Updated: 01 Oct 2020

Author: Marc Bevand

This project studies the age-stratified infection fatality ratio (IFR) of COVID-19:

• compare COVID-19 to seasonal influenza (flu)
• calculate the expected overall IFR based on countries' population pyramids
• calculate the age-stratified IFR of COVID-19 from the Spanish ENE-COVID serosurvey

Comparing COVID-19 to seasonal influenza

The above chart compares the IFR of COVID-19 to the IFR of seasonal influenza. We find that COVID-19 is definitely significantly more fatal than influenza at all ages above 30 years. The source code producing this chart is covid_vs_flu.py.

The vertical indicators represent the difference in fatality between COVID-19 and influenza at various ages, from 30 to 80 years at 10-year intervals. The top/bottom of the indicators are anchored at the geometric means of the COVID-19/influenza IFR estimates.

The COVID-19 IFR curves represent these estimates:

1. ENE-COVID Spanish serosurvey (calculated by calc_ifr.py, see this section)
2. US CDC COVID-19 Pandemic Planning Scenarios (table 1); which is based on Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
3. Verity et al.: Estimates of the severity of coronavirus disease 2019: a model-based analysis (table 1)
4. Levin et al.: Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications (table 3)
5. Perez-Saez et al.: Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland
6. Poletti et al.: Age-specific SARS-CoV-2 infection fatality ratio and associated risk factors, Italy, February to April 2020 (table 1, column "Any time")
7. Picon et al.: Coronavirus Disease 2019 Population-based Prevalence, Risk Factors, Hospitalization, and Fatality Rates in Southern Brazil (table 2)
8. Gudbjartsson et al.: Humoral Immune Response to SARS-CoV-2 in Iceland, specifically Supplementary Appendix 1 (table S7)
9. PHAS - Public Health Agency of Sweden: The infection fatality rate of COVID-19 in Stockholm – Technical report (table B.1)
10. O’Driscoll et al.: Age-specific mortality and immunity patterns of SARS-CoV-2 infection in 45 countries (table S4)
11. Ward et al.: Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults, specifically Supplementary Appendix (table S2a, column "Based on confirmed COVID-19 deaths")
12. Yang et al.: Estimating the infection fatality risk of COVID-19 in New York City during the spring 2020 pandemic wave (table 1)
13. Molenberghs et al.: Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates (table 6)

The seasonal influenza IFR curves represent data from the US CDC on multiple seasons of flu:

1. 2019-2020 influenza burden
2. 2018-2019 influenza burden
3. 2017-2018 influenza burden
4. 2016-2017 influenza burden
5. 2015-2016 influenza burden
6. 2014-2015 influenza burden

However, these CDC statistics (eg. table 1 in "2018-2019 influenza burden",) only give the estimated number of symptomatic illnesses. We must account for asymptomatic ones as well to calculate the IFR.

In Key Facts About Influenza (Flu) the CDC implies 55-60% of illnesses are symptomatic:

«on average, about 8% of the U.S. population gets sick from flu each season, with a range of between 3% and 11%, depending on the season. [...] The commonly cited 5% to 20% estimate was based on a study that examined both symptomatic and asymptomatic influenza illness, which means it also looked at people who may have had the flu but never knew it because they didn’t have any symptoms. The 3% to 11% range is an estimate of the proportion of people who have symptomatic flu illness.»

Thus, the CDC acknowledges that 55-60% of illnesses are symptomatic (3/5 = 60%, and 11/20 = 55%.) We use the mid-point, 57.5%, to infer the number of asymptomatic illnesses:

total_illnesses = symptomatic_illnesses / .575

Age-stratified IFR applied to countries' population pyramids

The script apply_ifr.py uses a handful of age-stratified IFR estimates (from the chart above) 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.

