Marc Bevand
65 lines
2.9 KiB
Markdown
65 lines
2.9 KiB
Markdown
# Calculating the age-stratified infection fatality ratio (IFR) of COVID-19
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*Updated: 09 June 2020*
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Author: Marc Bevand
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The [largest serological prevalence survey][sero] of COVID-19 was conducted in
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Spain on 60 897 valid samples between 27 April and 11 May. We used its results
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to calculate the overall and age-stratified IFR of COVID-19 with the Python
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script `calc_ifr.py`:
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```
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$ ./calc_ifr.py
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Ages 0 to 9: 109803 infected, 3 deaths, 0.003% IFR
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Ages 10 to 19: 180401 infected, 7 deaths, 0.004% IFR
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Ages 20 to 29: 216507 infected, 33 deaths, 0.015% IFR
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Ages 30 to 39: 261550 infected, 87 deaths, 0.033% IFR
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Ages 40 to 49: 436122 infected, 281 deaths, 0.065% IFR
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Ages 50 to 59: 412847 infected, 864 deaths, 0.209% IFR
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Ages 60 to 69: 313907 infected, 2363 deaths, 0.753% IFR
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Ages 70 to 79: 256631 infected, 6470 deaths, 2.521% IFR
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Ages 80 to 89: 123416 infected, 10982 deaths, 8.898% IFR
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Ages 90 to 199: 33807 infected, 5654 deaths, 16.724% IFR
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Ages 0 to 199: 2344992 infected, 26744 deaths, 1.140% IFR
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```
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The average IFR for Spain is **1.140%**. However the true IFR may be higher due
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to right-censoring and under-reporting of deaths.
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The Spanish serological study was conducted between 27 April 2020 and 11 May 2020 and
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remains the largest published study available to this day. The age-stratified
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IFR was calculated from three sources:
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1. Detailed *prevalence data for age brackets*, from the [serosurvey][sero] (page 8)
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1. *Total deaths* and *deaths per age bracket* from the [Ministry of Health's daily report for 11 May][deaths] (page 1 and table 3)
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1. *Population pyramid* for Spain, from [worldpopulationreview.com][wpop]
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Important detail to note: there were 26 744 total deaths, however age information
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was only available for 18 722 deaths, and was missing for 8 022 deaths.
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We assume that these 8 022 deaths were distributed proportionally—not equally—among age
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brackets, which seems to be a reasonable assumption.
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# Applying the age-stratified IFR to other countries
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The script `calc_ifr.py` is also able to apply the age-stratified IFR to
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another population pyramid, thus calculating the expected average IFR for other
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countries.
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In the second half of the script, edit `pyramid_target` with the demographics data.
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As an example, we supply pyramid data for the United States and calculate an IFR of **0.721%**:
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```
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$ ./calc_ifr.py
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[...]
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IFR on target country assuming disease prevalence equal among ages: 0.721%
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```
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However IFR is highly dependent on factors other than age: availability
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of healthcare, population health, etc, so this estimate should be interpreted
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with caution.
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[sero]: https://www.mscbs.gob.es/gabinetePrensa/notaPrensa/pdf/13.05130520204528614.pdf
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[deaths]: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_102_COVID-19.pdf
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[wpop]: https://worldpopulationreview.com/countries/spain-population/
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