red state – blue state – vaccinated state – unvaccinated state

The New York Times published an article demonstrating the partisan separation between US Democrats and Republicans by regression lines. As the one above, regressing the proportion of vaccinated on the proportion of Trump voters but no scale on the first axis. But no correction for age composition or population density. And the one below, plotted at the county level, which seems quite meaningless given the spread of red dots in Wyoming.

Still, there is a clear opposition between places (counties) that voted more than 70% Trump (representing 33M people) and those that voted more than 70% Biden (more than 58M people), even though county density, age composition, and earlier deaths from COVID should also be accounted for. But the vaccination rate also exhibits this opposition, with a 1.65 ratio between the first and last decile of the blue counties.

a meaningful divide?

Le Monde published this map in its 26 July edition, to illustrate the contrast between South-East and North and West France(s). Meaning that the North-West upper part is more vaccinated than the South-East lower part of the map. The figure being computed as the sum of the differences between local and national rates, per age group, weighted by the group sizes. The paper goes on analysing the divide in terms of sociology of the territories, as well as political opposition to Président Macron… But I wonder (over breakfast) if it does not see too much in this picture. First some districts have to be either above or below the national average. Second, the map does not incorporate the population density: very sparsely populated districts in the South-East, like Auvergne or central Corsica are more visible than the densest areas like the Greater Paris, while being more prone to low vaccination rates due to the larger distance to vaccination centres. Third, most of the districts are within ±15% of the average, which may be too large for statistical variation but not much! The geographer Emmanuel Vigneron points out in the paper an inverse correlation between vaccination and earlier COVID cases, but this is not so surprising in that people who have already been exposed to the virus may conclude they are well (enough) protected. Further, the age effect is not eliminated by the contrast, in that areas with an older population are bound to get closer to the average, given that vaccination in the older groups started earlier and was more seen as a life-or-death issue. The soundest observation is rather in the opposition between urban districts where, despite an equivalent access to vaccination opportunities, the poorer burbs like the Northern districts of Marseille being the least vaccinated (with possibly an age effect?).

COVID-19, the musical

My friend Anto has been instrumental in creating this musical about COVID-19 vaccine, now translated into English

from the Italian original by by Lorenzo Baglioni

on Astra and clots

A tribune this morning in The Guardian by David Spiegelhalter on having no evidence that the Oxford/AstraZeneca vaccine causes blood clots.

“It’s a common human tendency to attribute a causal effect between different events, even when there isn’t one present: we wash the car and the next day a bird relieves itself all over the bonnet. Typical.”

David sets the 30 throboembolic events among the 5 million people vaccinated with AstraZeneca in perpective of the expected 100 deep vein thromboses a week within such a population. Which coincides with the UK’s Medicines and Healthcare Products Regulatory Agency statement that the blood clots are in par with the expected numbers in the vaccinated population. (The part of the tribune about the yellow card reports, based on 10 million vaccinated people, reiterates the remark but may prove confusing to some!) As for hoping for a rational approach to the issue,  … we would need a different type of vaccine, far from being available! As demonstrated by the decision to temporarily stop vaccinating with this vaccine, causing sure additional deaths in the coming weeks.

“Will we ever be able to resist the urge to find causal relationships between different events? One way of doing this would be promoting the scientific method and ensuring everyone understands this basic principle. Testing a hypothesis helps us see which hunches or assumptions are correct and which aren’t. In this way, randomised trials have proved the effectiveness of some Covid treatments and saved vast numbers of lives, while also showing us that some overblown claims about treatments for Covid-19, such as hydroxychloroquine and convalescent plasma, were incorrect.”

probability that a vaccinated person is shielded from COVID-19?

Over my flight to Montpellier last week, I read an arXival on a Bayesian analysis of the vaccine efficiency. Whose full title is “What is the probability that a vaccinated person is shielded from Covid-19? A Bayesian MCMC based reanalysis of published data with emphasis on what should be reported as `efficacy'”, by Giulio D’Agostini and Alfredo Esposito. In short I was not particularly impressed.

“But the real point we wish to highlight, given the spread of distributions, is that we do not have enough data for drawing sound conclusion.”

The reason for this lack of enthusiasm on my side is that, while the authors’ criticism of an excessive precision in Pfizer, Moderna, or AstraZeneca press releases is appropriate, given the published confidence intervals are not claiming the same precision, a Bayesian reanalysis of the published outcome of their respective vaccine trial outcomes does not show much, simply because there is awfully little data, essentially two to four Binomial-like outcomes. Without further data, the modelling is one of a simple graph of Binomial observations, with two or three probability parameters, which results in a very standard Bayesian analysis that does depend on the modelling choices being made, from a highly unrealistic assumption of homogeneity throughout the population(s) tested for the vaccine(s), to a lack of hyperparameters that could have been shared between vaccinated populations. Parts of the arXival are unrelated and unnecessary, like the highly detailed MCMC algorithm for simulating the posterior (incl. JAGS code) to the reminiscence of Bayes’ and Laplace’s early rendering of inverse probability. (I find both interesting and revealing that arXiv, just like medRxiv, posts a warning on top of COVID related preprints.)