Marc Bevand
92 lines
3.6 KiB
Python
Executable File
92 lines
3.6 KiB
Python
Executable File
#!/usr/bin/python3
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#
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# Calculate the age-stratified IFR based on the second round of the Spanish
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# serosurvey of 63564 participants.
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# Author: Marc Bevand — @zorinaq
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# Prevalence of antibodies by age bracket, in % (serosurvey dates: 18-May-2020 to 01-June-2020)
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# Source: https://portalcne.isciii.es/enecovid19/ene_covid19_inf_pre2.pdf (table 1)
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prevalence_by_age = {
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(0,0): 2.2,
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(1,4): 2.4,
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(5,9): 2.9,
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(10,14): 3.8,
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(15,19): 3.8,
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(20,24): 4.2,
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(25,29): 4.9,
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(30,34): 4.4,
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(35,39): 4.7,
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(40,44): 5.4,
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(45,49): 5.9,
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(50,54): 6.1,
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(55,59): 5.7,
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(60,64): 6.3,
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(65,69): 6.6,
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(70,74): 7.3,
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(75,79): 6.4,
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(80,84): 5.1,
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(85,89): 6.4,
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(90,199): 8.0,
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}
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# Total deaths, and number of deaths by age bracket (as of 29-May-2020)
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# Source: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_120_COVID-19.pdf (table 2 and table 3)
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# Total deaths (27121) differs from the total for all age brackets (20585)
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# because age information is not available for 6536 deaths
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total_deaths = 27121
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deaths_by_age = {
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(0,9): 3,
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(10,19): 5,
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(20,29): 24,
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(30,39): 65,
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(40,49): 218,
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(50,59): 663,
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(60,69): 1825,
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(70,79): 4896,
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(80,89): 8463,
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(90,199): 4423,
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}
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deaths_by_age[(0,199)] = total_brackets = sum(deaths_by_age.values()) # 20585
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# To properly calculate the IFR, we need to account for the extra 6536 deaths
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# for which age information was not available, so we simply assume they are
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# distributed proportionally (not equally) among age brackets
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for bracket in deaths_by_age:
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deaths_by_age[bracket] *= (total_deaths / total_brackets)
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# Population pyramid for Spain (age 0 to 100)
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# Source: https://worldpopulationreview.com/countries/spain-population/
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# Hack to extract the raw data: https://twitter.com/zorinaq/status/1265380966450622464
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# pyramid_spain[N] = number of people of age N
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pyramid_spain = [
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389071,395760,404555,414953,411842,432086,450617,467032,480928,493573,
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506233,510163,501625,485224,469003,450996,438622,436275,440530,443668,
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447359,450865,453090,454935,458810,464718,470848,476697,483276,491535,
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500604,515444,538403,566972,594959,622001,652353,686993,723042,757510,
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792033,815052,820836,814644,807993,799212,788640,777807,766575,752713,
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736006,722714,715523,711721,706221,700412,690511,674241,653635,633659,
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614107,592701,569064,544537,519985,494201,475071,466281,463940,460575,
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457809,451462,438746,421694,405814,390815,372987,351294,327555,303574,
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277747,258748,250683,249083,246213,244626,235612,214376,185512,155765,
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131040,113392,91852,66359,48324,40084,32862,24229,14184,8251,
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12310]
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def get_infected(bracket):
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'''Returns number of infected people in the given age bracket.'''
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i = 0
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for age in range(bracket[0], bracket[1] + 1):
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for (bracket2, percentage) in prevalence_by_age.items():
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if age >= bracket2[0] and age <= bracket2[1] and age < len(pyramid_spain):
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i += pyramid_spain[age] * percentage / 100.0
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return i
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ifrs = {}
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for (bracket, deaths) in deaths_by_age.items():
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infected = get_infected(bracket)
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ifr = 100.0 * deaths / infected
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print('Ages {:2} to {:3}: {:7} infected, {:5} deaths, {:6.3f}% IFR'.format(
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bracket[0], bracket[1], round(infected), round(deaths), ifr))
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if bracket != (0,199):
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ifrs[bracket] = ifr
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print('True IFR may be higher due to right-censoring and under-reporting of deaths')
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