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	O'Drisc	Region
1.274	1.311	1.605	2.247	1.283	0.961	Japan
1.065	1.092	1.382	1.847	1.068	0.811	Italy
1.041	1.043	1.339	1.806	1.057	0.796	Greece
0.993	1.012	1.305	1.702	0.994	0.764	Germany
0.984	1.045	1.320	1.726	0.986	0.753	Portugal
0.931	0.985	1.270	1.648	0.918	0.704	Martinique
0.919	0.933	1.221	1.658	0.918	0.706	Lithuania
0.916	0.942	1.207	1.648	0.921	0.694	Spain
0.914	0.941	1.201	1.606	0.913	0.682	France
0.899	1.008	1.248	1.556	0.883	0.671	Finland
0.881	0.919	1.192	1.624	0.870	0.672	Latvia
0.875	0.957	1.205	1.528	0.857	0.655	Puerto Rico
0.868	0.892	1.171	1.516	0.870	0.672	Estonia
0.865	0.932	1.209	1.487	0.855	0.665	Croatia
0.855	0.965	1.205	1.471	0.835	0.637	Malta
0.846	0.881	1.137	1.478	0.847	0.639	Belgium
0.843	0.905	1.181	1.489	0.829	0.645	Slovenia
0.840	0.943	1.154	1.471	0.838	0.632	Sweden
0.836	0.910	1.149	1.462	0.830	0.638	Austria
0.825	0.897	1.132	1.447	0.819	0.624	Switzerland
0.810	0.861	1.117	1.396	0.802	0.615	Europe
0.802	0.909	1.141	1.394	0.785	0.603	Netherlands
0.801	0.949	1.181	1.377	0.779	0.608	Bulgaria
0.798	0.864	1.113	1.399	0.779	0.600	Guadeloupe
0.797	0.935	1.136	1.370	0.782	0.595	Denmark
0.794	0.868	1.089	1.384	0.794	0.598	United Kingdom
0.791	0.811	1.102	1.367	0.761	0.588	China, Hong Kong SAR
0.759	0.830	1.090	1.285	0.745	0.578	Romania
0.753	0.854	1.101	1.323	0.732	0.573	Hungary
0.745	0.800	1.064	1.285	0.724	0.557	Poland
0.739	0.828	1.059	1.315	0.737	0.565	Channel Islands
0.738	0.878	1.092	1.302	0.717	0.555	Czechia
0.734	0.810	1.047	1.288	0.714	0.548	Canada
0.719	0.756	0.998	1.259	0.705	0.534	Barbados
0.713	0.808	1.038	1.210	0.694	0.538	Curaçao
0.709	0.802	1.007	1.239	0.699	0.528	Norway
0.680	0.800	1.029	1.160	0.659	0.515	Serbia
0.676	0.694	0.904	1.193	0.686	0.505	Uruguay
0.675	0.745	0.967	1.181	0.660	0.503	Northern America
0.671	0.736	0.944	1.174	0.670	0.503	Australia
0.671	0.733	0.983	1.103	0.651	0.513	Ukraine
0.669	0.738	0.958	1.169	0.654	0.497	United States of America
0.659	0.757	1.011	1.088	0.620	0.503	Bosnia and Herzegovina
0.654	0.742	0.945	1.137	0.645	0.490	New Zealand
0.644	0.735	0.941	1.131	0.630	0.489	Cuba
0.641	0.877	1.059	1.152	0.608	0.491	United States Virgin Islands
0.633	0.725	0.963	1.090	0.606	0.483	Republic of Korea
0.628	0.686	0.938	1.100	0.599	0.472	China, Taiwan Province of China
0.624	0.663	0.916	1.030	0.606	0.473	Russian Federation
0.624	0.657	0.921	1.082	0.606	0.483	Belarus
0.615	0.668	0.881	1.078	0.616	0.467	Luxembourg
0.607	0.670	0.896	1.002	0.598	0.464	Georgia
0.605	0.683	0.888	1.061	0.599	0.453	Iceland
0.604	0.709	0.935	1.059	0.580	0.457	Slovakia
0.582	0.667	0.891	0.989	0.567	0.444	Montenegro
0.563	0.653	0.838	0.972	0.559	0.424	Ireland
0.561	0.649	0.839	0.957	0.559	0.426	Cyprus
