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First the link to this week’s complete list as HTML and as PDF.
As Nadeau et al. demonstrate, closing borders on its own is not enough to stem the tide of an epidemic. Contrary to their conclusion New Zealand, Taiwan, and Singapore show that together with decisively dealing with internal spread, stopping any new entries can result in highly effective outcomes.
I don’t get Subramanian et al. at all. The reproductive number is derived from two inputs alone, the spread of infection expressed as e.g. a doubling time and the serial interval. As long as it stays reasonably constant over time, the dark number of unobserved cases has no influence on it whatsoever. Of course the real Reff of transmission may be different for symptomatic and asymptomatic cases, one higher and one lower that the observed average, but that is not what is reported here. I can’t pinpoint where exactly the fallacy arises, but a strong dependence of Reff on the fraction of unobserved asymptomatic cases has to be wrong.
Mann et al. is just one more example of treating model outcomes as data and as experimental verification of theory. When real world measured data contradict theoretical predictions, it is the data that have to be adjusted to fit.
And again with Ramo et al. and Zhang & Wei we have two examples for important cases of anthropogenic climate change, both nothing whatever to do with carbon emission from fossil fuel burning. Indeed in the case of Ramo et al. it can be convincingly argued, that increased use of fossil fuels might go a long way to mitigate the problem at least in the short and medium term.
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