Here are some relevant criticisms from Soltani, Calo, and Bergstrom:
Studies suggest that people have on average about a dozen close contacts a day—incidents involving direct touch or a one-on-one conversation—yet even in the absence of social distancing measures the average infected person transmits to only 2 or 3 other people throughout the entire course of the disease. Fleeting interactions, such as crossing paths in the grocery store, will be substantially more common and substantially less likely to cause transmission. If the apps flag these lower-risk encounters as well, they will cast a wide net when reporting exposure. If they do not, they will miss a substantive fraction of transmission events. Because most exposures flagged by the apps will not lead to infection, many users will be instructed to self-quarantine even when they have not been infected. A person may put up with this once or twice, but after a few false alarms and the ensuing inconvenience of protracted self-isolation, we expect many will start to disregard the warnings.
At least as problematic is the issue of false negatives—instances where these apps will fail to flag individuals as potentially at risk even when they’ve encountered someone with the virus. Smartphone penetration in the United States remains at about 81 percent—meaning that even if we had 100 percent installation of these apps (which is extremely unlikely without mandatory policies in place), we would still only see a fraction of the total exposure events (65 percent according to Metcalf’s Law). Furthermore, people don’t always have their phones on them.
There is also a very real danger that these voluntary surveillance technologies will effectively become compulsory for any public and social engagement. Employers, retailers, or even policymakers can require that consumers display the results of their app before they are permitted to enter a grocery store, return back to work, or use public services—is as slowly becoming the norm in China, Hong Kong, and even being explored for visitors to Hawaii.
Taken with the false positive and “griefing” (intentionally crying wolf) issues outlined above, there is a real risk that these mobile-based apps can turn unaffected individuals into social pariahs, restricted from accessing public and private spaces or participating in social and economic activities. The likelihood that this will have a disparate impact on those already hardest hit by the pandemic is also high. Individuals living in densely populated neighborhoods and apartment buildings—characteristics that are also correlated to non-white and lower income communities—are likelier to experience incidences of false positives due their close proximity to one another.
In another study:
Nearly 3 in 5 Americans say they are either unable or unwilling to use the infection-alert system under development by Google and Apple, suggesting that it will be difficult to persuade enough people to use the app to make it effective against the coronavirus pandemic, a Washington Post–University of Maryland poll finds.
And here are skeptical remarks from Bruce Schneier.
I also have worried about how testing and liability law would interact. If the positive cases test as positive, it may be harder for businesses and schools to reopen, because they did not “do enough” to keep the positive cases out, or perhaps the businesses and the schools are the ones doing the testing in the first place. Whereas under a lower-testing “creative ambiguity” equilibrium, perhaps it is easier to think in terms of statistical rather than known lives lost, and to proceed with some generally beneficial activities, even though of course some positive cases will be walking through the doors.
I wonder if there also is a negative economic effect, over the longer haul, simply by making fear of the virus more focal in people’s minds. The plus of course is simply that contact tracing does in fact slow down the spread of the virus and allows resources to be allocated to individuals and areas of greatest need.
Here is the opening:
Here is the full story by Will Hobson. Recommended.
I found it interesting throughout, the first half was on Covid-19 testing, and the second half on everything else. Here is the audio and transcript. Here is the summary:
Tyler invited Glen to discuss the plan, including how it’d overcome obstacles to scaling up testing and tracing, what other countries got right and wrong in their responses, the unusual reason why he’s bothered by price gouging on PPE supplies, where his plan differs with Paul Romer’s, and more. They also discuss academia’s responsibility to inform public discourse, how he’d apply his ideas on mechanism design to reform tenure and admissions, his unique intellectual journey from socialism to libertarianism and beyond, the common element that attracts him to both the movie Memento and Don McLean’s “American Pie,” what talent he looks for in young economists, the struggle to straddle the divide between academia and politics, the benefits and drawbacks of rollerblading to class, and more.
