Xs Xplain’d

David 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

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.

additional deaths in England & Wales

Source: United Kingdom Office for National Statistics

David Spiegelhalter wrote another piece for The Guardian about the number of COVID-related deaths in Britain, remarking that with the objective statistic of excess death, the kingdom is doing worse than any other country, including Belgium which is reported as the worst performer in the fight again the pandemic based on its reported COVID death numbers. David also shows the proper degree of caution in providing reasons for this terrible record rather than starting the blame game. One factor differentiating England from other countries like Italy being the spread of its COVID clusters, partly due to the higher mobility of the population, in particular its travelling for vacations. (The comparison also reveals a stable higher level of overall mortality in the UK when compared with south-west EU countries, except Portugal. It surprisingly misses Germany, which is unlikely to be a country with missing statistics!)

Ghost Town [The Specials]

[Nature on] simulations driving the world’s response to COVID-19

Nature of 02 April 2020 has a special section on simulation methods used to assess and predict the pandemic evolution. Calling for caution as the models used therein, like the standard ODE S(E)IR models, which rely on assumptions on the spread of the data and very rarely on data, especially in the early stages of the pandemic. One epidemiologist is quote stating “We’re building simplified representations of reality” but this is not dire enough, as “simplified” evokes “less precise” rather than “possibly grossly misleading”. (The graph above is unrelated to the Nature cover and appears to me as particularly appalling in mixing different types of data, time-scale, population at risk, discontinuous updates, and essentially returning no information whatsoever.)

“[the model] requires information that can be only loosely estimated at the start of an epidemic, such as the proportion of infected people who die, and the basic reproduction number (…) rough estimates by epidemiologists who tried to piece together the virus’s basic properties from incomplete information in different countries during the pandemic’s early stages. Some parameters, meanwhile, must be entirely assumed.”

The report mentions that the team at Imperial College, which predictions impacted the UK Government decisions, also used an agent-based model, with more variability or stochasticity in individual actions, which require even more assumptions or much more refined, representative, and trustworthy data.

“Unfortunately, during a pandemic it is hard to get data — such as on infection rates — against which to judge a model’s projections.”

Unfortunately, the paper was written in the early days of the rise of cases in the UK, which means predictions were not much opposed to actual numbers of deaths and hospitalisations. The following quote shows how far off they can fall from reality:

“the British response, Ferguson said on 25 March, makes him “reasonably confident” that total deaths in the United Kingdom will be held below 20,000.”

since the total number as of April 29 is above 21,000 24,000 29,750 and showing no sign of quickly slowing down… A quite useful general public article, nonetheless.