there is a large independent branch of public choice research. It is hard to integrate. Still, it means that epidemiological models will be too optimistic, rather than too pessimistic as in #1. Epidemiologists might protest that it is not the purpose of their science or models to incorporate politics, but these factors are relevant for prediction, and if you try to wash your hands of them (no pun intended) you will be wrong a lot.
3. The Lucas critique, namely that agents within a model, knowing the model, will change how the model itself operates. Epidemiologists seem super-aware of this, much more than Keynesian macroeconomists are these days, though it seems to be more of a “I told you that you should listen to us” embodiment than trying to find an actual closed-loop solution for the model as a whole. That is really hard, either in macroeconomics or epidemiology. Still, on the predictive front without a good instantiation of the Lucas critique again a lot will go askew, as indeed it does in economics.
The epidemiological models also do not seem to incorporate Sam Peltzman-like risk offset effects. If you tell everyone to wear a mask, great! But people will feel safer as a result, and end up going out more. Some of the initial safety gains are given back through the subsequent behavioral adjustment. Epidemiologists might claim these factors already are incorporated in the variables they are measuring, but they are not constant across all possible methods of safety improvement. Ideally you may wish to make people safer in a not entirely transparent manner, so that they do not respond with greater recklessness. I have not yet seen a Straussian dimension in the models, though you might argue many epidemiologists are “naive Straussian” in their public rhetoric, saying what is good for us rather than telling the whole truth. The Straussian economists are slightly subtler.
4. Selection bias from the failures coming first. The early models were calibrated from Wuhan data, because what else could they do? Then came northern Italy, which was also a mess. It is the messes which are visible first, at least on average. So some of the models may have been too pessimistic at first. These days we have Germany, Australia, and a bunch of southern states that haven’t quite “blown up” as quickly as they should have. If the early models had access to all of that data, presumably they would be more predictive of the entire situation today. But it is no accident that the failures will be more visible early on.
And note that right now some of the very worst countries (Mexico, Brazil, possibly India?) are not far enough along on the data side to yield useful inputs into the models. So currently those models might be picking up too many semi-positive data points and not enough from the “train wrecks,” and thus they are too optimistic.
On this list, I think my #1 comes closest to being an actual criticism, the other points are more like observations about doing science in a messy, imperfect world. In any case, when epidemiological models are brandished, keep these limitations in mind. But the more important point may be for when critics of epidemiological models raise the limitations of those models. Very often the cited criticisms are chosen selectively, to support some particular agenda, when in fact the biases in the epidemiological models could run in either an optimistic or pessimistic direction.
Which is how it should be.
Now, to close, I have a few rude questions that nobody else seems willing to ask, and I genuinely do not know the answers to these:
a. As a class of scientists, how much are epidemiologists paid? Is good or bad news better for their salaries?
b. How smart are they? What are their average GRE scores?
c. Are they hired into thick, liquid academic and institutional markets? And how meritocratic are those markets?
d. What is their overall track record on predictions, whether before or during this crisis?
e. On average, what is the political orientation of epidemiologists? And compared to other academics? Which social welfare function do they use when they make non-trivial recommendations?
f. We know, from economics, that if you are a French economist, being a Frenchman predicts your political views better than does being an economist (there is an old MR post on this somewhere). Is there a comparable phenomenon in epidemiology?
g. How well do they understand how to model uncertainty of forecasts, relative to say what a top econometrician would know?
h. Are there “zombie epidemiologists” in the manner that Paul Krugman charges there are “zombie economists”? If so, what do you have to do to earn that designation? And are the zombies sometimes right, or right on some issues? How meta-rational are those who allege zombie-ism?
i. How many of them have studied Philip Tetlock’s work on forecasting?
Just to be clear, as MR readers will know, I have not been criticizing the mainstream epidemiological recommendations of lockdowns. But still those seem to be questions worth asking.
I will be doing a Conversation with him, no associated public event. He has been tweeting about the risks of a financial crisis during Covid-19, but more generally he is one of the most influential historians, currently being a Professor at Columbia University. His previous books cover German economic history, German statistical history, the financial crisis of 2008, and most generally early to mid-20th century European history. Here is his home page, here is his bio, here is his Wikipedia page.
So what should I ask him?
Walmart, Amazon and other firms are developing safety protocols for the COVID workplace. Walmart, for example, will be doing temperature checks of its employees:
Walmart Blog: As the COVID-19 situation has evolved, we’ve decided to begin taking the temperatures of our associates as they report to work in stores, clubs and facilities, as well as asking them some basic health screening questions. We are in the process of sending infrared thermometers to all locations, which could take up to three weeks.