Here is one excerpt:
WEYL: There’s one really critical element of this plan that I don’t think has been widely discussed, which is that there are 40 percent of people in the essential sector who are still out there doing their jobs. There may have been some improvements in sanitation. There probably have been, though there have been a lot of issues with getting the PPE required to do that.
But those people are basically transmitting the diseases they always have been. And so, by far, our first priority has to be not “reopening the economy,” but rather stabilizing that sector of the economy so that transmission is not taking place within that sector.
Once we’ve accomplished that goal, it will actually be relatively easy to reopen the rest of the economy, given that that’s 40 percent. It’s just a doubling to get to everybody being in a disease-stabilized situation. So I really think the focus has to be on stabilizing the essential sector by building up this regimen. I think we can do that by the end of June.
Once that’s accomplished, I think we can, over the course of July, reintroduce most of the rest of the economy and have the confidence that, because we haven’t seen reemergence of diseases within the essential sector, that reintroducing everybody else will proceed in a similar fashion.
COWEN: Other than possibly the adoption of your plan, what do you think will be the most enduring economic or social change from this pandemic?
WEYL: My guess is that there will be a lot of large corporations that take on important social responsibilities because of the trust environment that you were talking about and that it becomes increasingly illegitimate for them to be run under a pure shareholder-maximization perspective once they’re taking on that role. I think we’re going to see fundamental shifts in some of the corporate governance parameters as a result of the social role that a bunch of companies end up taking on.
COWEN: At heart, coming out of the Jewish socialist tradition, through a matter of biographical accident, you first became a libertarian. Needed time to find your way back to the tradition you belonged to. Along the way, did economics, so you believe in some notion of markets, albeit directly adjusted by regulation and mechanism design. And you’ve moved away from methodological individualism.
But you’re this weird person of a Jewish socialist, believes in markets, and had this path leading away from libertarianism. No other person in the world probably is that, but you are. Is that a unified theory of you?
WEYL: Well, the thing that throws a little bit of a wrench into that is that I was actually a Jewish socialist before I became a libertarian.
COWEN: Does that strengthen or weaken the theory?
For me the most instructive part was this:
COWEN: What do you view yourself as rebelling against? At the foundational level.
But you will have to read or listen to hear Glen’s very good answer.
These would seem to be some important results:
To model COVID-19 spread, we use an SEIR agent-based model on a graph, which takes into account several important real-life attributes of COVID-19: super-spreaders, realistic epidemiological parameters of the disease, testing and quarantine policies. We find that mass-testing is much less effective than testing the symptomatic and contact tracing, and some blend of these with social distancing is required to achieve suppression. We also find that the fat tail of the degree distribution matters a lot for epidemic growth, and many standard models do not account for this. Additionally, the average reproduction number for individuals, equivalent in many models to R0, is not an upper bound for the effective reproduction number, R. Even with an expectation of less than one new case per person, our model shows that exponential spread is possible. The parameter which closely predicts growth rate is the ratio between 2nd to 1st moments of the degree distribution. We provide mathematical arguments to argue that certain results of our simulations hold in more general settings.
And from the body of the paper:
To create containment, we need to test 30% of the population every day. If we only test 10% of the population every day, we get 34% of the population infected – no containment (blue bars).
As for test and trace:
Even with 100% of contacts traced and tested, still mass-testing of just over 10% of the population daily is required for containment.
The authors are not anti-testing (though relatively skeptical about mass testing compared to some of its adherents), but rather think a combination is required in what is a very tough fight:
Our simulations suggest some social distancing (short of lockdown), testing of symptomatics and contact tracing are the way to go.
That is all from a new paper by Ofir Reich, Guy Shalev, and Tom Kalvari, from Google, Google, and Tel Aviv University, respectively. Here is a related tweetstorm. With this research, I feel we are finally getting somewhere.
Savers at the Bank of Nook are being driven to speculate on turnips and tarantulas, as the most popular video game of the coronavirus era mimics global central bankers by making steep cuts in interest rates.
