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jr vildmarks's avatar

Hi

In my view, you allow too little time for 2. shot disaster to appear. Link

https://metatron.substack.com/p/alberta-just-inadvertently-confessed

So you see that 50% of break-thru cases appear in 15 days after 1st shot. But the damage takes more time after 2nd shot, the months 5,6,7 are peaking as breakthru cases.

This guy has a snapshot two months earlier, on nov 4th

https://robertmoloney.substack.com/p/what-the-alberta-covid-19-dashboard

The situation after the first shot is the same, only younger ages have been added.

However, the second shot is still very much evolving, it really jumps within 2 months.

And most likely there is data in the making; 1st shots are done, but there are many people under 5 months afters 2nd shot.

Please note that this Alberta data captures the Delta vawe effect; omicron was a game changer (robert has some graphs on it).

Also, by eye you can see the correlations to hospitalizations and deaths...

JR

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henjin's avatar

How much variation did vaccines explain when you didn't allow each COVID wave to have a different term in your model?

Your 2015-2019 average baseline exaggerates excess deaths in 2021 and 2022 relative to 2020. Part of your variation explained by vaccines might actually be due to your inaccurate baseline, because your baseline produces superfluous excess deaths in 2021 and 2022 that happen to partially coincide with vaccination waves. And because your model has a different term for each COVID wave, it allows the weight of COVID waves in 2021 and 2022 to be reduced in order to accommodate a higher weight to vaccines.

In your first plot which shows excess deaths in the CDC dataset, there's no week where the excess mortality is even close to zero after the first few weeks of 2020. However at Mortality Watch if you plot ASMR with a 2010-2019 linear baseline, there's even a few weeks with negative excess mortality in March and April of 2022: https://www.mortality.watch/explorer/?c=USA&ct=weekly&df=2020%2520W01&bm=lin_reg.

You wrote that the CDC dataset had a total of 1,743,770 excess deaths in 2020-2022. When I downloaded the CDC dataset, I got the same result for MMWR weeks in 2020 and 2022 as a whole when I looked at the column "Number above average (unweighted)". I got 585,409 excess deaths on MMWR weeks in the year 2020, 670,667 in 2021, and 487,694 in 2022:

t=fread("AH_Excess_Deaths_by_Sex__Age__and_Race_and_Hispanic_Origin_20250211.csv")

t[Sex=="All Sexes"&RaceEthnicity=="All Race/Ethnicity Groups"&AgeGroup=="All Ages",sum(`Number above average (unweighted)`),MMWRyear]

However when I used my own more accurate method to calculate excess deaths where I multiplied the 2010-2019 linear trend in CMR for each age by the mid-year resident population estimates of the age, I got only about 1.27 million excess deaths in 2020-2022: sars2.net/rootclaim.html#Table_of_excess_deaths_by_cause. I got about 468,885 excess deaths in 2020, 515,125 in 2021, and 285,019 in 2022. So the CDC dataset had about 117,000 more excess deaths in 2020, 156,000 in 2021, and 203,000 in 2022, so the CDC dataset exaggerated excess deaths each year but it was particularly bad in 2022.

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