Archive for UK

another poor infographics [from Nature, with a warning]

Posted in Books, Statistics with tags , , , , , , , , on December 23, 2021 by xi'an

intimate touch [wildlife photograph of the year]

Posted in Books, Kids, pictures, Travel with tags , , , , , , on October 20, 2021 by xi'an

back to W

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on October 3, 2021 by xi'an

Xs Xplain’d

Posted in Statistics with tags , , , , , , , , , , on January 31, 2021 by xi'an

tempDavid Spiegelhalter is starting a column in The Guardian about COVID-19, the first installment being about excess death statistics. Arguing rightly that it is “fairer to look at what has happened to the total number of deaths”, since this is an objective quantity (in countries with trustworthy death statistics). The discussion on how many of the excess deaths can be attributed to the pandemic is somewhat confusing, though, as little can be said with enough confidence, between the positive impact (flu deaths have plummeted, 30% less traffic deaths in France, &tc.) and the negative impact (stress, harsher economic or social conditions, &tc.) A worthy warning: the deficit in “other” deaths during the second wave is partly due to the extra deaths during the first wave, esp. for fragile and elderly persons.

the limits of R

Posted in Books, pictures, R, Statistics with tags , , , , , , , , , , , , on August 10, 2020 by xi'an

It has been repeated many times on many platforms, the R (or R⁰) number is not a great summary about the COVID-19 pandemic, see eg Rossman’s warning in The Conversation, but Nature chose to stress it one more time (in its 16 Jul edition). Or twice when considering a similar piece in Nature Physics. As Boris Johnson made it a central tool of his governmental communication policy. And some mayors started asking for their own local R numbers! It is obviously tempting to turn the messy and complex reality of this planetary crisis into a single number and even a single indicator R<1, but it is unhelpful and worse, from the epidemiology models being wrong (or at least oversimplifying) to the data being wrong (i.e., incomplete, biased and late), to the predictions being wrong (except for predicting the past). Nothing outrageous from the said Nature article, pointing out diverse degrees of uncertainty and variability and stressing the need to immediately address clusters rather than using the dummy R. As an aside, the repeated use of nowcasting instead of forecasting sounds like a perfect journalist fad, given that it does not seem to be based on a different model of infection or on a different statistical technique. (There is a nowcasting package in R, though!) And a wee bit later I have been pointed out at an extended discussion of an R estimation paper on Radford Neal’s blog.

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