dedicated script to apply IFR to population pyramids
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Marc Bevand
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Author: Marc Bevand
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Author: Marc Bevand
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# Calculating the age-stratified infection fatality ratio (IFR) of COVID-19 from the Spanish ENE-COVID study
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This repository contains code to:
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* apply estimates of the age-stratified infection fatality ratio (IFR) of
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COVID-19 to countries' population pyramids, to find their expected overall IFR
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* calculate the age-stratified IFR from the Spanish ENE-COVID serosurvey
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# Age-stratified IFR applied to countries' population pyramids
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The script [apply_ifr.py](apply_ifr.py) uses a handful of age-stratified
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IFR estimates and applies them to countries' population pyramids, to
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find their overall IFR assuming equal prevalence of the disease among all
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age groups. IFR estimates come from:
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1. ENE-COVID Spanish serosurvey (calculated by `calc_ifr.py`, see next section)
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1. [US CDC](https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html) (table 1)
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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)
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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)
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1. [Humoral Immune Response to SARS-CoV-2 in Iceland](https://www.nejm.org/doi/full/10.1056/NEJMoa2026116),
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[Supplementary Appendix 1](https://www.nejm.org/doi/suppl/10.1056/NEJMoa2026116/suppl_file/nejmoa2026116_appendix_1.pdf) (table S7)
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Data for the population pyramids comes from the
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[United Nations](https://population.un.org/wpp/Download/Standard/Population/),
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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:
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[WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv](WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv)
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## Results
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The results are summarized in this table:
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| ENE-COV | US_CDC | Verity | Levin | Iceland | Region |
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| ------- | ------ | ------ | ----- | ------- | ------ |
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| 0.074 | 0.099 | 0.147 | 0.121 | 0.137 | Uganda |
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| 0.083 | 0.107 | 0.158 | 0.134 | 0.144 | Zambia |
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| 0.083 | 0.104 | 0.190 | 0.130 | 0.124 | United Arab Emirates |
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| 0.085 | 0.111 | 0.164 | 0.139 | 0.145 | Angola |
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| 0.087 | 0.117 | 0.169 | 0.145 | 0.147 | Mali |
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| 0.088 | 0.109 | 0.169 | 0.144 | 0.146 | Burundi |
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| 0.088 | 0.118 | 0.173 | 0.144 | 0.145 | Burkina Faso |
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| 0.088 | 0.121 | 0.172 | 0.146 | 0.147 | Niger |
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| 0.090 | 0.112 | 0.203 | 0.141 | 0.126 | Qatar |
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| 0.091 | 0.116 | 0.170 | 0.150 | 0.152 | Chad |
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| 0.091 | 0.126 | 0.177 | 0.146 | 0.149 | Gambia |
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| 0.092 | 0.118 | 0.179 | 0.149 | 0.149 | Equatorial Guinea |
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| 0.094 | 0.130 | 0.190 | 0.154 | 0.145 | Nigeria |
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| 0.094 | 0.122 | 0.186 | 0.153 | 0.148 | Kenya |
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| 0.096 | 0.128 | 0.184 | 0.156 | 0.151 | Afghanistan |
