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First the link to this week’s complete list as HTML and as PDF.
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The reproduction number R is most important for model building and highly relevant for a precise analysis in hindsight, when all the data are in. But as Alvarez et al. rightly stress again, it critically depends on an exact determination of the serial interval, which has to be determined independently, itself depends on outer conditions and circumstances, and can vary drastically and quickly in time (Ali et al., list of 2020-07-26). Thus for describing and evaluating an ongoing epidemic R is essentially use- and meaningless. What you need here is the trend, i.e. the current doubling of half time. (If you just assume a serial interval, both doubling time and R become essentially the same but only the former is correct.)
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Unless I misunderstand Bates et al. compare twice vaccinated subjects with and without a breakthrough infection. While not entirely without interest, their result is hardly surprising. What would have been of interest is a comparison between mild breakthrough and a third “booster” vaccination.
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Kupferschmidt’s “Missed Shots” offers nothing new but is an excellent overview especially for those, who have not followed everything Covid-related closely.
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In Hangartner et al. we again have an intervention from the moral high ground and utterly beyond reproach. But again, what we really have is not the single example chosen for the trial but rather a general method adaptable to anything. True, ten percent of the standard deviation is a small effect, but that’s for a single round. Elisabeth Noelle-Neumann’s “Schweigespirale” readily comes to mind here. Enlighteningly the authors themselves call their intervention a bot in spite of entirely using human experimenters this time. That tells us where they plan to be going. So again we have one more example of state controlled public money being spent on forming the scientific base for large scale social engineering.
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