The estimated 12m players of Nintendo’s cartoon fantasy Animal Crossing: New Horizons were informed last week about the move, in which the Bank of Nook slashed the interest paid on savings from around 0.5 per cent to just 0.05 per cent.
The total interest available on any amount of savings has now been capped at 9,999 bells — the in-game currency that can be bought online at a rate of about $1 per 1.9m bells.
The abrupt policy shift, imposed by an obligatory software update on April 23, provoked a stream of online fury that a once-solid stream of income had been reduced to a trickle with the stroke of a raccoon banker’s pen.
“I’m never going to financially recover from this,” one player wrote on a Reddit forum. “Island recession incoming,” said another.
Here is more from the FT, via Malinga Fernando.
The Economist has a full article on this topic, here is one good excerpt:
But the defining feature of the latest innovation revolution is breakneck speed. Companies are being forced to raise their corporate metabolism and overcome “analysis paralysis”, an affliction caused by top managers having pored over the same irrelevant case studies at business school. In a recent briefing consultants at Bain urged companies to throw out old data, test quickly and often, and assume you will be in testing mode for some time to come.
The article is interesting throughout, and here is my earlier post on the rising speed premium in a pandemic world.
…while I have written about Taiwan’s use of cellphone-enforced quarantines for recent travelers and close contacts of those infected, I should also note that every single positive infection — symptomatic or not — is isolated away from their home and family. That is also the case in South Korea, and while it was the case for Singaporean citizens, it was not the case for migrant workers, which is a major reason why the virus has exploded in recent weeks.
Here’s the thing, though: isolating people is hard. It would be very controversial. It would require overbearing police powers that people in the West are intrinsically allergic to. Politicians that instituted such a policy would be very unpopular. It is so much easier to let tech companies build a potential magic bullet, and then demand they let government use it; most people wouldn’t know or wouldn’t care, which appears to matter more than whether or not the approach would actually work (or, to put it another way, it appears that the French government sees privacy as a club with which to beat tech companies, not a non-negotiable principle their citizens demand).
So that is why I have changed my mind: Western governments are not willing to take actions that we know work because it would be unpopular and controversial (indeed, the fact that central quarantine is so clearly a violation of liberties is arguably a benefit, because there is no way people would tolerate it once the crisis is over). And, on the flipside, that makes digital surveillance too dangerous to build. Politicians would rather leverage tech companies to violate liberty on the sly, and tech companies, once they have the capability, are all too willing to offload the responsibility of using it wisely to whatever government entity is willing to give them cover. There just isn’t much evidence that either side is willing to make hard choices.
That is from Ben’s Stratechery email newsletter, gated but you can pay to get it. There is currently the risk that “test and trace” becomes for the Left what “chloroquine” has been for Trump and parts of the political right — namely a way to make otherwise unpalatable plans sound as if they have hope for more than “develop herd immunity and bankrupt the economy in the process.”
To be clear, I fully favor “test and trace,” and I’ve worked hard to help fund some of it. That said, I wonder if we will anytime soon reach the point where it is a game changer. So when people argue we should not reopen the economy until “test and trace” is in place, I increasingly see that as a kind of emotive declaration that others do not care enough about human lives (possibly true!), rather than an actual piece of advice.
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.
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?
That is the topic of my latest Bloomberg column, here is one excerpt:
Now consider issues beyond specific user groups. The U.S. will almost certainly need to introduce a “track and trace” system, using information technology, preferably with privacy safeguards. One version of this idea uses geolocation methods, which tracks where people are in physical space and sends individuals a text message if they come into close contact with others diagnosed with Covid-19.
That technology requires participants to have a smartphone. The federal government probably will not mandate smartphone usage, which would both be politically unpopular and difficult to enforce. Nonetheless, businesses are likely to turn to such schemes to increase workplace safety. But again, exactly who already owns or afford a smartphone? Some of the jobs with the closest physical contact, such as service jobs, employ relatively low paid workers.