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| 0.096 | 0.129 | 0.186 | 0.157 | 0.152 | United Republic of Tanzania |
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| 0.097 | 0.129 | 0.183 | 0.157 | 0.155 | Malawi |
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| 0.097 | 0.130 | 0.192 | 0.160 | 0.150 | Guinea-Bissau |
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| 0.098 | 0.130 | 0.189 | 0.160 | 0.153 | Cameroon |
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| 0.100 | 0.135 | 0.197 | 0.165 | 0.152 | Côte d'Ivoire |
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| 0.100 | 0.134 | 0.194 | 0.166 | 0.154 | Guinea |
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| 0.101 | 0.133 | 0.191 | 0.166 | 0.156 | Central African Republic |
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| 0.101 | 0.137 | 0.201 | 0.166 | 0.152 | Congo |
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| 0.102 | 0.139 | 0.202 | 0.168 | 0.152 | Togo |
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| 0.102 | 0.135 | 0.192 | 0.168 | 0.158 | Somalia |
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| 0.103 | 0.136 | 0.192 | 0.168 | 0.160 | Mozambique |
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| 0.103 | 0.165 | 0.239 | 0.197 | 0.135 | Kiribati |
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| 0.106 | 0.141 | 0.203 | 0.174 | 0.159 | Sierra Leone |
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| 0.107 | 0.139 | 0.202 | 0.175 | 0.160 | Yemen |
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| 0.110 | 0.144 | 0.203 | 0.182 | 0.166 | Democratic Republic of the Congo |
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| 0.111 | 0.146 | 0.208 | 0.184 | 0.165 | Senegal |
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| 0.112 | 0.146 | 0.216 | 0.193 | 0.161 | Rwanda |
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| 0.112 | 0.142 | 0.205 | 0.188 | 0.169 | Zimbabwe |
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| 0.113 | 0.149 | 0.220 | 0.193 | 0.162 | Comoros |
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| 0.116 | 0.146 | 0.216 | 0.193 | 0.166 | Madagascar |
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| 0.116 | 0.157 | 0.230 | 0.190 | 0.159 | Ghana |
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| 0.118 | 0.160 | 0.260 | 0.195 | 0.143 | Western Sahara |
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| 0.118 | 0.138 | 0.222 | 0.195 | 0.165 | Oman |
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| 0.118 | 0.178 | 0.278 | 0.228 | 0.136 | Micronesia (Fed. States of) |
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| 0.119 | 0.150 | 0.219 | 0.173 | 0.170 | Sao Tome and Principe |
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| 0.120 | 0.157 | 0.222 | 0.196 | 0.171 | State of Palestine |
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| 0.120 | 0.155 | 0.224 | 0.199 | 0.170 | Mauritania |
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| 0.121 | 0.157 | 0.222 | 0.203 | 0.173 | Benin |
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| 0.121 | 0.157 | 0.226 | 0.203 | 0.171 | Liberia |
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| 0.122 | 0.165 | 0.247 | 0.204 | 0.160 | Papua New Guinea |
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| 0.123 | 0.168 | 0.244 | 0.196 | 0.163 | Vanuatu |
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| 0.124 | 0.163 | 0.225 | 0.204 | 0.176 | South Sudan |
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| 0.125 | 0.150 | 0.253 | 0.199 | 0.156 | Bahrain |
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| 0.125 | 0.157 | 0.229 | 0.212 | 0.176 | Iraq |
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| 0.127 | 0.165 | 0.236 | 0.212 | 0.176 | Africa |
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| 0.128 | 0.158 | 0.245 | 0.207 | 0.169 | Tajikistan |
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| 0.130 | 0.174 | 0.242 | 0.224 | 0.179 | Solomon Islands |
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| 0.130 | 0.167 | 0.231 | 0.216 | 0.183 | Ethiopia |
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| 0.133 | 0.173 | 0.245 | 0.221 | 0.182 | Gabon |
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| 0.134 | 0.173 | 0.244 | 0.224 | 0.183 | Sudan |
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| 0.135 | 0.176 | 0.302 | 0.222 | 0.145 | Kuwait |
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| 0.139 | 0.173 | 0.248 | 0.234 | 0.188 | Namibia |
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| 0.145 | 0.187 | 0.254 | 0.239 | 0.199 | Eswatini |
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| 0.146 | 0.180 | 0.282 | 0.248 | 0.177 | Saudi Arabia |
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| 0.152 | 0.209 | 0.279 | 0.253 | 0.197 | Timor-Leste |