0.548	0.659	0.857	0.941	0.533	0.418	Albania
0.531	0.652	0.874	0.920	0.508	0.412	Aruba
0.518	0.580	0.754	0.902	0.526	0.388	Oceania
0.510	0.588	0.795	0.881	0.491	0.385	Thailand
0.500	0.569	0.763	0.880	0.493	0.377	Réunion
0.499	0.545	0.710	0.872	0.516	0.371	Israel
0.497	0.522	0.748	0.794	0.491	0.377	Armenia
0.497	0.616	0.816	0.851	0.474	0.378	North Macedonia
0.493	0.550	0.736	0.861	0.492	0.370	Chile
0.479	0.550	0.796	0.829	0.433	0.356	Singapore
0.454	0.515	0.671	0.790	0.471	0.343	Argentina
0.449	0.514	0.738	0.770	0.412	0.328	China, Macao SAR
0.447	0.544	0.732	0.772	0.425	0.331	Mauritius
0.435	0.501	0.721	0.766	0.413	0.332	Republic of Moldova
0.415	0.511	0.681	0.717	0.402	0.310	Trinidad and Tobago
0.413	0.475	0.638	0.712	0.417	0.309	Costa Rica
0.413	0.468	0.626	0.704	0.413	0.304	Saint Lucia
0.409	0.507	0.695	0.707	0.385	0.308	China
0.394	0.497	0.648	0.712	0.409	0.303	Antigua and Barbuda
0.387	0.495	0.651	0.668	0.377	0.286	Sri Lanka
0.377	0.487	0.635	0.622	0.373	0.284	Dem. People's Republic of Korea
0.376	0.437	0.594	0.645	0.382	0.282	Brazil
0.365	0.464	0.611	0.618	0.373	0.279	Guam
0.364	0.425	0.569	0.627	0.381	0.275	Jamaica
0.361	0.454	0.605	0.663	0.375	0.279	Saint Vincent and the Grenadines
0.359	0.399	0.540	0.623	0.380	0.269	Panama
0.358	0.418	0.566	0.615	0.370	0.269	WORLD
0.356	0.410	0.555	0.614	0.371	0.267	Latin America and the Caribbean
0.355	0.412	0.560	0.613	0.367	0.267	Colombia
0.354	0.438	0.595	0.642	0.368	0.271	Grenada
0.347	0.399	0.528	0.595	0.377	0.265	El Salvador
0.343	0.363	0.528	0.602	0.355	0.257	Viet Nam
0.342	0.408	0.552	0.588	0.356	0.260	Turkey
0.339	0.397	0.537	0.583	0.358	0.258	Peru
0.336	0.394	0.553	0.579	0.344	0.256	Tunisia
0.327	0.432	0.573	0.547	0.328	0.249	New Caledonia
0.325	0.394	0.541	0.559	0.331	0.245	Asia
0.310	0.350	0.463	0.535	0.346	0.233	Bolivia (Plurinational State of)
0.309	0.348	0.480	0.532	0.333	0.232	Dominican Republic
0.307	0.348	0.503	0.505	0.319	0.236	Kazakhstan
0.306	0.352	0.485	0.532	0.327	0.230	Mexico
0.305	0.365	0.502	0.526	0.317	0.228	Venezuela (Bolivarian Republic of)
0.303	0.347	0.474	0.525	0.329	0.228	Ecuador
0.295	0.346	0.482	0.501	0.317	0.225	Lebanon
0.293	0.400	0.542	0.477	0.284	0.225	Seychelles
0.287	0.384	0.527	0.513	0.286	0.220	French Polynesia
0.282	0.326	0.456	0.456	0.298	0.208	Guyana
0.280	0.335	0.463	0.469	0.298	0.211	Suriname
0.273	0.313	0.474	0.452	0.279	0.208	Azerbaijan
0.272	0.337	0.474	0.462	0.284	0.207	Morocco
0.269	0.358	0.495	0.460	0.272	0.206	Bahamas
0.263	0.325	0.453	0.442	0.279	0.196	Malaysia
0.260	0.305	0.426	0.446	0.289	0.198	Algeria
0.257	0.307	0.418	0.436	0.287	0.193	Paraguay
0.251	0.297	0.403	0.423	0.288	0.190	Bhutan
0.246	0.301	0.431	0.418	0.263	0.186	Iran (Islamic Republic of)
0.234	0.293	0.414	0.398	0.253	0.176	India
