Archive for coronavirus epidemics
updated (over)mortality curves
Posted in Statistics with tags coronavirus epidemics, COVID-19, France, INSEE, lockdown, mortality chart, official statistics, quarantine on May 26, 2020 by xi'anpolitics coming [too close to] statistics [or the reverse]
Posted in Books, pictures, Statistics, University life with tags Bayesian decision theory, Boris Johnson, coronavirus epidemics, COVID-19, data, David Spiegelhalter, death certificate, Jim Smith, official statistics, The Guardian, UK Parliement, University of Warwick on May 9, 2020 by xi'an
On 30 April, David Spiegelhalter wrote an opinion column in The Guardian, Coronavirus deaths: how does Britain compare with other countries?, where he pointed out the difficulty, even “for a bean-counting statistician to count deaths”, as the reported figures are undercounts, and stated that “many feel that excess deaths give a truer picture of the impact of an epidemic“. Which, on the side, I indeed believe is a more objective material, as also reported by INSEE and INED in France.
“…my cold, statistical approach is to wait until the end of the year, and the years after that, when we can count the excess deaths. Until then, this grim contest won’t produce any league tables we can rely on.” D. Spiegelhalter
My understanding of the tribune is that the quick accumulation of raw numbers, even for deaths, and their use in the comparison of procedures and countries is not helping in understanding the impacts of policies and actions-reactions from a week ago. Starting with the delays in reporting death certificates, as again illustrated by the ten day lag in the INSEE reports. And accounting for covariates such as population density, economic and health indicators. (The graph below for instance relies on deaths so far attributed to COVID-19 rather than on excess deaths, while these attributions depend on the country policy and its official statistics capacities.)
“Polite request to PM and others: please stop using my Guardian article to claim we cannot make any international comparisons yet. I refer only to detailed league tables—of course we should now use other countries to try and learn why our numbers are high.” D. Spiegelhalter
However, when on 6 May Boris Johnson used this Guardian article during prime minister’s questions in the UK Parliement, to defuse a question from the Labour leader, Keir Starmer, David Spiegelhalter reacted with the above tweet, which is indeed that even with poor and undercounted data the total number of cases is much worse than predicted by the earlier models and deadlier than in neighbouring countries. Anyway, three other fellow statisticians, Phil Brown, Jim Smith (Warwick), and Henry Wynn, also reacted to David’s tribune by complaining at the lack of statistical modelling behind it and the fatalistic message it carries, advocating for model based decision-making, which would be fine if the data was not so unreliable… or if the proposed models were equipped with uncertainty bumpers accounting for misspecification and erroneous data.
un des aspects surprenants des analyses et des commentaires sur l’épidémie de Covid-19 est l’absence de la statistique
Posted in Statistics, University life with tags coronavirus epidemics, COVID-19, demographics, dynamical system, France, INED, Le Monde, Statistics on May 6, 2020 by xi'an
From one French demographer (INED) in Le Monde [my translation], with a clustering of French departments into three classes [the figures on the above map are the lags after the first death in Haut-Rhin]:
One of the surprising aspects of the analyses and commentaries on the Covid-19 epidemic is the absence of statistics. Every evening, however, we are bombarded with figures, and many sites, from Public Health France (SpF) to Johns-Hopkins University (Maryland), abound in data.
But a number carries a meaning only in reference to other figures. This is where the real statistics start. However, apart from comparing the number of contagions and deaths by country and date, little has been learned from the data, which could provide useful information on the nature and progression of the epidemic (…)
We can see that the diversity of close contacts is one of the keys to the evolution of the epidemic. Instead of reasoning on abstract coefficients such as the famous average number R⁰ of contagions per person, we should be able to delve into the details of these contagions. We see here that traffic axes, institutions and housing probably occupy a strategic position towards an explanation.
This analysis is inevitably limited to the nature of the data and their possible faults. It would be useful to collect more detailed information on the nature of the contacts of each new case of contagion and to analyze it, or even to carry out random surveys with Covid-19 test, in a word, to make the statistics.


