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*Updated: 27 Sep 2020*
Author: Marc Bevand
This repository contains code to:
* 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](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:
1. ENE-COVID Spanish serosurvey (calculated by `calc_ifr.py`, see next section)
1. [US CDC](https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html) (table 1)
1. [Verity et al.: Estimates of the severity of coronavirus disease 2019: a model-based analysis](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099%2820%2930243-7/fulltext) (table 1)
1. [Levin et al.: Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications](https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v5) (table 3)
1. [Gudbjartsson et al.: Humoral Immune Response to SARS-CoV-2 in Iceland](https://www.nejm.org/doi/full/10.1056/NEJMoa2026116),
[Supplementary Appendix 1](https://www.nejm.org/doi/suppl/10.1056/NEJMoa2026116/suppl_file/nejmoa2026116_appendix_1.pdf) (table S7)
Data for the population pyramids comes from the
[United Nations](https://population.un.org/wpp/Download/Standard/Population/),
specifically the first sheet of [Population by Age Groups - Both Sexes](https://population.un.org/wpp/Download/Files/1_Indicators%20%28Standard%29/EXCEL_FILES/1_Population/WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx). This excel file was converted to CSV format:
[WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv](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][sero] 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):
```
$ ./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][sero] (table 1)
1. *Total deaths* and *deaths per age bracket* from the [Ministry of Health's daily report for 29 May][daily] (table 2 and table 3)
1. *Population pyramid* for Spain, from [worldpopulationreview.com][wpop]
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][dailyalt], [29 May][daily]. 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][sero], page 3)
* 100% measured by the Ministry of Health ([serosurvey][sero], page 3)
* 99.2% measured by a [third-party][hoffman]
However the sensitivity is more uncertain:
* 97% claimed by the manufacturer ([serosurvey][sero], page 3)
* 79% measured by the Ministry of Health ([serosurvey][sero], page 3)
* 93.1% measured by a [third-party][hoffman]
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.
[sero]: https://www.mscbs.gob.es/ciudadanos/ene-covid/docs/ESTUDIO_ENE-COVID19_SEGUNDA_RONDA_INFORME_PRELIMINAR.pdf
[daily]: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_120_COVID-19.pdf
[dailyalt]: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_109_COVID-19.pdf
[wpop]: https://worldpopulationreview.com/countries/spain-population/
[hoffman]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178815/
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