apply estimates of the age-stratified infection fatality ratio (IFR) of COVID-19 to countries' population pyramids, to find their expected overall IFR
calculate the age-stratified IFR from the Spanish ENE-COVID serosurvey

Age-stratified IFR applied to countries' population pyramids

The script apply_ifr.py uses a handful of age-stratified IFR estimates and applies them to countries' population pyramids, to find their expected overall IFR assuming equal prevalence of the disease among all age groups.

Of course, the real-world overall IFR will dependent on many factors: varying prevalence among age groups, underlying health conditions, access to healthcare, socioeconomic status, ethnicity, etc.

IFR estimates come from:

ENE-COVID Spanish serosurvey (calculated by calc_ifr.py, see next section)
US CDC (table 1)
Verity et al.: Estimates of the severity of coronavirus disease 2019: a model-based analysis (table 1)
Levin et al.: Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications (table 3)
Gudbjartsson et al.: Humoral Immune Response to SARS-CoV-2 in Iceland, Supplementary Appendix 1 (table S7)

Data for the population pyramids comes from the United Nations, specifically the first sheet of Population by Age Groups - Both Sexes. This excel file was converted to CSV format: WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv

Results

The overall expected IFR percentages are summarized in this table (sorted by IFR according to ENE-COV column):

ENE-COV	US_CDC	Verity	Levin	Gudbj	Region
1.274	1.311	1.605	2.247	1.283	Japan
1.065	1.092	1.382	1.847	1.068	Italy
1.041	1.043	1.339	1.806	1.057	Greece
0.993	1.012	1.305	1.702	0.994	Germany
0.984	1.045	1.320	1.726	0.986	Portugal
0.931	0.985	1.270	1.648	0.918	Martinique
0.919	0.933	1.221	1.658	0.918	Lithuania
0.916	0.942	1.207	1.648	0.921	Spain
0.914	0.941	1.201	1.606	0.913	France
0.899	1.008	1.248	1.556	0.883	Finland
0.881	0.919	1.192	1.624	0.870	Latvia
0.875	0.957	1.205	1.528	0.857	Puerto Rico
0.868	0.892	1.171	1.516	0.870	Estonia
0.865	0.932	1.209	1.487	0.855	Croatia
0.855	0.965	1.205	1.471	0.835	Malta
0.846	0.881	1.137	1.478	0.847	Belgium
0.843	0.905	1.181	1.489	0.829	Slovenia
0.840	0.943	1.154	1.471	0.838	Sweden
0.836	0.910	1.149	1.462	0.830	Austria
0.825	0.897	1.132	1.447	0.819	Switzerland
0.810	0.861	1.117	1.396	0.802	Europe
0.802	0.909	1.141	1.394	0.785	Netherlands
0.801	0.949	1.181	1.377	0.779	Bulgaria
0.798	0.864	1.113	1.399	0.779	Guadeloupe
0.797	0.935	1.136	1.370	0.782	Denmark
0.794	0.868	1.089	1.384	0.794	United Kingdom
0.791	0.811	1.102	1.367	0.761	China, Hong Kong SAR
0.759	0.830	1.090	1.285	0.745	Romania
0.753	0.854	1.101	1.323	0.732	Hungary
0.745	0.800	1.064	1.285	0.724	Poland
0.739	0.828	1.059	1.315	0.737	Channel Islands
0.738	0.878	1.092	1.302	0.717	Czechia
0.734	0.810	1.047	1.288	0.714	Canada
0.719	0.756	0.998	1.259	0.705	Barbados
0.713	0.808	1.038	1.210	0.694	Curaçao
0.709	0.802	1.007	1.239	0.699	Norway
0.680	0.800	1.029	1.160	0.659	Serbia
0.676	0.694	0.904	1.193	0.686	Uruguay
0.675	0.745	0.967	1.181	0.660	Northern America
0.671	0.736	0.944	1.174	0.670	Australia
0.671	0.733	0.983	1.103	0.651	Ukraine
0.669	0.738	0.958	1.169	0.654	United States of America
0.659	0.757	1.011	1.088	0.620	Bosnia and Herzegovina
0.654	0.742	0.945	1.137	0.645	New Zealand
0.644	0.735	0.941	1.131	0.630	Cuba
0.641	0.877	1.059	1.152	0.608	United States Virgin Islands