0.229	0.293	0.416	0.387	0.244	0.172	Indonesia
0.228	0.259	0.373	0.393	0.262	0.173	Nicaragua
0.221	0.269	0.368	0.374	0.255	0.167	Bangladesh
0.221	0.232	0.349	0.374	0.260	0.170	Cabo Verde
0.217	0.284	0.375	0.310	0.247	0.163	Tonga
0.215	0.277	0.401	0.366	0.228	0.161	Myanmar
0.208	0.235	0.334	0.351	0.244	0.153	Belize
0.206	0.235	0.331	0.353	0.249	0.155	Honduras
0.204	0.233	0.323	0.349	0.253	0.155	Guatemala
0.204	0.255	0.360	0.342	0.232	0.152	Philippines
0.202	0.267	0.360	0.344	0.232	0.151	Nepal
0.197	0.249	0.381	0.331	0.210	0.149	Brunei Darussalam
0.194	0.237	0.331	0.330	0.233	0.146	Haiti
0.193	0.251	0.355	0.325	0.220	0.145	South Africa
0.193	0.222	0.339	0.316	0.220	0.145	Turkmenistan
0.192	0.247	0.343	0.321	0.224	0.142	Egypt
0.192	0.262	0.376	0.318	0.206	0.142	Fiji
0.188	0.220	0.335	0.310	0.217	0.141	Kyrgyzstan
0.188	0.245	0.353	0.344	0.216	0.143	French Guiana
0.187	0.219	0.340	0.317	0.212	0.142	Uzbekistan
0.185	0.224	0.323	0.317	0.222	0.140	Syrian Arab Republic
0.182	0.226	0.324	0.307	0.216	0.140	Libya
0.181	0.229	0.319	0.308	0.221	0.136	Lesotho
0.173	0.216	0.320	0.294	0.201	0.130	Mongolia
0.170	0.225	0.317	0.287	0.205	0.128	Djibouti
0.170	0.238	0.325	0.271	0.197	0.126	Samoa
0.168	0.221	0.314	0.288	0.199	0.126	Cambodia
0.164	0.210	0.292	0.274	0.206	0.123	Pakistan
0.158	0.204	0.273	0.268	0.208	0.118	Eritrea
0.157	0.183	0.277	0.252	0.203	0.123	Maldives
0.157	0.194	0.268	0.281	0.212	0.120	Mayotte
0.156	0.199	0.279	0.260	0.199	0.118	Jordan
0.155	0.203	0.289	0.263	0.193	0.115	Botswana
0.154	0.199	0.287	0.257	0.191	0.115	Lao People's Democratic Republic
0.152	0.209	0.279	0.253	0.197	0.113	Timor-Leste
0.146	0.180	0.282	0.248	0.177	0.112	Saudi Arabia
0.145	0.187	0.254	0.239	0.199	0.109	Eswatini
0.139	0.173	0.248	0.234	0.188	0.104	Namibia
0.135	0.176	0.302	0.222	0.145	0.103	Kuwait
0.134	0.173	0.244	0.224	0.183	0.100	Sudan
0.133	0.173	0.245	0.221	0.182	0.101	Gabon
0.130	0.167	0.231	0.216	0.183	0.096	Ethiopia
0.130	0.174	0.242	0.224	0.179	0.098	Solomon Islands
0.128	0.158	0.245	0.207	0.169	0.095	Tajikistan
0.127	0.165	0.236	0.212	0.176	0.094	Africa
0.125	0.157	0.229	0.212	0.176	0.095	Iraq
0.125	0.150	0.253	0.199	0.156	0.093	Bahrain
0.124	0.163	0.225	0.204	0.176	0.092	South Sudan
0.123	0.168	0.244	0.196	0.163	0.091	Vanuatu
0.122	0.165	0.247	0.204	0.160	0.090	Papua New Guinea
0.121	0.157	0.226	0.203	0.171	0.091	Liberia
0.121	0.157	0.222	0.203	0.173	0.090	Benin
0.120	0.155	0.224	0.199	0.170	0.089	Mauritania
0.120	0.157	0.222	0.196	0.171	0.089	State of Palestine
0.119	0.150	0.219	0.173	0.170	0.089	Sao Tome and Principe
0.118	0.178	0.278	0.228	0.136	0.087	Micronesia (Fed. States of)
0.118	0.138	0.222	0.195	0.165	0.090	Oman
0.118	0.160	0.260	0.195	0.143	0.087	Western Sahara
0.116	0.157	0.230	0.190	0.159	0.085	Ghana