Companies may well decide to help workers buy smartphones, perhaps with government subsidies too. But that would then make having a smartphone a job requirement, including in the retail and public sectors.
This would create a new and in some ways more serious digital divide. Imagine you want to visit your local shopping mall. Its owners might require that you subscribe to one of the Covid-19 tracing apps. Or imagine not being able to get your license renewed without a smartphone certifying your health status.
All of a sudden the U.S. will have a new segregation — between those who have smartphones and those who don’t. If you’re on the wrong side of that divide, many places and services will be hard if not impossible to reach.
And to close:
It is plausible that the U.S. could end up with 10% or more of the population exiled from many key institutions of American life — simply because they lack the right kind of technology.
Don’t get me wrong; the digital divide deserves the additional attention soon to come its way. The trick will be ensuring that any proposed solutions don’t just trade one kind of divide for another.
I can’t even figure out how to work those parking spots that are “app only” for the parking meter. Pity me!
There is another round of prize winners, and I am pleased and honored to announce them:
1. Petr Ludwig.
Petr has been instrumental in building out the #Masks4All movement, and in persuading individuals in the Czech Republic, and in turn the world, to wear masks. That already has saved numerous lives and made possible — whenever the time is right — an eventual reopening of economies. And I am pleased to see this movement is now having an impact in the United States.
Here is Petr on Twitter, here is the viral video he had a hand in creating and promoting, his work has been truly impressive, and I also would like to offer praise and recognition to all of the people who have worked with him.
The covid19india project is a website for tracking the progress of Covid-19 cases through India, and it is the result of a collaboration.
It is based on a large volunteer group that is rapidly aggregating and verifying patient-level data by crowdsourcing.They portray a website for tracking the progress of Covid-19 cases through India and open-sources all the (non-personally identifiable) data for researchers and analysts to consume. The data for the react based website and the cluster graph are a crowdsourced Google Sheet filled in by a large and hardworking Ops team at covid19india. They manually fill in each case, from various news sources, as soon as the case is reported. Top contributor amongst 100 odd other code contributors and the maintainer of the website is Jeremy Philemon, an undergraduate at SUNY Binghamton, majoring in Computer Science. Another interesting contribution is from Somesh Kar, a 15 year old high school student at Delhi Public School RK Puram, New Delhi. For the COVID-19 India tracker he worked on the code for the cluster graph. He is interested in computer science tech entrepreneurship and is a designer and developer in his free time. Somesh was joined in this effort by his brother, Sibesh Kar, a tech entrepreneur in New Delhi and the founder of MayaHQ.
3. Debes Christiansen, the head of department at the National Reference Laboratory for Fish and Animal Diseases in the capital, Tórshavn, Faroe Islands.
Here is the story of Debes Christiansen. Here is one part:
A scientist who adapted his veterinary lab to test for disease among humans rather than salmon is being celebrated for helping the Faroe Islands avoid coronavirus deaths, where a larger proportion of the population has been tested than anywhere in the world.
Debes was prescient in understanding the import of testing, and also in realizing in January that he needed to move quickly.
Please note that I am trying to reach Debes Christiansen — can anyone please help me in this endeavor with an email?
Here is the list of the first cohort of winners, here is the original prize announcement. Most of the prize money still remains open to be won. It is worth noting that the winners so far are taking the money and plowing it back into their ongoing and still very valuable work.
There is a new NBER working paper (by economists) on Covid-19:
We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
That is by Theresa Kuchler, Dominic Russell, and Johannes Stroebel.
Since COVID-19 can be transmitted through close proximity to affected individuals, public health organizations have identified contact tracing as a valuable tool to help contain its spread. A number of leading public health authorities, universities, and NGOs around the world have been doing important work to develop opt-in contact tracing technology. To further this cause, Apple and Google will be launching a comprehensive solution that includes application programming interfaces (APIs) and operating system-level technology to assist in enabling contact tracing. Given the urgent need, the plan is to implement this solution in two steps while maintaining strong protections around user privacy.
