Category Archives: PYMC

A bit more serious attempt to estimate COVID Vaccine Efficacy

A few days ago, I did a quick & dirty Bayesian estimate on Covid Vaccine Efficacy, based on Israeli data, given in a Twitter post (see details on the data in the link above). As stated in the earlier post, … Continue reading

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Covid Sweden – status 2020-08-03

SCB.se just released their weekly preliminary stat’s on deaths all causes. However, before looking at that data, let’s take a look at how Covid has evolved in Sweden, based on the numbers from Johns Hopkins University: Something remarkable seems to … Continue reading

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Covid-19 Sweden: A summary

Sweden has been painted as the Black Sheep of the international community with respect to its ways to deal with the Covid-19 virus.  People and orginizations around the world were very quick to paint Sweden as the Sodom & Gomorra … Continue reading

Posted in Bayes, Data Analytics, Epidemics, MCMC, Pandas, Politik, Probability, PYMC, Python, Society, Statistics, Sverige | Tagged , , , , , , , , , , , | 4 Comments

Corona Sweden : Probability of dying per age group

Just for fun, hacked a simple Bayesian Model* to figure out how the probability of dying has changed for a few age groups Jan-Jun 2020, compared to the average of the same period 2015-2019.  (In case anyone wonders why on … Continue reading

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Corona – Latitude matters (!)

I was pointed towards looking into the possible impact of Latitude – yes, that’s right! – on Corona related deaths by Ivor Cummins, twitter handle @FatEmperor, who is very much involved in clearing up the pseudo-scientific mess the world has … Continue reading

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Corona Sweden : Infection Fatality Rate

It’s been a while ago since I ran my MCMC-hack to estimate the Swedish Infection Fatality Rate, main reason being that each run takes an awful lot of computing time, around 10h, to get the MCMC to converge… and during … Continue reading

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Corona Weekly Trends US & Sweden : Actual vs Expected deaths – now 4th consecutive week of decline in deaths

US: SWEDEN:

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Gender Bias – but not the way you’d think : Simpson’s Paradox strikes again!

I’ve previously touched upon Simpson’s Paradox and the (for statisticians!) famous example fromUniversity of California Berkeley, where it indeed looked like that the admission board favored men over women: while 44% of male applicants got admitted, only 35% of the … Continue reading

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Fooled by Averages and Ignorant of Uncertainty – Bayesian Inference to assistance

As a follow up to my previous posts [1,2,3] on the danger’s of relying upon averages, and Simpson’s paradox, which is a consequence of misused averaging, here’s yet another angle on the same topic. Let’s first resume with the baseball … Continue reading

Posted in Bayes, Data Analytics, Gambling, Pandas, Probability, PYMC, Python, Statistics | Tagged , , , , , , | 3 Comments

Corona Weekly Trends US & Sweden : Actual vs Expected deaths – Sweden now third consecutive week of decline in deaths

Both US and Sweden continue demonstrating consistent decrease of weekly number of deaths. However, for Sweden, the weekly number of confirmed increased slightly, from 3745 to 3942 during the past 7-day period, which comparing to other countries with an overall … Continue reading

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