dedicated script to apply IFR to population pyramids
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Marc Bevand
Executable
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#!/usr/bin/python3
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#
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# Apply various estimates of the age-stratified Infection Fatality Ratio of COVID-19 to
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# countries' population pyramids in order to calculate their overall IFR.
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# Author: Marc Bevand — @zorinaq
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import pandas as pd
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# Pyramid data is from the United Nations: this file is a CSV export of the first sheet
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# of "Population by Age Groups - Both Sexes" linked from:
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# https://population.un.org/wpp/Download/Standard/Population/
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# Direct link:
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# https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx
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file_pyramids = 'WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv'
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maxage = 100
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# Age groups defined in the CSV file
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age_groups = [(0,4), (5,9), (10,14), (15,19), (20,24), (25,29), (30,34), (35,39), (40,44), (45,49), (50,54), (55,59), (60,64), (65,69), (70,74), (75,79), (80,84), (85,89), (90,94), (95,99), (100,maxage)]
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# This will hold parsed pyramid data. Example to get the number of people in the
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# age group 20-24 in France: pyramid['France'][(20,24)]
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pyramid = {}
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# Various age-stratified IFR estimates
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ifrs = [
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# Calculated from Spanish ENE-COVID study
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# (see calc_ifr.py)
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('ENE-COV', {
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(0,9): 0.003,
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(10,19): 0.004,
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(20,29): 0.015,
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(30,39): 0.030,
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(40,49): 0.064,
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(50,59): 0.213,
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(60,69): 0.718,
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(70,79): 2.384,
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(80,89): 8.466,
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(90,maxage): 12.497,
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}),
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# US CDC estimate as of 10 Sep 2020
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# https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
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# (table 1)
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('US_CDC', {
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(0,19): 0.003,
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(20,49): 0.02,
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(50,69): 0.5,
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(70,maxage): 5.4,
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}),
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# Verity et al.
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# https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext
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# (table 1)
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('Verity', {
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(0,9): 0.00161,
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(10,19): 0.00695,
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(20,29): 0.0309,
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(30,39): 0.0844,
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(40,49): 0.161,
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(50,59): 0.595,
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(60,69): 1.93,
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(70,79): 4.28,
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(80,maxage): 7.80,
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}),
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# Levin et al.
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# https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v5
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# (table 3)
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('Levin', {
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(0,34): 0.004,
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(35,44): 0.06,
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(45,54): 0.2,
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(55,64): 0.7,
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(65,74): 2.3,
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(75,84): 7.6,
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(85,maxage): 22.3,
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}),
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# Humoral Immune Response to SARS-CoV-2 in Iceland
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# https://www.nejm.org/doi/full/10.1056/NEJMoa2026116
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# Supplementary Appendix 1
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# https://www.nejm.org/doi/suppl/10.1056/NEJMoa2026116/suppl_file/nejmoa2026116_appendix_1.pdf
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# (table S7)
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('Iceland', {
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(0,70): 0.1,
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(71,80): 2.4,
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(81,maxage): 11.2,
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}),
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]
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def ag2str(age_group):
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if age_group[1] == maxage:
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return f'{age_group[0]}+'
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return f'{age_group[0]}-{age_group[1]}'
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def parse_pyramids():
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df = pd.read_csv(file_pyramids)
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# ignore labels as they don't contain any data
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df = df[df['Type'] != 'Label/Separator']
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# only take rows with data as of 2020
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df = df[df['Reference date (as of 1 July)'] == 2020]
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# only parse countries, world, and continents
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df = df[df['Type'].isin(('Country/Area', 'World', 'Region'))]
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# remove spaces used as thousands separators, and convert cell values to floats
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columns = [ag2str(x) for x in age_groups]
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for col in columns:
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df[col] = df[col].str.replace('\s+', '').astype(float)
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regions = list(df['Region, subregion, country or area *'])
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#regions = ('France',)
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for region in regions:
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pyramid[region] = {}
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df_region = df[df['Region, subregion, country or area *'] == region]
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for ag in age_groups:
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# values are in thousands
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pyramid[region][ag] = 1000 * float(df_region[ag2str(ag)])
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def people_of_age(pyramid_region, age):
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# Returns the number of people of exact age 'age', given the provided age pyramid
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for ((a, b), n) in pyramid_region.items():
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if age in range(a, b + 1):
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return n / float(b - a + 1)
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def overall_ifr(pyramid_region, ifr_age_stratified):
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pop = 0
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deaths = 0
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for (age_group, ifr) in ifr_age_stratified.items():
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for age in range(age_group[0], age_group[1] + 1):
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pop += people_of_age(pyramid_region, age)
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deaths += people_of_age(pyramid_region, age) * ifr / 100.0
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assert pop == sum(pyramid_region.values())
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return 100.0 * deaths / pop
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def calc_overall_ifrs():
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oifrs = []
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for region in pyramid.keys():
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# The overall IFRs are appended to the array in the same order as listed in ifrs
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tmp = []
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for i in ifrs:
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ifr_age_stratified = i[1]
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# calculate the overall IFR for region, using IFR estimate ifr_age_stratified
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tmp.append(overall_ifr(pyramid[region], ifr_age_stratified))
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oifrs.append((region, *tmp))
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return oifrs
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def show_overall_ifrs(oifrs):
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def header():
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for i in ifrs:
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print(f'{i[0]:>7} ', end='')
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print('')
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# Each entry in the oifrs array is a tuple:
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# (<region_name>, <ifr_according_to_1st_estimate>, <ifr_according_to_2nd_estimate>, ...)
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# Sort by element index 1, that is by <ifr_according_to_1st_estimate>
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# To sort by region name, use index 0 (x[0])
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oifrs.sort(key=lambda x: x[1])
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header()
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for region in oifrs:
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for i in region[1:]:
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print(f'{i:7.3f} ', end='')
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print(f' {region[0]}')
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header()
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def main():
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parse_pyramids()
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oifrs = calc_overall_ifrs()
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show_overall_ifrs(oifrs)
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if __name__ == '__main__':
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main()
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