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Philippe Lemoine Profile picture
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16 Apr, 6 tweets, 2 min read
Sauf qu'il n'y a aucune raison de penser que E(années de vie restantes | age = x & sexe = y & victime du COVID-19) est égal à E(années de vie restantes | age = x & sexe = y) et qu'il est même parfaitement évident que ce n'est pas le cas 🤷‍♂️
À n'importe quel âge, l'immense majorité des gens qui sont infectés par SARS-CoV-2 survivent, donc ceux qui en meurent sont vraisemblablement plus fragiles que la moyenne des gens du même age et du même sexe et auraient sans doute vécu moins longtemps.
Ce tableau est donc trompeur dans le contexte du débat sur le nombre d'années de vie perdues par les victimes du COVID-19. Bref, avant de faire le mariole et de donner des leçons de démographie aux autres, mieux vaut réfléchir un peu et s'assurer qu'on ne dit pas de connerie...
Effectivement, je n'y crois pas un instant et je ne défends pas Raoult, mais le fait qu'il ait dit une connerie n'est pas une raison pour lui répondre avec une autre connerie, surtout quand on se pose en donneur de leçon.
Cela dit, même si je suis sûr que ce que dit Raoult est faux, je pense que c'est beaucoup moins faux que la plupart des gens le pensent car les rares personnes jeunes qui sont mortes du COVID-19 ont un impact disproportionné sur le nombre total d'années de vie perdues.
Je caricature pour illustrer, mais un individu jeune mort du COVID-19 alors qu'il lui restait 50 ans de vie à vivre a le même impact sur le nombre total d'années de vie perdues que 50 personnes âgées à qui il ne restait qu'un an de vie à vivre, donc c'est très déséquilibré.

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More from @phl43

Philippe Lemoine Profile picture
16 Apr
Not only did your study show no such thing, but it rests on demonstrably false assumptions, so it's really extraordinary that you continue to peddle those results. Here is a thread in which I explain why this study is worthless and should never have been used to guide policy 🧵
First, the model used in that study assumes that B.1.1.7, the UK variant, is 59% more transmissible than the historical lineage. This estimate is based from Gaymard et al. (2021), which obtained it by fitting a simple exponential growth model to only 2 data points from January.
As I explained at length in this post, even if we just use those 2 data points from January, this estimate is highly sensitive to the assumptions we make about the distribution of the generation time and there is a lot of uncertainty about that. cspicenter.org/blog/waronscie…
Read 16 tweets
Philippe Lemoine Profile picture
14 Apr
Des nouvelles de B.1.1.7, le « variant anglais » qui était censé provoquer un tsunami en raison de sa transmissibilité accrue, à partir des dernières données de Santé publique France 😂 Image
Même chose mais quand on fait la comparaison uniquement avec la souche historique plutôt qu’avec tous les variants non-B.1.1.7. En gros, la première méthode est sans doute un peu biaisée, tandis que celle-ci ne l’est pas mais l’erreur de mesure est plus grande. Image
Je rappelle que les génies de l’Inserm et de l’Institut Pasteur continuent de faire l’hypothèse qu’il est 50% à 70% plus transmissible dans les modèles qu’ils utilisent pour faire les projections qu’ils présentent au gouvernement 👌
Read 5 tweets
Philippe Lemoine Profile picture
13 Apr
Top 3 things I've been wrong about during the pandemic:

1) That lockdowns were a good policy
2) Relatedly, that most of the uncertainty early on was about the IFR, as opposed to how to model the spread
3) That a vaccine wouldn't be approved until mid-2021 at the earliest
I should actually have put number 2 in first position, because it's the reason I was wrong on lockdowns. I thought there was a chance the IFR was significantly lower than 1% because of Japan, which I assumed was swimming in virus, yet still had very few COVID-19 deaths by March.
That's because I assumed a SIR model with constant contact rate was a good representation of transmission in the absence of strong government interventions, but in fact it's not and the explanation for Japan's low COVID-19 mortality was just that the virus had not spread much.
Read 5 tweets
Philippe Lemoine Profile picture
13 Apr
61% of people tested negative for antibodies, but instead of concluding that most people in that sample who claimed to suffer from "long COVID" are just hypochondriacs who were never even infected, the conclusion is that "long COVID" patients have a weaker antibody response 😂
But the problem is not that medical researchers make that ridiculous inference with a straight face, not at all, it's that a journal asked them to exclude people who tested negative, which apparently is racist because most of them were minorities 🙃
It's amazing, just have a look at how many doctors and scientists liked/retweeted this garbage, medical research is rotten to the core. The mere fact that a prominent researcher can say that with a straight face and apparently no fear of damaging her reputation speaks volumes.
Read 5 tweets
Philippe Lemoine Profile picture
11 Apr
People are assuming it would have been different with 2 doses, which might be true, but it's hardly obvious. There was an outbreak in my grandmother LTCF after every resident had received 2 doses and according to the doctor my father talked to this is not uncommon.
As I said in January, I think there are good reasons to think the most vulnerable people were undersampled in the trials, so unfortunately I suspect we're going to find out that vaccines are less effective on this group, even after 2 doses.
If this turns out to be true, it's very important that public health officials and the media don't lie about it to maintain trust in vaccines, because I think it will inevitably backfire and make things even worse. Unfortunately it's probably what they'll do though.
Read 5 tweets
Philippe Lemoine Profile picture
9 Apr
Epidemiologists claim that B.1.1.7 is far more transmissible than the historical lineage. In this post, I look at what happened in France and argue that, while B.1.1.7 initially had a large transmissibility advantage, it's been going down rapidly. (THREAD) cspicenter.org/blog/waronscie…
Various studies have estimated B.1.1.7's transmissibility advantage, but I focus on Gaymard et al. (2021), which found that it was between 50% and 70% more transmissible and has been used to calibrate official projections in France. eurosurveillance.org/content/10.280…
This estimate is based on fitting a simple exponential growth model to only 2 data points from January, during the early expansion of B.1.1.7 in France. This is problematic for several reasons and I won't get to the worst of them until the end of the thread, so stick with me.
Read 61 tweets

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