Any associate with a temperature of 100.0 degrees will be paid for reporting to work and asked to return home and seek medical treatment if necessary. The associate will not be able to return to work until they are fever-free for at least three days.
Many associates have already been taking their own temperatures at home, and we’re asking them to continue that practice as we start doing it on-site. And we’ll continue to ask associates to look out for other symptoms of the virus (coughing, feeling achy, difficulty breathing) and never come to work when they don’t feel well.
Our COVID-19 emergency leave policy allows associates to stay home if they have any COVID-19 related symptoms, concerns, illness or are quarantined – knowing that their jobs will be protected.
Amazon is even investing in their own testing labs.
Amazon Blog: A next step might be regular testing of all employees, including those showing no symptoms. Regular testing on a global scale across all industries would both help keep people safe and help get the economy back up and running. But, for this to work, we as a society would need vastly more testing capacity than is currently available. Unfortunately, today we live in a world of scarcity where COVID-19 testing is heavily rationed.
If every person, including people with no symptoms, could be tested regularly, it would make a huge difference in how we are all fighting this virus. Those who test positive could be quarantined and cared for, and everyone who tests negative could re-enter the economy with confidence.
Until we have an effective vaccine available in billions of doses, high-volume testing capacity would be of great help, but getting that done will take collective action by NGOs, companies, and governments.
For our part, we’ve begun the work of building incremental testing capacity. A team of Amazonians with a variety of skills – from research scientists and program managers to procurement specialists and software engineers – have moved from their normal day jobs onto a dedicated team to work on this initiative. We have begun assembling the equipment we need to build our first lab (photos below) and hope to start testing small numbers of our front line employees soon.
Actions and experiments like these will discover ways to work safely till we reach the vaccine era.
Hey people, what is up with this?
Via John V. And in the meantime, the virus has now affected 70% of New Jersey’s long-term care centers.
Here is the link, it is about one hour long, with questions interspersed throughout, the title is “The future social and political implications of COVID-19.” Self-recommending!
“The SpaceKnow data suggest a continued slowing in China’s economy, despite official data saying otherwise,” says Jeremy Fand, SpaceKnow’s chief executive.
Pollution data from SpaceKnow, collected via satellite by measuring things like methane and ozone over China, also suggest that activity remains depressed compared with previrus levels. That index, last updated on March 30, is unchanged from the end of February…
Regardless of why China’s activity remains lower than officially reported—whether it’s the virus, frozen demand, or a combination of factors—the point is that the country hasn’t yet begun to rebound.
Here is the full story by Lisa Beilfuss. Given this data, as I have been arguing, we should not expect a V-shaped U.S. recovery.
Shruti Rajagopalan and I have written a policy brief on pandemic policy in developing countries with specific recommendations for India. The Indian context requires a different approach. Even washing hands, for example, is not easily accomplished when hundreds of millions of people do not have access to piped water or soap. India needs to control the COVID-19 pandemic better than other nations because the consequences of losing control are more severe given India’s relatively low healthcare resources, limited state capacity, and large population of poor people, many of whom are already burdened with other health issues. We make 10 recommendations:
1: Any test kit approved in China, Japan, Singapore, South Korea, Taiwan, the United States, or Western Europe should be immediately approved in India.
2: The Indian government should announce a commitment to pay any private Indian lab running coronavirus tests at least the current cost of tests run at government labs.
3: All import tariffs and quotas on medical equipment related to the COVID-19 crisis should be immediately lifted and nullified.
4: Use mobile phones to survey, inform, and prescreen for symptoms. Direct any individual with symptoms and his or her family to a testing center, or direct mobile testing to them.
5: Keep mobile phone accounts alive even if the phone bills are not paid, and provide a subsidy for pay-as-you-go account holders who cannot afford to pay for mobile services.
6: Requisition government schools and buildings and rent private hotel rooms, repurposing them as quarantine facilities.
7: Rapidly scale up the production and distribution of masks and encourage everyone to wear masks.
8: Truck in water and soap for hand washing and use existing distribution networks to provide hand sanitizers.
9: Accept voter identification cards and AADHAAR cards for in-kind transfers at ration shops.
10: Announce a direct cash transfer of a minimum of 3000 rupees per month (equivalent to the poverty line of $1.25 a day or $38 a month) to be distributed through Jan Dhan accounts or mobile phone applications such as Paytm.
See the whole thing for more on the rationales.
