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Covid-19 Came to the U.S. later than we thought

Research into the spread of Covid-19 continues, with an important new preprint by Michael Woroby et al up today (tl;dr see the writeup in Stat News).  The standard narrative about the arrival of Covid-19 in the U.S. is that a patient arrived in Seattle, WA from Wuhan on January 15^th.  He felt ill, and aware of CDC messaging about a new virus circulating in Hubei, sought medical help.  On Jan. 19, he became the first confirmed case of Covid in the U.S. Then, a few weeks later on Feb. 24, another patient turned up with what appeared to be community spread.  Containment had failed.  Per Woroby:

“On February 29th, 2020, a SARS-CoV-2 genome was reported from a second Washington State patient, ‘WA2’, whose virus had been sampled on February 24th as part of a community surveillance study of respiratory viruses. The report’s authors calculated a high probability that WA2 was a direct descendent of WA1 [the first patient, from January], coming to the surprising conclusion that there had by that point already been six weeks of cryptic circulation of the virus in Washington State. The finding, described in a lengthy Twitter thread on February 29th, fundamentally altered the picture of the SARS-CoV-2 situation in the US, and seemed to show how the power of genomic epidemiology could be harnessed to uncover hidden epidemic dynamics and inform policy making in real time” (3, internal citations omitted). 

Then there was more genetic sequencing, and it turned up a strange anomaly: the cases that appeared to stem from WA2 had mutated from WA1 in two places, and there was neither evidence of a transitional virus strain between them or of other infections in Washington whose genetic sequencing matched WA1.  Woroby et al then ran a computer simulation – 1000 times! – of the epidemic, seeding it with WA1.  The result was a surprise: “when we seeded the Washington outbreak simulations with WA1 on January 15th, 2020, we failed to observe a single simulated epidemic that has the characteristics of the real phylogeny” (7).  In other words, the narrative about the early spread in Washington is almost certainly wrong.  Patient WA1 was not the source, and the virus was not spreading covertly for weeks before emerging again in WA2.  Rather, the spread around WA1 was contained, and WA2 represented a new infection, and was either the source of the actual epidemic, or near it.  Specifically, the virus arrived a second time from Hubei, around Feb. 13 (95% probability between 2/7 and 2/19) (9).

Testing Nonsense Redux

As most folks know by now, there's two kinds of Covid tests.  One of them tests for whether you have the disease now.  The other tests for the presence of antibodies in your blood, indicating that you have had the disease at some point.  You might think that an outfit like, say, the CDC, would distinguish between them in its count of Covid-19 testing.  Alas, you'd be wrong, as The Atlantic reported today.  Why would the CDC (and Georgia, Texas, and some other states) do this?  Well, it lets you (a) claim to be doing a lot more testing for current infections than you currently are (because the addition of antibody tests to the viral tests drives up the total number of "tests"), and (b) will drive down the percentage of positive test results (because a small number of people have the antibodies, generally a much smaller number than test positive in most places in the U.S., especially those that conduct relatively few viral tests).

And if you do massive numbers of tests with low percent positive, then those annoying scientists say you can reopen sooner!  It puts you on the good green side of charts like the one above (source), not the bad yellow one.

Trump has been in office too long not to assume that this is the work of Trumpian political operatives and not CDC scientists, at least until proven otherwise.  Trump himself is obviously too stupid and incurious to know the difference between the testing types, but like Henry II, he makes it clear enough what he would like to see happen.  And that's lots of tests, lots of opening, and lots of loyal servants who translate his impulses into "reality."

More on Covid Transmission

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.

On understanding what "Covid cases" means

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.

Getting a more nuanced understanding of Covid Transmission

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

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.”