Updated: 20 Jun 2021

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 Levin et al. (see ref. 4 below)
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 (table S3)
11. Ward et al.: Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults (table 2)
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)
14. Brazeau et al.: Report 34: COVID-19 Infection Fatality Ratio: Estimates from Seroprevalence (table 2, column "IFR (%) with Seroreversion")

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.

Not all influenza infections have symptoms, the infected people may not be aware they are infected. The fraction of cases without symptoms but a confirmation (serologic) of antibodies is called the asymptomatic fraction. The asymptomatic fraction of influenza cases has been studied in recent years in various journal articles. The most recent study was part of UK FluWatch study with results published in the Lancet - showing the asymptomatic fraction was 77%. https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(14)70034-7/fulltext Another study published at : https://journals.lww.com/epidem/Fulltext/2010/09000/Estimating_Pathogen_specific_Asymptomatic_Ratios.28.aspx determines for H1N1 subtype 75%, and H3N2 subtype 65% asymptomatic fraction. Finally a meta study is available here : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586318/ from which a range of 65-85% asymptomatic fraction is determined. We use an estimate of 67% asymptomatic fraction - or 33% symptomatic.

total_illnesses = symptomatic_illnesses / .33

Age-stratified IFR applied to countries' population pyramids

The script apply_ifr.py uses a handful of age-stratified IFR estimates for COVID-19 and the seasonal flu and applies them to countries' population pyramids, to find their expected average IFR. The calculation assumes equal prevalence of the disease among all age groups.

IFR estimates are a subset of the same sources as in covid_vs_flu.py. The flu IFR is from the US CDC (last flu season, 2019-2020.)

The real-world overall IFR will, of course, 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 on the ENE-COVID column):