Addendum: As we went to press we heard that India will lift tariffs on medical equipment. My co-author lobbied hard for this.
I “zoom bombed” a high school class that is using Modern Principles of Economics. I thought that it would be useful to relate some virus economics to some regular economics. Here’s what I said:
Why has the response to coronavirus been so poor? Exponential growth, rare events, and the necessity of using theory instead of experience.
Coronavirus infections, when unchecked, double approximately every three days. If we start out with 1000 infections that means in 10 doublings, just 30 days, there will be one million infections (1,2,4,8,16,32,64,256,512,1024). If you act early and stop just one doubling, you prevent 500,000 people from being infected. Speed is of the essence. But you need to act when the problem appears small. You need to use theory rather than observation which isn’t natural or easy.
People get good at something when they have repeated attempts and rapid feedback. People can get pretty good at putting a basketball through a hoop. But for other decisions we only get one shot. One reason South Korea, Hong Kong and Taiwan have been much better at handling coronavirus is that within recent memory they had the SARS and H1N1 flu pandemics to build experience. The US and Europe were less hit by these earlier pandemics and responded less well. We don’t get many attempts to respond to once-in-a-lifetime events.
Even as coronavirus swept through China and Italy, many people dismissed the threat by thinking that we were somehow different. We weren’t. Even within the United States some people think that New York is different. It’s not. Most people learn, if they learn at all, from their own experiences, not from the experiences of others–even others like them. Learning from your own mistakes and experiences is a good skill. Many people make the same mistakes over and over again. But learning from other people’s mistakes or experiences is a great skill of immense power. It’s rare. Cultivate it.
Now let’s apply these issues to another one close to your life. Savings and retirement. Savings also follow an exponential process, albeit one neither as rapid nor as certain as those involving viruses. The same principles apply, however. But in this case instead of wanting to avoid the gains at the end you want to start saving early in order to capture the big gains in your 50s and 60s as you approach retirement. You don’t get many attempts at retirement so you need to use theory rather than experience. And because you don’t get many attempts you need to learn from other people, including other people’s mistakes, to guide your savings decisions today.
The students asked good questions and we also talked about aggregate demand and supply and how to think about the economic crisis.
Hat tip: Joel Cohen and Dr. Brian Dille.
P.S. I didn’t actually zoom bomb the class. I was invited but it was a surprise to the students.
I thought it useful to sum up my current views in a single paragraph, here goes:
I don’t view “optimal length of shutdown” arguments compelling, rather it is about how much pain the political process can stand. I expect partial reopenings by mid-May, sometimes driven by governors in the healthier states, even if that is sub-optimal for the nation as a whole. Besides you can’t have all the banks insolvent because of missed mortgage payments. But R0 won’t stay below 1 for long, even if it gets there at all. We will then have to shut down again within two months, but will then reopen again a bit after that. At each step along the way, we will self-deceive rather than confront the level of pain involved with our choices. We may lose a coherent national policy on the shutdown issue altogether, not that we have one now. The pandemic yo-yo will hold. At some point antivirals or antibodies will kick in (read Scott Gottlieb), or here: “There are perhaps 4-6 drugs that could be available by Fall and have robust enough treatment effect to impact risk of another epidemic or large outbreaks after current wave passes. We should be placing policy bets on these likeliest opportunities.” We will then continue the rinse and repeat of the yo-yo, but with the new drugs and treatments on-line with a death rate at maybe half current levels and typical hospital stays at three days rather than ten. It will seem more manageable, but how eager will consumers be to resume their old habits? Eventually a vaccine will be found, but getting it to everyone will be slower than expected. The lingering uncertainty and “value of waiting,” due to the risk of second and third waves, will badly damage economies along the way.
So there you have it.
In a short-sighted blunder, India’s Supreme Court has ruled that private labs cannot charge for coronavirus tests:
NDTV: “The private hospitals including laboratories have an important role to play in containing the scale of pandemic by extending philanthropic services in the hour of national crisis…We thus are satisfied that the petitioner has made out a case…to issue necessary direction to accredited private labs to conduct free of cost COVID-19 test,” the court said.
Whether the private labs should be reimbursed by the government, will be decided later, Justices Ashok Bhushan and S Ravindra Bhat said in a hearing conducted via video conferencing.
The Supreme Court’s ruling will reduce the number of tests and dissuade firms from rushing to develop and field new drugs and devices to fight the coronavirus. A price is a signal wrapped up in an incentive. Instead of incentivizing investment, this order incentives firms to invest resources elsewhere.