0.633	0.725	0.963	1.090	0.606	Republic of Korea
0.628	0.686	0.938	1.100	0.599	China, Taiwan Province of China
0.624	0.663	0.916	1.030	0.606	Russian Federation
0.624	0.657	0.921	1.082	0.606	Belarus
0.615	0.668	0.881	1.078	0.616	Luxembourg
0.607	0.670	0.896	1.002	0.598	Georgia
0.605	0.683	0.888	1.061	0.599	Iceland
0.604	0.709	0.935	1.059	0.580	Slovakia
0.582	0.667	0.891	0.989	0.567	Montenegro
0.563	0.653	0.838	0.972	0.559	Ireland
0.561	0.649	0.839	0.957	0.559	Cyprus
0.548	0.659	0.857	0.941	0.533	Albania
0.531	0.652	0.874	0.920	0.508	Aruba
0.518	0.580	0.754	0.902	0.526	Oceania
0.510	0.588	0.795	0.881	0.491	Thailand
0.500	0.569	0.763	0.880	0.493	Réunion
0.499	0.545	0.710	0.872	0.516	Israel
0.497	0.522	0.748	0.794	0.491	Armenia
0.497	0.616	0.816	0.851	0.474	North Macedonia
0.493	0.550	0.736	0.861	0.492	Chile
0.479	0.550	0.796	0.829	0.433	Singapore
0.454	0.515	0.671	0.790	0.471	Argentina
0.449	0.514	0.738	0.770	0.412	China, Macao SAR
0.447	0.544	0.732	0.772	0.425	Mauritius
0.435	0.501	0.721	0.766	0.413	Republic of Moldova
0.415	0.511	0.681	0.717	0.402	Trinidad and Tobago
0.413	0.475	0.638	0.712	0.417	Costa Rica
0.413	0.468	0.626	0.704	0.413	Saint Lucia
0.409	0.507	0.695	0.707	0.385	China
0.394	0.497	0.648	0.712	0.409	Antigua and Barbuda
0.387	0.495	0.651	0.668	0.377	Sri Lanka
0.377	0.487	0.635	0.622	0.373	Dem. People's Republic of Korea
0.376	0.437	0.594	0.645	0.382	Brazil
0.365	0.464	0.611	0.618	0.373	Guam
0.364	0.425	0.569	0.627	0.381	Jamaica
0.361	0.454	0.605	0.663	0.375	Saint Vincent and the Grenadines
0.359	0.399	0.540	0.623	0.380	Panama
0.358	0.418	0.566	0.615	0.370	WORLD
0.356	0.410	0.555	0.614	0.371	Latin America and the Caribbean
0.355	0.412	0.560	0.613	0.367	Colombia
0.354	0.438	0.595	0.642	0.368	Grenada
0.347	0.399	0.528	0.595	0.377	El Salvador
0.343	0.363	0.528	0.602	0.355	Viet Nam
0.342	0.408	0.552	0.588	0.356	Turkey
0.339	0.397	0.537	0.583	0.358	Peru
0.336	0.394	0.553	0.579	0.344	Tunisia
0.327	0.432	0.573	0.547	0.328	New Caledonia
0.325	0.394	0.541	0.559	0.331	Asia
0.310	0.350	0.463	0.535	0.346	Bolivia (Plurinational State of)
0.309	0.348	0.480	0.532	0.333	Dominican Republic
0.307	0.348	0.503	0.505	0.319	Kazakhstan
0.306	0.352	0.485	0.532	0.327	Mexico
0.305	0.365	0.502	0.526	0.317	Venezuela (Bolivarian Republic of)
0.303	0.347	0.474	0.525	0.329	Ecuador
0.295	0.346	0.482	0.501	0.317	Lebanon
0.293	0.400	0.542	0.477	0.284	Seychelles
0.287	0.384	0.527	0.513	0.286	French Polynesia
0.282	0.326	0.456	0.456	0.298	Guyana
0.280	0.335	0.463	0.469	0.298	Suriname
0.273	0.313	0.474	0.452	0.279	Azerbaijan
0.272	0.337	0.474	0.462	0.284	Morocco
0.269	0.358	0.495	0.460	0.272	Bahamas
0.263	0.325	0.453	0.442	0.279	Malaysia
0.260	0.305	0.426	0.446	0.289	Algeria
0.257	0.307	0.418	0.436	0.287	Paraguay
0.251	0.297	0.403	0.423	0.288	Bhutan
0.246	0.301	0.431	0.418	0.263	Iran (Islamic Republic of)
0.234	0.293	0.414	0.398	0.253	India
0.229	0.293	0.416	0.387	0.244	Indonesia
0.228	0.259	0.373	0.393	0.262	Nicaragua
0.221	0.269	0.368	0.374	0.255	Bangladesh
0.221	0.232	0.349	0.374	0.260	Cabo Verde
0.217	0.284	0.375	0.310	0.247	Tonga
0.215	0.277	0.401	0.366	0.228	Myanmar
0.208	0.235	0.334	0.351	0.244	Belize
0.206	0.235	0.331	0.353	0.249	Honduras
0.204	0.233	0.323	0.349	0.253	Guatemala
0.204	0.255	0.360	0.342	0.232	Philippines
0.202	0.267	0.360	0.344	0.232	Nepal
0.197	0.249	0.381	0.331	0.210	Brunei Darussalam