0.116	0.146	0.216	0.193	0.166	0.086	Madagascar
0.113	0.149	0.220	0.193	0.162	0.085	Comoros
0.112	0.142	0.205	0.188	0.169	0.085	Zimbabwe
0.112	0.146	0.216	0.193	0.161	0.083	Rwanda
0.111	0.146	0.208	0.184	0.165	0.083	Senegal
0.110	0.144	0.203	0.182	0.166	0.081	Democratic Republic of the Congo
0.107	0.139	0.202	0.175	0.160	0.078	Yemen
0.106	0.141	0.203	0.174	0.159	0.079	Sierra Leone
0.103	0.165	0.239	0.197	0.135	0.076	Kiribati
0.103	0.136	0.192	0.168	0.160	0.076	Mozambique
0.102	0.135	0.192	0.168	0.158	0.075	Somalia
0.102	0.139	0.202	0.168	0.152	0.075	Togo
0.101	0.137	0.201	0.166	0.152	0.075	Congo
0.101	0.133	0.191	0.166	0.156	0.074	Central African Republic
0.100	0.134	0.194	0.166	0.154	0.074	Guinea
0.100	0.135	0.197	0.165	0.152	0.074	Côte d'Ivoire
0.098	0.130	0.189	0.160	0.153	0.072	Cameroon
0.097	0.130	0.192	0.160	0.150	0.071	Guinea-Bissau
0.097	0.129	0.183	0.157	0.155	0.071	Malawi
0.096	0.129	0.186	0.157	0.152	0.071	United Republic of Tanzania
0.096	0.128	0.184	0.156	0.151	0.070	Afghanistan
0.094	0.122	0.186	0.153	0.148	0.070	Kenya
0.094	0.130	0.190	0.154	0.145	0.068	Nigeria
0.092	0.118	0.179	0.149	0.149	0.069	Equatorial Guinea
0.091	0.126	0.177	0.146	0.149	0.067	Gambia
0.091	0.116	0.170	0.150	0.152	0.067	Chad
0.090	0.112	0.203	0.141	0.126	0.067	Qatar
0.088	0.121	0.172	0.146	0.147	0.064	Niger
0.088	0.118	0.173	0.144	0.145	0.064	Burkina Faso
0.088	0.109	0.169	0.144	0.146	0.064	Burundi
0.087	0.117	0.169	0.145	0.147	0.064	Mali
0.085	0.111	0.164	0.139	0.145	0.063	Angola
0.083	0.104	0.190	0.130	0.124	0.063	United Arab Emirates
0.083	0.107	0.158	0.134	0.144	0.061	Zambia
0.074	0.099	0.147	0.121	0.137	0.054	Uganda
ENE-COV	US_CDC	Verity	Levin	Gudbj	O'Drisc	Region

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. and O’Driscoll et al., are relatively consistent with each other, usually within 30-40%. Levin et al. is up to 2-fold higher than the others, depending on the country.

The country with the oldest population is expected to have the highest overall IFR: Japan at 1.3-1.6% (excluding Levin et al. and O’Driscoll 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 (ENE-COVID) IFR=0.127% for Africa, and IFR=0.810% in Europe, a 6-fold difference.

The overall IFR is, for each world region and the world:

• 0.810% Europe
• 0.675% Northern America
• 0.518% Oceania
• 0.356% Latin America and the Caribbean
• 0.325% Asia
• 0.127% Africa
• 0.358% World

Our code, with the ENE-COVID Spanish serosurvey data from June 2020, accurately predicted an overall IFR of 0.669% in the United States, which is very close to overall US CDC estimate of 0.65% published in July 2020.

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:

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