First, in May, both companies will release APIs that enable interoperability between Android and iOS devices using apps from public health authorities. These official apps will be available for users to download via their respective app stores.
Second, in the coming months, Apple and Google will work to enable a broader Bluetooth-based contact tracing platform by building this functionality into the underlying platforms. This is a more robust solution than an API and would allow more individuals to participate, if they choose to opt in, as well as enable interaction with a broader ecosystem of apps and government health authorities. Privacy, transparency, and consent are of utmost importance in this effort, and we look forward to building this functionality in consultation with interested stakeholders. We will openly publish information about our work for others to analyze.
Here is the full story. I cannot help but wonder if this would have happened sooner if not for a) antitrust concerns, and b) fears of existential risk due to attacks on the privacy issue. But I am pleased to see it is proceeding, and one hopes the risks on the legal side will not turn out to be too high.
That is the topic of my new Bloomberg column, excerpt:
The plunge in status-seeking behavior is yet another way in which the lockdown is a remarkable and scary social experiment. One possible consequence is that many people won’t work as much, simply because no one is watching very closely and it is harder to get that pat on the shoulder or kind word for extra effort.
Worse yet, for many people social approbation compensates for economic hardships, and that salve is now considerably weaker. Time was, even if you were unemployed, you could still walk down the street and command attention for that one stylish item in your wardrobe, or your cool haircut, or your witty repartee. Now there’s no one on the street to impress.
Americans are learning just how much we rely on our looks, our charisma and our eloquence for our social affect. As Sonia Gupta asked on Twitter: “Extremely attractive people, I have a genuine question for you, no snark: What’s it like to not be getting the regular daily social attention you might be accustomed to, now that you have to stay inside and isolate from others?”
…To some extent this status erosion is liberating. It may cause a lot of people to reexamine perennial questions about “what really matters.” There are other positive effects: fewer peer-related reasons to go out and spend money, for instance (do you really need that new jacket, or to try all the hot new restaurants?). That will help make tighter budgets or even unemployment more bearable. Some socially anxious people may even feel they are better off.
Yet overall this is a dangerous state of affairs.
There is much more at the link.
This is from a very able and perceptive correspondent:
|World 1.0||World 2.0|
|110 successive months of job growth||10 million jobless claims in 2 weeks|
|10 year bull market across sectors||Winners and losers with extreme outcome inequality|
|Full employment||30% unemployment|
|Base rate thinking||First principles thinking|
|Office by default||Remote by default|
|Office for work||Office for connection, community, ecosystem, makerspaces|
|Suit, tie, wristwatch, business card||Good lighting, microphone, webcam, home office background|
|Commute + traffic jams||Home + family|
|Last mile||Only mile|
|Restaurants||Groceries + delivery|
|$4 toast||Sourdough starter|
|$100k for college||Not paying $100k for a webinar|
|Internal issues||Exogenous shock|
|Lots of little problems||One big problem|
|Stupid bullshit||Actual issues|
|Too much technology||Too little technology|
|Assume some government competence||Assume zero government competence|
|Trusted institutions||Trusted people|
|Tail risk is kooky||Tail risk is mainstream|
|Boomers most powerful||Boomers most vulnerable|
|Productivity growth collapse||Economic collapse|
|Social services Democrat||UBI Communist|
|Corporate debt||Government debt|
|Techlash||Tech a pillar of civilization and lifeline to billions|
|Break up Amazon||Don’t break up Amazon!!!|
|Avoiding social issues||Avoiding layoffs|
|Phone is a cigarette||Phone is oxygen|
|Resource depletion||$20 oil, $0.75 watt solar, <$100/kwh batteries|
|Low volatility||High volatility|
|20th century||21st century|