0.194	0.237	0.331	0.330	0.233	Haiti
0.193	0.251	0.355	0.325	0.220	South Africa
0.193	0.222	0.339	0.316	0.220	Turkmenistan
0.192	0.247	0.343	0.321	0.224	Egypt
0.192	0.262	0.376	0.318	0.206	Fiji
0.188	0.220	0.335	0.310	0.217	Kyrgyzstan
0.188	0.245	0.353	0.344	0.216	French Guiana
0.187	0.219	0.340	0.317	0.212	Uzbekistan
0.185	0.224	0.323	0.317	0.222	Syrian Arab Republic
0.182	0.226	0.324	0.307	0.216	Libya
0.181	0.229	0.319	0.308	0.221	Lesotho
0.173	0.216	0.320	0.294	0.201	Mongolia
0.170	0.225	0.317	0.287	0.205	Djibouti
0.170	0.238	0.325	0.271	0.197	Samoa
0.168	0.221	0.314	0.288	0.199	Cambodia
0.164	0.210	0.292	0.274	0.206	Pakistan
0.158	0.204	0.273	0.268	0.208	Eritrea
0.157	0.183	0.277	0.252	0.203	Maldives
0.157	0.194	0.268	0.281	0.212	Mayotte
0.156	0.199	0.279	0.260	0.199	Jordan
0.155	0.203	0.289	0.263	0.193	Botswana
0.154	0.199	0.287	0.257	0.191	Lao People's Democratic Republic
0.152	0.209	0.279	0.253	0.197	Timor-Leste
0.146	0.180	0.282	0.248	0.177	Saudi Arabia
0.145	0.187	0.254	0.239	0.199	Eswatini
0.139	0.173	0.248	0.234	0.188	Namibia
0.135	0.176	0.302	0.222	0.145	Kuwait
0.134	0.173	0.244	0.224	0.183	Sudan
0.133	0.173	0.245	0.221	0.182	Gabon
0.130	0.167	0.231	0.216	0.183	Ethiopia
0.130	0.174	0.242	0.224	0.179	Solomon Islands
0.128	0.158	0.245	0.207	0.169	Tajikistan
0.127	0.165	0.236	0.212	0.176	Africa
0.125	0.157	0.229	0.212	0.176	Iraq
0.125	0.150	0.253	0.199	0.156	Bahrain
0.124	0.163	0.225	0.204	0.176	South Sudan
0.123	0.168	0.244	0.196	0.163	Vanuatu
0.122	0.165	0.247	0.204	0.160	Papua New Guinea
0.121	0.157	0.226	0.203	0.171	Liberia
0.121	0.157	0.222	0.203	0.173	Benin
0.120	0.155	0.224	0.199	0.170	Mauritania
0.120	0.157	0.222	0.196	0.171	State of Palestine
0.119	0.150	0.219	0.173	0.170	Sao Tome and Principe
0.118	0.178	0.278	0.228	0.136	Micronesia (Fed. States of)
0.118	0.138	0.222	0.195	0.165	Oman
0.118	0.160	0.260	0.195	0.143	Western Sahara
0.116	0.157	0.230	0.190	0.159	Ghana
0.116	0.146	0.216	0.193	0.166	Madagascar
0.113	0.149	0.220	0.193	0.162	Comoros
0.112	0.142	0.205	0.188	0.169	Zimbabwe
0.112	0.146	0.216	0.193	0.161	Rwanda
0.111	0.146	0.208	0.184	0.165	Senegal
0.110	0.144	0.203	0.182	0.166	Democratic Republic of the Congo
0.107	0.139	0.202	0.175	0.160	Yemen
0.106	0.141	0.203	0.174	0.159	Sierra Leone
0.103	0.165	0.239	0.197	0.135	Kiribati
0.103	0.136	0.192	0.168	0.160	Mozambique
0.102	0.135	0.192	0.168	0.158	Somalia
0.102	0.139	0.202	0.168	0.152	Togo
0.101	0.137	0.201	0.166	0.152	Congo
0.101	0.133	0.191	0.166	0.156	Central African Republic
0.100	0.134	0.194	0.166	0.154	Guinea
0.100	0.135	0.197	0.165	0.152	Côte d'Ivoire
0.098	0.130	0.189	0.160	0.153	Cameroon
0.097	0.130	0.192	0.160	0.150	Guinea-Bissau
0.097	0.129	0.183	0.157	0.155	Malawi
0.096	0.129	0.186	0.157	0.152	United Republic of Tanzania
0.096	0.128	0.184	0.156	0.151	Afghanistan
0.094	0.122	0.186	0.153	0.148	Kenya
0.094	0.130	0.190	0.154	0.145	Nigeria
0.092	0.118	0.179	0.149	0.149	Equatorial Guinea
0.091	0.126	0.177	0.146	0.149	Gambia
0.091	0.116	0.170	0.150	0.152	Chad
0.090	0.112	0.203	0.141	0.126	Qatar
0.088	0.121	0.172	0.146	0.147	Niger
0.088	0.118	0.173	0.144	0.145	Burkina Faso
0.088	0.109	0.169	0.144	0.146	Burundi
0.087	0.117	0.169	0.145	0.147	Mali
0.085	0.111	0.164	0.139	0.145	Angola
0.083	0.104	0.190	0.130	0.124	United Arab Emirates
0.083	0.107	0.158	0.134	0.144	Zambia
0.074	0.099	0.147	0.121	0.137	Uganda

