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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

Infection Fatality Ratio of COVID-19 vs. 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:

However the sensitivity is more uncertain:

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.

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Calculates the age-stratified infection fatality ratio (IFR) of COVID-19

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