This paper looks at the strategy of eliminating vs. mitigating and concludes that eliminating is better. It says that the countries which went for elimination (Australia, Iceland, Japan, New Zealand, and South Korea) have done much better than the ones which went for mitigation.
Um, notice anything about those countries? None of them have land borders except for South Korea, which only has a land border with North Korea, so it practically doesn’t have a land border. I think those were the only countries which could contemplate elimination.
The paper would be much stronger if there had been a large country with borders which had tried elimination.
This study did a blitz of please-stay-home advertisements around the time of Thanksgiving and Christmas holidays to randomly selected counties in the US, with paired control counties not getting a message. The counties with the advertising ended up with 3.5% fewer cases.
I just found this old (2020-09-09) article about cats. FIFTEEN PERCENT of the cats they surveyed in Wuhan tested positive for COVID-19. I’m tempted to say that cats, not bats, were the index species, but that’s probably not right; the cats probably caught it from humans and then other cats.
This paper talks about the distinctions between symptomatic, asymptomatic, and pre-symptomatic. It says that people have gotten classified as asymptomatic when really they were pre-symptomatic because they weren’t followed for long enough; it says that a lot of people were classified as asymptomatic because their symptoms were too mild or did not match the approved list of symptoms. (For example, “loss of smell” was not recognized as a symptom early on.)
This long tweet thread talks about asymptomatic Delta cases (and Lateral Flow Tests, and quarantine strategy), but points out that the studies on Delta symptoms are probably not taking into account that Delta is preferentially hitting the unvaccinated => younger people, who have milder symptoms.
This paper from CDC shows that kids (0-17 years old) have been much more likely to catch COVID-19 than the flu.
Three related papers just came out showing that the Alpha strain leads to worse outcomes. I had thought that had be debunked, but this paper adjusted carefully for all kinds of cofactors, and found a +42% risk of hospitalization in Denmark for Alpha over COVID Classic. This paper did the same for English hospitals, and found a +1155% risk of ICU admission and a +65% risk of death for Alpha over COVID Classic. This paper is fundamentally a summary of a whole bunch of Alpha-vs-COVID-Classic studies.
This Twitter thread has an estimate of Rt for various variants in the US; it finds that Rt is trending below 1 for all of the variants — except for Gamma.
Okay, look. I’m pretty bored with speculations of whether COVID-19 came from a lab leak: if it was, so what? It was clearly a mistake (unless you think China would unless this upon itself!). Do we need to waggle our finger at China and say, “Don’t do that again!”? I THINK THEY KNOW THEY SHOULDN’T DO THAT AGAIN. And if they don’t, us telling them not to do that again won’t make a whit of difference.
Ahem. That’s preamble to pointing y’all at a paper which details some detective work to find some genetic sequences from early cases which had been deleted from a sequence database at the request of the authors. (The author snooped around in Google Cloud and made some guesses and found them.) It’s not clear why the original author deleted the sequences: if it was an attempt to hide something or what. (One possibility: the Chinese government put out an edict that all COVID papers had to get government approval, and they retroactively cancelled papers and data which had not gotten approval.)
The author found three sequences which looked much more likely to be progenitor sequences (i.e. closer to bat coronaviruses) than the published sequences from the wet market. What does this mean? Does it mean COVID-19 could not possibly originate at the wet market? Welll not completely: there could have been transmission from bat to person A, person A infected a chain leading to person X who worked at the wet market and spreads it around the wet market. It’s just much much less likely. (Wuhan is a little bigger than the Chicago metro area. It’s not a little village.)
This preprint does a cost-benefit analysis of asymptomatic testing in Barcelona.
BC’s Provincial Health Officer Dr. Henry has frequently said that they found that testing asymptomatic people gave too many false negatives to be useful. I’m now wondering: how you tell if a test is a false negative or a true negative?
My friend Dan noted that efficacy in studies is probably lower than efficacy in real life: “In a study, you know you might have gotten a placebo, so you probably behave cautiously just in case. In the real-world, you know you got an effective vaccine, so you may increase the riskiness of your behaviour. The studies likely overestimate the real-world efficacy because of this risk compensation. Especially if people confuse reduced risk with license to do every risky thing without any fear of consequences.” (See Wikipedia’s Risk Compensation.)
In addition, studies tend to exclude a lot of people on purpose, usually people more at risk: people with comorbidities and pregnant people, for example. A lot of other people might get excluded by accident: busy people, disabled people, people who live a long way from sites which do studies, etc.
This paper shows good results of a pan-coronavirus (SARS-CoV, SARS-CoV-2 Classic, SARS-CoV-2 Beta, and two bat coronaviruses) vaccine in mice. They did this by making four different mRNA molecules, each one encoding a carefully designed spike protein with elements from two or three different coronaviruses. They then gave a first dose and a second dose of some combination of those four mRNA molecules and looked at antibody levels.
This paper talks about why vaccines (can) cause fatigue.
This study found that COVID-19 can hide out in the gut after the patient has allegedly recovered.
This old (2021-04-20) essay about how the author decided to send her little kids to daycare is excellent. It gives a good heuristic of “pay more attention to unexpected results that nobody wants”, for example.