New APPS

One of the widely-discussed metrics for understanding Covid-19 transmission is its R0 number – the average number of infections that a given person causes.  An R0 of 3, for example, means that each person infects an average of 3 others.  In order to stop the disease from eventually spiraling out of control you need to keep the R0 number at or below 1.  But of course R0 represents an average – and a recent write-up in Science elaborates on the emerging evidence that a relatively small number of cases account for most of the spread.  Understanding why that is could of course go a long way toward making it possible to restore some sense of normalcy, even if figuring this out is incredibly difficult.

One thing that does appear to be the case is that indoor transmission is more likely than outdoor, and that mixing large numbers of people together in small spaces is a bad idea.  That's why Michael Sorrell, president of Paul Quinn College, writes in the CHE that the desire to have in-person college in the fall is nonetheless a bad idea.

On the other hand, a group of German medical organizations just issued a statement advocating the immediate and full return of K-12 schools.  There is some nuance – older kids can do some social distancing, and there is an assumption of testing and contact-tracing – but they summarize quite a bit of research saying that there is little evidence that outbreaks occur in groups of kids, and some evidence that kids don't readily transmit the disease.  We are all about to owe France and Germany, which are both competent at testing and are gradually reopening schools, a debt for better understanding how Covid transmits among kids.

In the meantime, the dishes here keep piling up and the racist sociopath pretending to be President is deporting children who aren't sick cuz the #MexicanCaravanWuhanOBAMAChinaWTOFAKENEWS is obviously the reason 93,000+ Americans have died!  But that's ok, because we have done lots of tests, very good tests, and "per capita" is one of those phrases that libtards use to confuse you with their elitism.

With reference to malaria tracking, I've tried to suggest some of the reasons we don't really know what "Covid cases" means, either insofar as that is measured by positive tests (because we don't know how many more cases there are beyond the tested ones, so tested cases is at best an rough guide to Covid incidence) or symptoms (a task that gets harder by the day, as Covid continues to present in new and strange ways).  Here's a nice piece that starts with how they deal with malaria incidence, and works through a lot of the problems in translating from test results to meaningful information about Covid.

It’s fairly clear that one of the keys to living with Covid-19 is understanding the dynamics of transmission: absent something more nuanced than what we have, “stay 6 feet away from everyone at all times!” becomes the only public health advice that can be given.  Getting past the initial maximin strategy requires better data on everything.  There’s an interesting Twitter thread by Dr. Muge Cevik (St. Andrews) that collates some of the recent studies about transmission within and through social groups.

A lot of discussion recently about transmission dynamics, most of which are extrapolated from viral loads & estimates. What does contact tracing/community testing data tell us about actual probability of #COVID19 transmission(infection rate), high risk environments/age?
[thread]

— Dr Muge Cevik (@mugecevik) May 4, 2020

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It’s not a formal meta-analysis or anything like that but there’s emerging confirmation for the idea that most transmission depends on sustained, close contact, and that children do not appear to be a primary vector for the disease (this latter one is enormously important, because nobody knows for sure what should be done about schools).  She summarizes at the end (the paragraph breaks are mine):

“While the infectious inoculum required for infection is unknown, these studies indicate that close & prolonged contact is required for #COVID19 transmission. The risk is highest in enclosed environments; household, long-term care facilities and public transport. High infection rates seen in household, friend & family gatherings, transport suggest that closed contacts in congregation is likely the key driver of productive transmission. Casual, short interactions are not the main driver of the epidemic though keep social distancing! Increased rates of infection seen in enclosed & connected environments is in keeping with high infection rates seen in megacities, deprived areas, shelters. A recent preprint demonstrates that #COVID19 epidemic intensity is strongly shaped by crowding.

“Although limited, these studies so far indicate that susceptibility to infection increases with age (highest >60y) and growing evidence suggests children are less susceptible, are infrequently responsible for household transmission, are not the main drivers of this epidemic.”

“Finally, these studies indicate that most transmission is caused by close contact with a symptomatic case, highest risk within first 5d of symptoms. To note: this preprint suggests that most infections are not asymptomatic during infection.  In conclusion, contact tracing data is crucial to understand real transmission dynamics. Cautionary note: This data & interpretation is based on the available evidence as of May 4th. Our understanding might change based on community testing/lifting lockdown measures.”

As Daniele Lorenzini reminds us, the coronavirus pandemic exposes nothing if not the differential precarity of our biopolitics.  Sure, biopolitics is about promoting life, but it’s also about deciding that some people can die in order that others may live.  The most obvious candidates are “essential”  workers in various parts of the supply chain who form what Marx calls the "disposable industrial reserve army" of capitalism.

In case there was any doubt about this, Trump is apparently about to invoke the Defense Production Act to force meat packing and processing plants to stay open.  This is the same Donald Trump who has routinely failed to use the same DPA to get medical supplies to hospitals and caregivers. 

But what about when it’s a question of protecting a bunch of mostly Latinx folks with a median annual wage of under $28,000 and whose working conditions look like this:

“The demographics in the meatpacking industry may be multicultural in the 21st century, but workers share many of the same characteristics. They are uneducated and work in extremely unsafe work conditions. Nearly every worker in meatpacking plants has injuries. Cutting stations are located close to each other, automated lines move too quickly for workers to keep up, and workers must put in long shifts or fear losing their jobs. Men and women must wear goggles, hardhats, stainless steel mesh gloves, rubber aprons and chaps. Still, every worker from the line cutter to the cleaning crew is in danger of suffering amputations, body part crushing, burns, punctures and other traumatic injuries. Though the demographics have opened up to include all segments of society, poor safety conditions in the plants continue to plague workers. According to Human Rights Watch, the poor labor market has caused safety concerns to backslide in the United States. Collective bargaining eroded, and by 2005, the injury rate was twice as high in the meatpacking industry as any other industry in the country.”

Trump wants you to know: sustaining Americans’ egregious overconsumption of cheap meat, which involves the brutal exploitation of vulnerable workers in the best of times, is more important than PPE’s for healthcare workers!  Oh, wait, Trump says (per the Bloomberg piece) that “the government will provide additional protective gear for employees as well as guidance.”

I wonder if that will arrive before, or after, the additional protective gear for hospitals?  But let no one accuse Trump of inconsistency!  He’s using the DPA to ensure that vulnerable workers, many or even most non-white and/or female, are at high risk for Covid.

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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