Note that in addition to countries, there are rows for each continent and for the world.

Findings

The overall IFR estimates, with the exception of Levin et al., are relatively consistent with each other, usually within 30-40%.

The country with the oldest population is expected to have the highest overall IFR: Japan at 1.3-1.6% (excluding Levin et al.)

The country with the youngest population is expected to have the lowest overall IFR: Uganda at 0.074-0.147%

The overall IFR varies dramatically by more than 10-fold between countries with a young population and those with an old population.

In fact, the young age of the population in Africa is a major factor explaining the relatively small number of deaths on this continent. We find IFR=0.127% for Africa, and IFR=0.810% in Europe, a 6-fold difference.

Calculating the age-stratified IFR of COVID-19 from the Spanish ENE-COVID study

The largest serological prevalence survey of COVID-19 was conducted by Spain during the second round of the ENE-COVID study that analyzed 63 564 samples between 18 May 2020 and 01 June 2020. We used its provisional results published on 03 June to calculate the overall and age-stratified IFR of COVID-19 with the Python script calc_ifr.py:

$ ./calc_ifr.py
Ages  0 to   9:  115013 infected,     4 deaths,  0.003% IFR
Ages 10 to  19:  177929 infected,     7 deaths,  0.004% IFR
Ages 20 to  29:  212099 infected,    32 deaths,  0.015% IFR
Ages 30 to  39:  281290 infected,    86 deaths,  0.030% IFR
Ages 40 to  49:  447942 infected,   287 deaths,  0.064% IFR
Ages 50 to  59:  410213 infected,   874 deaths,  0.213% IFR
Ages 60 to  69:  334709 infected,  2404 deaths,  0.718% IFR
Ages 70 to  79:  270572 infected,  6451 deaths,  2.384% IFR
Ages 80 to  89:  131703 infected, 11150 deaths,  8.466% IFR
Ages 90 to 199:   46631 infected,  5827 deaths, 12.497% IFR
Ages  0 to 199: 2428102 infected, 27121 deaths,  1.117% IFR

The average IFR for Spain is 1.117%. However the true IFR may be higher due to right-censoring, under-reporting of deaths, or low specificity of the serological test; or the true IFR may be lower due to low sensitivity of the serological test.

The Spanish serological study remains the largest published study available to this day. The age-stratified IFR was calculated from three sources:

Detailed prevalence data for age brackets, from the serosurvey (table 1)
Total deaths and deaths per age bracket from the Ministry of Health's daily report for 29 May (table 2 and table 3)
Population pyramid for Spain, from worldpopulationreview.com

In order to minimize right-censoring (deaths lagging infections,) the parameters total deaths and deaths per age bracket should be obtained from a point in time as close as possible to when the serosurvey was conducted (18 May to 01 June, preferably closer to the mid-point 25 May.) This is because the seroconversion time is roughly the same as the time between infection and death. We found only two Ministry of Health reports in this time period that document deaths per age bracket: 18 May, 29 May. However the Ministry of Health has made significant corrections to deaths statistics on 25 May by subtracting approximately 2 000 deaths. Therefore we trusted the statistics from 29 May over those of 18 May. Furthermore, 29 May is closer to the mid-point.

Important detail to note: there were 27 121 total deaths, however age information was only available for 20 585 deaths, and was missing for 6 536 deaths. We assume that these 6 536 deaths were distributed proportionally—not equally—among age brackets, which seems to be a reasonable assumption.

Regarding the specificity of the commercial test used (COVID-19 IgG Rapid Test Cassette by Zhejiang Orient Gene Biotech Co Ltd) we found various claims, all 100% or close, so no significant false positives are expected:

100% claimed by the manufacturer (serosurvey, page 3)
100% measured by the Ministry of Health (serosurvey, page 3)
99.2% measured by a third-party

However the sensitivity is more uncertain:

97% claimed by the manufacturer (serosurvey, page 3)
79% measured by the Ministry of Health (serosurvey, page 3)
93.1% measured by a third-party

So a false negative rate anywhere from 3% to 21% could be possible, and we think it is premature to adjust IFR calculations given the exact sensitivity is not known.