ENE-COVID	COVID: US CDC	COVID: Verity	COVID: Levin	COVID: Brazeau	Flu: US CDC	Region
1.274	2.689	1.605	2.660	1.359	0.072	Japan
1.065	2.251	1.382	2.177	1.140	0.061	Italy
1.041	2.152	1.339	2.135	1.103	0.058	Greece
0.993	2.110	1.305	1.997	1.059	0.057	Germany
0.984	2.196	1.320	2.027	1.059	0.059	Portugal
0.931	2.117	1.270	1.938	1.021	0.057	Martinique
0.919	2.008	1.221	1.953	1.017	0.055	Lithuania
0.916	1.947	1.207	1.950	1.014	0.053	Spain
0.914	2.004	1.201	1.899	1.003	0.054	France
0.899	2.166	1.248	1.819	0.980	0.059	Finland
0.881	2.009	1.192	1.910	0.998	0.055	Latvia
0.875	2.024	1.205	1.789	0.969	0.055	Puerto Rico
0.868	1.967	1.171	1.773	0.939	0.054	Estonia
0.865	2.059	1.209	1.733	0.932	0.056	Croatia
0.855	2.089	1.205	1.716	0.936	0.057	Malta
0.846	1.875	1.137	1.740	0.925	0.051	Belgium
0.843	2.012	1.181	1.740	0.929	0.055	Slovenia
0.840	1.961	1.154	1.722	0.922	0.053	Sweden
0.836	1.881	1.149	1.709	0.919	0.051	Austria
0.825	1.868	1.132	1.697	0.911	0.051	Switzerland
0.810	1.864	1.117	1.633	0.887	0.051	Europe
0.802	1.951	1.141	1.625	0.886	0.053	Netherlands
0.801	2.075	1.181	1.592	0.874	0.056	Bulgaria
0.798	1.884	1.113	1.637	0.893	0.051	Guadeloupe
0.797	1.954	1.136	1.593	0.878	0.053	Denmark
0.794	1.815	1.089	1.622	0.873	0.050	United Kingdom
0.791	1.804	1.102	1.607	0.893	0.050	China, Hong Kong SAR
0.759	1.873	1.090	1.494	0.826	0.051	Romania
0.753	1.952	1.101	1.534	0.841	0.053	Hungary
0.745	1.825	1.064	1.502	0.830	0.050	Poland
0.739	1.763	1.059	1.538	0.819	0.048	Channel Islands
0.738	1.946	1.092	1.510	0.830	0.053	Czechia
0.734	1.775	1.047	1.505	0.829	0.049	Canada
0.719	1.644	0.998	1.480	0.816	0.045	Barbados
0.713	1.753	1.038	1.403	0.789	0.048	Curaçao
0.709	1.714	1.007	1.447	0.797	0.047	Norway
0.680	1.851	1.029	1.337	0.752	0.051	Serbia
0.676	1.476	0.904	1.407	0.760	0.041	Uruguay
0.675	1.647	0.967	1.379	0.768	0.045	Northern America
0.671	1.588	0.944	1.374	0.754	0.044	Australia
0.671	1.669	0.983	1.271	0.736	0.046	Ukraine
0.669	1.632	0.958	1.365	0.761	0.045	United States of America
0.659	1.768	1.011	1.243	0.736	0.049	Bosnia and Herzegovina
0.654	1.606	0.945	1.323	0.738	0.044	New Zealand
0.644	1.588	0.941	1.314	0.740	0.044	Cuba
0.641	1.976	1.059	1.308	0.728	0.054	United States Virgin Islands
0.633	1.587	0.963	1.259	0.722	0.044	Republic of Korea
0.628	1.584	0.938	1.280	0.730	0.044	China, Taiwan Province of China
0.624	1.537	0.916	1.191	0.693	0.043	Russian Federation
0.624	1.552	0.921	1.252	0.707	0.043	Belarus
0.615	1.426	0.881	1.261	0.698	0.040	Luxembourg
0.607	1.510	0.896	1.155	0.667	0.042	Georgia
0.605	1.512	0.888	1.236	0.688	0.042	Iceland
0.604	1.642	0.935	1.222	0.695	0.045	Slovakia
0.582	1.556	0.891	1.138	0.654	0.043	Montenegro
0.563	1.437	0.838	1.126	0.641	0.040	Ireland
0.561	1.421	0.839	1.106	0.632	0.040	Cyprus
0.548	1.467	0.857	1.079	0.623	0.041	Albania
0.531	1.493	0.874	1.049	0.608	0.042	Aruba
0.518	1.265	0.754	1.052	0.593	0.036	Oceania
0.510	1.319	0.795	1.017	0.600	0.037	Thailand
0.500	1.284	0.763	1.020	0.585	0.036	Réunion
0.499	1.218	0.710	1.021	0.570	0.034	Israel
0.497	1.195	0.748	0.915	0.552	0.034	Armenia
0.497	1.443	0.816	0.968	0.578	0.040	North Macedonia
0.493	1.230	0.736	1.001	0.576	0.035	Chile
0.479	1.367	0.796	0.952	0.588	0.039	Singapore
0.454	1.130	0.671	0.917	0.521	0.032	Argentina
0.449	1.241	0.738	0.887	0.556	0.035	China, Macao SAR
0.447	1.273	0.732	0.885	0.539	0.036	Mauritius
0.435	1.263	0.721	0.876	0.527	0.036	Republic of Moldova
0.415	1.168	0.681	0.820	0.502	0.033	Trinidad and Tobago
0.413	1.048	0.638	0.823	0.490	0.030	Costa Rica
0.413	1.018	0.626	0.818	0.498	0.030	Saint Lucia
0.409	1.226	0.695	0.806	0.502	0.035	China