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| 0.154 | 0.199 | 0.287 | 0.257 | 0.191 | Lao People's Democratic Republic |
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| 0.155 | 0.203 | 0.289 | 0.263 | 0.193 | Botswana |
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| 0.156 | 0.199 | 0.279 | 0.260 | 0.199 | Jordan |
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| 0.157 | 0.194 | 0.268 | 0.281 | 0.212 | Mayotte |
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| 0.157 | 0.183 | 0.277 | 0.252 | 0.203 | Maldives |
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| 0.158 | 0.204 | 0.273 | 0.268 | 0.208 | Eritrea |
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| 0.164 | 0.210 | 0.292 | 0.274 | 0.206 | Pakistan |
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| 0.168 | 0.221 | 0.314 | 0.288 | 0.199 | Cambodia |
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| 0.170 | 0.238 | 0.325 | 0.271 | 0.197 | Samoa |
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| 0.170 | 0.225 | 0.317 | 0.287 | 0.205 | Djibouti |
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| 0.173 | 0.216 | 0.320 | 0.294 | 0.201 | Mongolia |
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| 0.181 | 0.229 | 0.319 | 0.308 | 0.221 | Lesotho |
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| 0.182 | 0.226 | 0.324 | 0.307 | 0.216 | Libya |
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| 0.185 | 0.224 | 0.323 | 0.317 | 0.222 | Syrian Arab Republic |
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| 0.187 | 0.219 | 0.340 | 0.317 | 0.212 | Uzbekistan |
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| 0.188 | 0.245 | 0.353 | 0.344 | 0.216 | French Guiana |
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| 0.188 | 0.220 | 0.335 | 0.310 | 0.217 | Kyrgyzstan |
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| 0.192 | 0.262 | 0.376 | 0.318 | 0.206 | Fiji |
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| 0.192 | 0.247 | 0.343 | 0.321 | 0.224 | Egypt |
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| 0.193 | 0.222 | 0.339 | 0.316 | 0.220 | Turkmenistan |
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| 0.193 | 0.251 | 0.355 | 0.325 | 0.220 | South Africa |
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| 0.194 | 0.237 | 0.331 | 0.330 | 0.233 | Haiti |
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| 0.197 | 0.249 | 0.381 | 0.331 | 0.210 | Brunei Darussalam |
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| 0.202 | 0.267 | 0.360 | 0.344 | 0.232 | Nepal |
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| 0.204 | 0.255 | 0.360 | 0.342 | 0.232 | Philippines |
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| 0.204 | 0.233 | 0.323 | 0.349 | 0.253 | Guatemala |
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| 0.206 | 0.235 | 0.331 | 0.353 | 0.249 | Honduras |
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| 0.208 | 0.235 | 0.334 | 0.351 | 0.244 | Belize |
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| 0.215 | 0.277 | 0.401 | 0.366 | 0.228 | Myanmar |
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| 0.217 | 0.284 | 0.375 | 0.310 | 0.247 | Tonga |
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| 0.221 | 0.232 | 0.349 | 0.374 | 0.260 | Cabo Verde |
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| 0.221 | 0.269 | 0.368 | 0.374 | 0.255 | Bangladesh |
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| 0.228 | 0.259 | 0.373 | 0.393 | 0.262 | Nicaragua |
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| 0.229 | 0.293 | 0.416 | 0.387 | 0.244 | Indonesia |
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| 0.234 | 0.293 | 0.414 | 0.398 | 0.253 | India |
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| 0.246 | 0.301 | 0.431 | 0.418 | 0.263 | Iran (Islamic Republic of) |
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| 0.251 | 0.297 | 0.403 | 0.423 | 0.288 | Bhutan |
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| 0.257 | 0.307 | 0.418 | 0.436 | 0.287 | Paraguay |
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| 0.260 | 0.305 | 0.426 | 0.446 | 0.289 | Algeria |
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| 0.263 | 0.325 | 0.453 | 0.442 | 0.279 | Malaysia |
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| 0.269 | 0.358 | 0.495 | 0.460 | 0.272 | Bahamas |
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| 0.272 | 0.337 | 0.474 | 0.462 | 0.284 | Morocco |
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| 0.273 | 0.313 | 0.474 | 0.452 | 0.279 | Azerbaijan |
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| 0.280 | 0.335 | 0.463 | 0.469 | 0.298 | Suriname |
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| 0.282 | 0.326 | 0.456 | 0.456 | 0.298 | Guyana |
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| 0.287 | 0.384 | 0.527 | 0.513 | 0.286 | French Polynesia |
