New APPS

Remdesivir has been one of the most closely watched potential therapies for COVID-19, and a couple of early cohort and observational studies have been encouraging.  But apparently the first results from a randomized controlled trial in China indicate that it did not make a statistically significant difference.  At least, it looks that way: results were accidentally posted on the WHO website and then removed, with a promise of an official publication to come later.  It's all a bit murky, and there's a number of other trials ongoing, but this is not the sort of result you hope to see, even accidentally.

By now it should be apparent just how little we know about the coronavirus pandemic, from how to treat it to basic facts about what the “number of COVID cases” means. Even “deaths due to COVID” turns out to be difficult: both New York and UK have revised their numbers up to accommodate likely cases that hadn’t been counted, and there’s a brewing political battle over how to count them. This ignorance also affects pandemic modeling; the aspect I want to look at here is over what “social distancing” means.

COVID Models are for guiding action: we want to look at them and know when we can leave the house again (or, maybe more importantly, when the kids can leave the house again!). In that sense, models are inherently political, in several ways. First, the model doesn’t tell you what to do; the decision about what to do can be informed by the model, but it requires an entire apparatus of priorities, intuitions, and whatever else goes into a decision about what to do. Part of this is a second-order decision strategy about how to process uncertainty. I’ve suggested before that we’ve been operating on maximin; whether I’m right isn’t important here except to underline that the model is insufficient. Second, the construction of the model is going to embody a number of social judgments. I’m not being Latour here and arguing that whatever comes out of laboratories or complex instruments is broadly political (though he’s right). What I do want to say is that to use a model, you have to answer some antecedent questions what your use of the model is trying to achieve, and how society operates.

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President Nero wants you to know that the U.S. has conducted a bigly number of coronavirus tests, higher than he can count, and maybe even more tests than there were people at his inauguration!  Anyway, the U.S. is still terrible at COVID testing, as the following chart from Vox reminds us:

As the accompanying article points out, this is completely mediocre.  It is a little better than before, but it nonetheless underscores that we are not ready to go back to a new-normal.  All the actual plans I’ve seen begin with the presumption of widespread testing.  The U.S. is absolutely nowhere close to that.

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Cellphone tracking – whether through geolocation or something like detecting the proximity of bluetooth devices – has been getting a lot of attention for its potential to improve COVID surveillance.  Given that there are estimates that a workforce of upwards of 100,000 people would be necessary to get a good contact-tracing regime going in the U.S., any automated way to reduce this would be welcome.  Apple and Google have announced that they are developing an App for that.  It would enable a user to know if they had been within six feet or so of someone who tested positive for COVID.  Assume for the moment that enough people get tested that such a strategy would generate meaningful data, what are its limitations?  Here are 5 concerns.  Not surprisingly, they echo concerns about other uses of cellphone tracking: not just privacy invasion, but whether they actually generate a lot of false positives (the person in the apartment next to yours might well be within 6 feet of you), whether they promote discrimination (neighborhood hotspots, etc.), and so on.

The EFF has some useful principles here.

UPDATE: Here's a nice piece that talks about the complexities of reducing restrictions, framing the overall need in terms of keeping R0 (the number of new infections a given case leads to) from rising much above 1.

A new article in Science models our future under the new Coronavirus regime.  It is not pretty.  A few takeaways, followed by a thought on social distancing (the whole study is worth a read, because I’m cherry-picking here, and I’m not indicating anything about the modeling process, only some of the implications):