0.394	1.042	0.648	0.822	0.463	0.030	Antigua and Barbuda
0.387	1.136	0.651	0.762	0.469	0.032	Sri Lanka
0.377	0.984	0.635	0.706	0.447	0.029	Dem. People's Republic of Korea
0.376	0.984	0.594	0.743	0.451	0.029	Brazil
0.365	1.033	0.611	0.703	0.425	0.030	Guam
0.364	0.929	0.569	0.725	0.431	0.027	Jamaica
0.361	1.018	0.605	0.764	0.435	0.029	Saint Vincent and the Grenadines
0.359	0.874	0.540	0.724	0.428	0.026	Panama
0.358	0.953	0.566	0.709	0.428	0.028	WORLD
0.356	0.918	0.555	0.710	0.426	0.027	Latin America and the Caribbean
0.355	0.930	0.560	0.707	0.427	0.027	Colombia
0.354	0.987	0.595	0.740	0.421	0.029	Grenada
0.347	0.877	0.528	0.687	0.401	0.026	El Salvador
0.343	0.828	0.528	0.701	0.422	0.025	Viet Nam
0.342	0.920	0.552	0.675	0.408	0.027	Turkey
0.339	0.893	0.537	0.671	0.404	0.026	Peru
0.336	0.919	0.553	0.664	0.407	0.027	Tunisia
0.327	0.974	0.573	0.616	0.396	0.028	New Caledonia
0.325	0.916	0.541	0.640	0.399	0.027	Asia
0.310	0.761	0.463	0.622	0.365	0.023	Bolivia (Plurinational State of)
0.309	0.779	0.480	0.616	0.374	0.023	Dominican Republic
0.307	0.826	0.503	0.574	0.368	0.024	Kazakhstan
0.306	0.791	0.485	0.615	0.376	0.023	Mexico
0.305	0.829	0.502	0.604	0.377	0.024	Venezuela (Bolivarian Republic of)
0.303	0.782	0.474	0.607	0.368	0.023	Ecuador
0.295	0.788	0.482	0.576	0.356	0.023	Lebanon
0.293	0.886	0.542	0.527	0.368	0.026	Seychelles
0.287	0.897	0.527	0.579	0.370	0.026	French Polynesia
0.282	0.737	0.456	0.521	0.343	0.022	Guyana
0.280	0.740	0.463	0.536	0.344	0.022	Suriname
0.273	0.737	0.474	0.515	0.343	0.022	Azerbaijan
0.272	0.795	0.474	0.522	0.337	0.024	Morocco
0.269	0.814	0.495	0.517	0.345	0.024	Bahamas
0.263	0.754	0.453	0.504	0.329	0.023	Malaysia
0.260	0.702	0.426	0.512	0.319	0.021	Algeria
0.257	0.703	0.418	0.500	0.313	0.021	Paraguay
0.251	0.650	0.403	0.486	0.302	0.020	Bhutan
0.246	0.699	0.431	0.476	0.312	0.021	Iran (Islamic Republic of)
0.234	0.694	0.414	0.452	0.300	0.021	India
0.229	0.675	0.416	0.437	0.299	0.021	Indonesia
0.228	0.599	0.373	0.452	0.286	0.018	Nicaragua
0.221	0.567	0.368	0.428	0.279	0.018	Bangladesh
0.221	0.530	0.349	0.432	0.276	0.017	Cabo Verde
0.217	0.610	0.375	0.342	0.253	0.019	Tonga
0.215	0.670	0.401	0.412	0.285	0.020	Myanmar
0.208	0.521	0.334	0.406	0.267	0.016	Belize
0.206	0.529	0.331	0.407	0.258	0.017	Honduras
0.204	0.526	0.323	0.403	0.250	0.016	Guatemala
0.204	0.590	0.360	0.388	0.262	0.018	Philippines
0.202	0.614	0.360	0.388	0.258	0.019	Nepal
0.197	0.595	0.381	0.373	0.269	0.019	Brunei Darussalam
0.194	0.547	0.331	0.376	0.245	0.017	Haiti
0.193	0.589	0.355	0.366	0.253	0.018	South Africa
0.193	0.529	0.339	0.360	0.250	0.017	Turkmenistan
0.192	0.567	0.343	0.363	0.247	0.018	Egypt
0.192	0.621	0.376	0.354	0.258	0.019	Fiji
0.188	0.525	0.335	0.353	0.245	0.017	Kyrgyzstan
0.188	0.579	0.353	0.392	0.253	0.018	French Guiana
0.187	0.532	0.340	0.359	0.250	0.017	Uzbekistan
0.185	0.524	0.323	0.361	0.240	0.017	Syrian Arab Republic
0.182	0.502	0.324	0.349	0.242	0.016	Libya
0.181	0.529	0.319	0.350	0.231	0.017	Lesotho
0.173	0.490	0.320	0.333	0.236	0.016	Mongolia
0.170	0.509	0.317	0.324	0.225	0.016	Djibouti
0.170	0.540	0.325	0.299	0.223	0.017	Samoa
0.168	0.527	0.314	0.325	0.227	0.017	Cambodia
0.164	0.470	0.292	0.310	0.212	0.015	Pakistan
0.158	0.468	0.273	0.303	0.200	0.015	Eritrea
0.157	0.401	0.277	0.285	0.204	0.014	Maldives
0.157	0.433	0.268	0.324	0.199	0.014	Mayotte
0.156	0.437	0.279	0.294	0.206	0.014	Jordan
0.155	0.482	0.289	0.296	0.209	0.015	Botswana
0.154	0.467	0.287	0.289	0.207	0.015	Lao People's Democratic Republic
0.152	0.458	0.279	0.285	0.196	0.015	Timor-Leste
0.146	0.413	0.282	0.281	0.214	0.014	Saudi Arabia
0.145	0.425	0.254	0.271	0.182	0.014	Eswatini