Nor do private labs have a special obligation that mandates their conscription–an obligation to fund testing for all, falls on all.
The ruling is especially unfortunate because as Rajagopalan and Choutagunta document, India’s health care sector is predominantly private:
…India must rely primarily on the private sector and civil society to lead the response to COVID-19,…the role of the government should be financing and subsidizing testing and treatment for those who cannot afford to pay. India’s private healthcare system is better funded and better staffed than the government healthcare system, and it serves more people. It is estimated to be four times bigger in overall healthcare capacity, and it has 55 percent of the total hospital bed capacity, 90 percent of the doctors, and 80 percent of the ventilators.
The temptation to requisition private resources for state use in an emergency is ever present—but Indian policymakers must resist that temptation because it will compromise instead of increase capacity.
Benevolence is laudatory but even in a pandemic we should not rely on the benevolence of the butcher, brewer or baker for our dinner nor on the lab for our coronavirus tests. If we want results, never talk to suppliers of our own necessities, but only of their advantages.
Emergent Ventures, a project of the Mercatus Center at George Mason University, is leading a new “Fast Grants” program to support research to fight Covid-19. Here is the bottom line:
Science funding mechanisms are too slow in normal times and may be much too slow during the COVID-19 pandemic. Fast Grants are an effort to correct this.
If you are a scientist at an academic institution currently working on a COVID-19 related project and in need of funding, we invite you to apply for a Fast Grant. Fast grants are $10k to $500k and decisions are made in under 48 hours. If we approve the grant, you’ll receive payment as quickly as your university can receive it.
More than $10 million in support is available in total, and that is in addition to earlier funds raised to support prizes. The application site has further detail and explains the process and motivation.
I very much wish to thank John Collison, Patrick Collison, Paul Graham, Reid Hoffman, Fiona McKean and Tobias Lütke, Yuri and Julia Milner, and Chris and Crystal Sacca for their generous support of this initiative, and I am honored to be a part of it.
Meanwhile, elsewhere in the world (FT):
The president of the European Research Council — the EU’s top scientist — has resigned after failing to persuade Brussels to set up a large-scale scientific programme to fight Covid-19.
During World War II, the NDRC accomplished a lot of research very quickly. In his memoir, Vannevar Bush recounts: “Within a week NDRC could review the project. The next day the director could authorize, the business office could send out a letter of intent, and the actual work could start.” Fast Grants are an effort to unlock progress at a cadence similar to that which served us well then.
We are not able at this time to process small donations for this project, but if If you are an interested donor please reach out to firstname.lastname@example.org.
China bent the curve, Italy bent the curve and I believe that the curve is bending in the United States. Suppression is working and the second part of the strategy of test, trace and isolate will start to come into play in a few weeks. The states are gearing up to test, trace and isolate and several large serological surveys are already underway which will gives us a much better idea of how widely the virus has spread. Ideally, we will move from test, trace and isolate to a situation where we can conduct millions of tests weekly which will take us into the vaccine time.
Before testing is fully operational, however, we will need to follow safety protocols. We can learn about what works from what essential workers are doing now. Green Circuits in CA, for example, redesigned the shift schedule:
His first move was to redesign the plant’s work schedule. The company, owned by the Dallas-based private equity firm Evolve Capital, always had the first and second shifts overlap for a half-hour. That allowed workers arriving in the afternoon to confer with colleagues as they handed off duties.
But O’Neil said they realized that would risk their whole workforce getting quarantined for 14 days, if someone got infected by the coronavirus and spent time at the factory as part of this larger group.
The solution was to create three separate teams of 40 workers each. The first shift now ends at 2 p.m., and then there’s an hour when the workspaces, tools, and breakrooms are sanitized. The third team does not work at all, but rather is held in reserve and available to jump in if an illness hampers one of the two other teams of workers.
Other safety protocols include:
- Shift work for white collar workers as well as for blue collar workers. Including spreading work over the weekends.
- Senior shopping hours.
- Temperature checks, perhaps via passive fever cameras at work and public transport.
- Mandatory masks for public transportation.
- Masks for workers.
- Sanitation breaks for mandatory hand washing.
- Quarantining at work for essential workers, as the MLB is thinking of doing despite not being essential.
- Reducing touch surfaces (even with simple things like propping up bathroom doors) and copper tape for hi-touch surfaces that cannot be eliminated.
It will take longer to reopen restaurants, clubs and sports stadiums but I believe that applying these protocols will allow many of us to work safely. We aren’t ready yet but now is the time to plan for our return.