• Social distancing works, BUT when you stop it, COVID will come back. The relation between the two is complex, because effective social-distancing reduces population immunity: “We evaluated the impact of one-time social distancing efforts of varying effectiveness and duration on the peak and timing of the epidemic with and without seasonal forcing. When transmission was not subject to seasonal forcing, one-time social distancing measures reduced the epidemic peak size. Under all scenarios, there was a resurgence of infection when the simulated social distancing measures were lifted. However, longer and more stringent temporary social distancing did not always correlate with greater reductions in epidemic peak size. In the case of a 20-week period of social distancing with 60% reduction in R0, for example, the resurgence peak size was nearly the same as the peak size of the uncontrolled epidemic: the social distancing was so effective that virtually no population immunity was built. The greatest reductions in peak size come from social distancing intensity and duration that divide cases approximately equally between peaks
• You’d better hope COVID is not seasonal: “For simulations with seasonal forcing, the post-intervention resurgent peak could exceed the size of the unconstrained epidemic, both in terms of peak prevalence and in terms of total number infected. Strong social distancing maintained a high proportion of susceptible individuals in the population, leading to an intense epidemic when R0 rises in the late autumn and winter. None of the one-time interventions was effective in maintaining the prevalence of critical cases below the critical care capacity.” And: “One-time social distancing efforts may push the SARS-CoV-2 epidemic peak into the autumn, potentially exacerbating the load on critical care resources if there is increased wintertime transmissibility”

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Why would you let a pandemic get in the way of voter suppression?  Much better to use it as a tactic of voter suppression.  At least, I can't see any other other coherent way to read the Wisconsin GOP and SCOTUS refusal to either delay Wisconsin's vote today, or allow absentee ballots to be delayed.  Wisconsin GOPs shenanigans to keep itself in power are well-known; we shouldn't forget that the Roberts Court is systematically hostile to voting rights.  The Onion has the right tweet, and reminds us that Wisconsin has 77 Covid deaths today.  Check that space in 2-3 weeks.

Unless it's that Republicans hate democracy so much they don't even care who wins, as long as fewer people vote.

Plus ça change…

By Gordon Hull

I argued a few days ago that late capitalism, with its fetishization of efficiency, leaves us unprepared for a pandemic because of vulnerabilities in the supply chain. In a recent blogpost, Frank Pasquale adds some healthcare-specific texture to the point, noting how our healthcare system is almost designed to fail in a pandemic. He cites one of his own papers from 2014:

“The reduction in hospital facilities and other resources, although “efficient” in normal times, may prove disastrous if there is an epidemic. For example, one national-preparedness plan for pandemic flu estimated that, in a worst-case scenario, the United States would be short over 600,000 ventilators. “To some experts, the ventilator shortage is the most glaring example of the country’s lack of readiness for a pandemic,” one journalist noted. The lack of “surge capacity” throughout the health care industry is a major infrastructural shortcoming, likely to cause tremendous, avoidable suffering if a pandemic emerges” (179).

The quotes are from… 2006 and 2007, and refer to warnings coming after the SARS epidemic. In other words, we’ve been as unprepared as possible for 14 years, despite a near-miss epidemic and constant warnings from epidemiologists. So Trump is an idiot and an imposter, and his son-in-law supply czar is a feckless idiot who understands nothing about supply, but, as Pasquale underscores, there is another, longer timeline to our pandemic preparation failure.

In Pasquale’s paper, he notes that part of the problem is how we frame healthcare in terms of aggregate costs (and the need to keep costs down), a construction that makes it impossible to notice that some things are over-funded and others under-funded. In particular, not only can mantras of cost-cutting shield wasteful allocations of resources like those to hedge-fund managers from scrutiny, but it can also hide the fact that some aspects of healthcare (let’s see, hmm. Pandemic prevention!) are radically under-funded. Worse, there is absolutely no way to guarantee that money saved here will actually be reallocated to something more socially useful:

“If the health care cost-cutters had a plan for reallocating excess health sector spending to pay for care that is now undercompensated or absent, they would merit the influence they have now achieved. But in reality, money freed up by cost-cutting is much more likely to be retained as profit or claimed by capital and rentiers in some other way” (191)

We see at least three versions of these problems playing out now.

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Geolocation data is getting increasing attention as a way of tracking social distancing in particular.  Google has just released a bunch of its geolocation data, which tracks changes in trips to retail, parks and other places.

In the meantime, a new paper in Science says that a good contact-tracing App, if sufficiently robust and adequately deployed, could avoid the need for lock-downs. 

Of related interest, Zeynep Tufekci has a smart piece in The Atlantic, pointing out that disease modeling isn't useful so much for producing truth or knowledge, but as a guide for how to avoid worst outcomes.  This seems absolutely right to me, and is in line the way health policy folks are pursuing what I've called a maximin strategy.