0.139	0.394	0.248	0.265	0.183	0.013	Namibia
0.135	0.400	0.302	0.246	0.219	0.014	Kuwait
0.134	0.400	0.244	0.254	0.178	0.013	Sudan
0.133	0.391	0.245	0.248	0.177	0.013	Gabon
0.130	0.381	0.231	0.244	0.170	0.013	Ethiopia
0.130	0.397	0.242	0.252	0.175	0.013	Solomon Islands
0.128	0.369	0.245	0.233	0.177	0.012	Tajikistan
0.127	0.384	0.236	0.239	0.171	0.013	Africa
0.125	0.378	0.229	0.240	0.171	0.013	Iraq
0.125	0.336	0.253	0.224	0.190	0.012	Bahrain
0.124	0.366	0.225	0.230	0.164	0.012	South Sudan
0.123	0.398	0.244	0.216	0.170	0.013	Vanuatu
0.122	0.401	0.247	0.227	0.176	0.013	Papua New Guinea
0.121	0.366	0.226	0.228	0.164	0.012	Liberia
0.121	0.360	0.222	0.229	0.162	0.012	Benin
0.120	0.357	0.224	0.224	0.163	0.012	Mauritania
0.120	0.355	0.222	0.220	0.162	0.012	State of Palestine
0.119	0.351	0.219	0.191	0.153	0.012	Sao Tome and Principe
0.118	0.488	0.278	0.250	0.193	0.016	Micronesia (Fed. States of)
0.118	0.306	0.222	0.222	0.173	0.011	Oman
0.118	0.398	0.260	0.214	0.184	0.013	Western Sahara
0.116	0.360	0.230	0.212	0.165	0.012	Ghana
0.116	0.346	0.216	0.218	0.159	0.012	Madagascar
0.113	0.349	0.220	0.217	0.159	0.012	Comoros
0.112	0.331	0.205	0.211	0.151	0.011	Zimbabwe
0.112	0.349	0.216	0.218	0.158	0.012	Rwanda
0.111	0.342	0.208	0.206	0.151	0.011	Senegal
0.110	0.331	0.203	0.205	0.148	0.011	Democratic Republic of the Congo
0.107	0.326	0.202	0.196	0.147	0.011	Yemen
0.106	0.326	0.203	0.195	0.147	0.011	Sierra Leone
0.103	0.394	0.239	0.217	0.170	0.013	Kiribati
0.103	0.315	0.192	0.189	0.139	0.011	Mozambique
0.102	0.319	0.192	0.189	0.139	0.011	Somalia
0.102	0.328	0.202	0.187	0.146	0.011	Togo
0.101	0.316	0.201	0.185	0.145	0.011	Congo
0.101	0.312	0.191	0.186	0.139	0.011	Central African Republic
0.100	0.325	0.194	0.185	0.139	0.011	Guinea
0.100	0.323	0.197	0.184	0.142	0.011	Côte d'Ivoire
0.098	0.305	0.189	0.178	0.137	0.011	Cameroon
0.097	0.321	0.192	0.178	0.137	0.011	Guinea-Bissau
0.097	0.295	0.183	0.176	0.133	0.010	Malawi
0.096	0.298	0.186	0.175	0.135	0.010	United Republic of Tanzania
0.096	0.298	0.184	0.175	0.134	0.010	Afghanistan
0.094	0.291	0.186	0.172	0.137	0.010	Kenya
0.094	0.309	0.190	0.171	0.136	0.011	Nigeria
0.092	0.279	0.179	0.167	0.131	0.010	Equatorial Guinea
0.091	0.286	0.177	0.162	0.127	0.010	Gambia
0.091	0.278	0.170	0.168	0.125	0.010	Chad
0.090	0.254	0.203	0.158	0.157	0.010	Qatar
0.088	0.288	0.172	0.162	0.124	0.010	Niger
0.088	0.275	0.173	0.160	0.126	0.010	Burkina Faso
0.088	0.270	0.169	0.161	0.124	0.010	Burundi
0.087	0.277	0.169	0.162	0.123	0.010	Mali
0.085	0.253	0.164	0.155	0.121	0.009	Angola
0.083	0.210	0.190	0.146	0.147	0.009	United Arab Emirates
0.083	0.246	0.158	0.150	0.118	0.009	Zambia
0.074	0.231	0.147	0.136	0.109	0.009	Uganda

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

Findings

The overall IFR estimates of COVID-19, with the exception of Levin et al., are relatively consistent with each other, usually within 30-40%. Levin et al. is often 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 1.274-1.605% (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 (ENE-COVID) IFR=0.127% for Africa, and IFR=0.810% in Europe, a 6-fold difference.

The overall IFR of COVID-19 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.

The IFR of COVID-19 is one order of magnitude (10×) higher than the seasonal flu for all regions. For example, in the US the average flu IFR is 0.078%, compared to 0.669-1.169% for COVID-19.

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

One of the largest serological prevalence surveys 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 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.