In estimating national infection rates, I’ve been using death rates as a lagging indicator, adjusted based on the median age of the national population. Compiling available published evidence, I’ve estimated that the covid fatality rate for the US is 0.65%. The median age of the US population is 38 years. Based on several seroprevalence studies conducted in the US and Europe, I’ve been age-adjusting other countries’ estimated fatality rates according to this formula:

- For countries with a median age
*higher*than the US,*multiply*0.65% by 1.1 raised to the Nth power, where N is the number of years that the country’s median age is*above*38 years. - For countries with a median age
*lower*than the US,*divide*0.65% by 1.1 raised to the Nth power, where N is the number of years that the country’s median age is*below*38 years.

This age adjustment factor works well enough within the relatively narrow median age range of economically developed countries. However, many countries in Africa have much younger populations; e.g., the median age is 20 in both Ethiopia and Kenya.

Using the CDC’s demographic data on covid-related deaths, it’s possible to calculate more accurate age adjusters for fatality rates. A couple of simplifying assumptions:

- In the US, each ten-year range of ages from 15 to 55 years accounts for roughly equal proportions of the population.
- In the US, covid infection rates are approximately equal for each ten-year range of ages from 15 to 55 years.

Using the CDC data, here are the age-adjusted fatality rate “power multipliers” for each ten-year age interval:

- 15 – 24 years = 1.25
- 25 – 34 years = 1.1
- 35 – 44 years = 1.1
- 45 – 54 years = 1.1

So the geometric increase in fatality rate by age is 1.1 per year across the 30-year range from age 25 through 54 — consistent with my algorithm. In the 15 to 24 year age range, however, the rate of increase is faster. Therefore I need to adjust my power multiplier accordingly:

- For countries with a median age
*lower*than the US,*divide*0.65% by 1.1 raised to the Nth power, where N is the number of years that the country’s median age is*between*38 years and*25 years. If the country’s median age is lower than 25 years, (a) divide 0.65% by 1.1*^{(38-25)=13}or 3.45 –>*0.19%, then (b) further divide that number by 1.25 raised to the Yth power, where Y is the number of years that the country’s median age is below 25 years.*

Based on this revision, the age-adjusted covid fatality rate for Ethiopia and Kenya, with median population ages of 20 years, would be 0.19/1.25^{5 }= 0.06%. That’s less than one-tenth the covid fatality rate of the US, and half the fatality rate calculated using the original simplified power multiplier.

Dude.

You amaze me. Often.

Using math to understand the world. Well done.

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Thanks, mah dude. Shifting focus closer to home than Ethiopia, it turns out the 1.1 annual factor multiplier for fatality rate holds steady all the way up to age 75. Implications: per the algo, the 63-year-old demographic tranche has a covid fatality rate of 0.65 x 1.1 to the (63-38) or 25th power = 7%, which is more than ten times the US national average. The 69-year-old tranche = 7% + 1.1 to the (69-63) or 6th power = 12.4%, nearly 20 times the national average. Hunker in the bunker; get the shots.

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