The line representing average expected mortality on the chart is a LOWESS regression
Normally demographers use more sophisticated statistical algorithm (eg. Farrington) to do so. LOWESS is kind of a sloppy technique, but it works well enough
2/n
For more accurate results, I didn't include 2020 data in the LOWESS regression. Instead I cut off the smoothing at 2019, and assume that without COVID-19 the expected mortality would have continued its generally improving trend of the last decades through 2020
3/n
Excess deaths/deaths deficit (bottom, in blue) are the difference between the expected and observed mortality (top, in red) multiplied by the population of that year.
) with one notable difference: I analyze 𝘆𝗲𝗮𝗿𝗹𝘆 mortality (01 Jan to 31 Dec) not 𝗺𝗼𝗻𝘁𝗵𝗹𝘆
This better reflects the true impact of COVID-19
5/n
Indeed some flu seasons might have 1-2 months with high mortality, appearing as high peaks on @VoidSurf1's chart
But through the whole year, COVID-19 causes far more deaths than a bad flu season, because excess deaths are spread out through at least 3 months (March-June)
6/n
And the year isn't done. You might have seen my forecast of Sweden surpassing 100 deaths/day before Christmas, hence surpassing the first wave:
It's also worth pointing out that COVID-19 causing the most excess deaths in a year since the 1918 influenza pandemic is doubly more impressive, when one realizes that <10% of Swedes were infected by November
• family isolation
• ban public events of more than 8
• cinemas/museums/gyms closed
• nightlife curbed (alcohol ban)
• Tegnell: yes to face masks
• nursing home visit ban
• + many restrictions
I dusted off my COVID-19 model (that predicted the Florida July wave) & applied it to Sweden
After today's data update from the Swedish Public Health Agency (FHM) I confidently forecast Sweden will surpass the peak of 100 COVID deaths/day they had in April
Hard to believe?
1/n
Specifically: by 25 December we will see Sweden has recorded 100 deaths/day around 11 December
(due to reporting delays, it takes up to 2 weeks past a given date to have a complete count of deaths on this date:
͏@VoidSurf1 wrote a cool thread on Sweden excess deaths over the last few centuries. At first sight, his analysis seems correct... But there is a fatal flaw.
1/n
COVID deaths & hospitalizations always lag cases. The lag has been demonstrated, is often 𝗺𝗼𝗿𝗲 than a month, and its timing can be predicted accurately (I have done it.) The #casedemic folks are just, well, wrong.
A thread explaining the lag with real-world examples.
1/n
I will show what causes the lag, and how I can predict it accurately. First, there are multiple causes behind it:
It applies various age-stratified IFR estimates to calculate the expected overall IFR in a given country. It's based on demographics (countries population pyramids): github.com/mbevand/covid1…
Many interesting findings—read on
1/n
First off, I use five different sources estimating the age-stratified Infection Fatality Ratio of COVID-19:
1. ENE-COVID 2. US CDC 3. Verity et al. 4. Levin et al. 5. Gudbjartsson et al.
If you know of more sources, let me know and I'll add them to my script
2/n
So, what do we find?
The overall IFR estimates, with the exception of Levin et al., are relatively consistent with each other, usually within 30-40%. Levin et al. is up to 2-fold higher than the others, depending on the country.
3/n