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| 0.293 | 0.400 | 0.542 | 0.477 | 0.284 | Seychelles |
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| 0.295 | 0.346 | 0.482 | 0.501 | 0.317 | Lebanon |
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| 0.303 | 0.347 | 0.474 | 0.525 | 0.329 | Ecuador |
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| 0.305 | 0.365 | 0.502 | 0.526 | 0.317 | Venezuela (Bolivarian Republic of) |
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| 0.306 | 0.352 | 0.485 | 0.532 | 0.327 | Mexico |
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| 0.307 | 0.348 | 0.503 | 0.505 | 0.319 | Kazakhstan |
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| 0.309 | 0.348 | 0.480 | 0.532 | 0.333 | Dominican Republic |
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| 0.310 | 0.350 | 0.463 | 0.535 | 0.346 | Bolivia (Plurinational State of) |
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| 0.325 | 0.394 | 0.541 | 0.559 | 0.331 | Asia |
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| 0.327 | 0.432 | 0.573 | 0.547 | 0.328 | New Caledonia |
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| 0.336 | 0.394 | 0.553 | 0.579 | 0.344 | Tunisia |
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| 0.339 | 0.397 | 0.537 | 0.583 | 0.358 | Peru |
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| 0.342 | 0.408 | 0.552 | 0.588 | 0.356 | Turkey |
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| 0.343 | 0.363 | 0.528 | 0.602 | 0.355 | Viet Nam |
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| 0.347 | 0.399 | 0.528 | 0.595 | 0.377 | El Salvador |
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| 0.354 | 0.438 | 0.595 | 0.642 | 0.368 | Grenada |
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| 0.355 | 0.412 | 0.560 | 0.613 | 0.367 | Colombia |
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| 0.356 | 0.410 | 0.555 | 0.614 | 0.371 | Latin America and the Caribbean |
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| **0.358 | 0.418 | 0.566 | 0.615 | 0.370 | WORLD** |
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| 0.359 | 0.399 | 0.540 | 0.623 | 0.380 | Panama |
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| 0.361 | 0.454 | 0.605 | 0.663 | 0.375 | Saint Vincent and the Grenadines |
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| 0.364 | 0.425 | 0.569 | 0.627 | 0.381 | Jamaica |
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| 0.365 | 0.464 | 0.611 | 0.618 | 0.373 | Guam |
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| 0.376 | 0.437 | 0.594 | 0.645 | 0.382 | Brazil |
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| 0.377 | 0.487 | 0.635 | 0.622 | 0.373 | Dem. People's Republic of Korea |
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| 0.387 | 0.495 | 0.651 | 0.668 | 0.377 | Sri Lanka |
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| 0.394 | 0.497 | 0.648 | 0.712 | 0.409 | Antigua and Barbuda |
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| 0.409 | 0.507 | 0.695 | 0.707 | 0.385 | China |
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| 0.413 | 0.468 | 0.626 | 0.704 | 0.413 | Saint Lucia |
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| 0.413 | 0.475 | 0.638 | 0.712 | 0.417 | Costa Rica |
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| 0.415 | 0.511 | 0.681 | 0.717 | 0.402 | Trinidad and Tobago |
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| 0.435 | 0.501 | 0.721 | 0.766 | 0.413 | Republic of Moldova |
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| 0.447 | 0.544 | 0.732 | 0.772 | 0.425 | Mauritius |
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| 0.449 | 0.514 | 0.738 | 0.770 | 0.412 | China, Macao SAR |
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| 0.454 | 0.515 | 0.671 | 0.790 | 0.471 | Argentina |
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| 0.479 | 0.550 | 0.796 | 0.829 | 0.433 | Singapore |
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| 0.493 | 0.550 | 0.736 | 0.861 | 0.492 | Chile |
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| 0.497 | 0.616 | 0.816 | 0.851 | 0.474 | North Macedonia |
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| 0.497 | 0.522 | 0.748 | 0.794 | 0.491 | Armenia |
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| 0.499 | 0.545 | 0.710 | 0.872 | 0.516 | Israel |
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| 0.500 | 0.569 | 0.763 | 0.880 | 0.493 | Réunion |
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| 0.510 | 0.588 | 0.795 | 0.881 | 0.491 | Thailand |
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| 0.518 | 0.580 | 0.754 | 0.902 | 0.526 | Oceania |
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| 0.531 | 0.652 | 0.874 | 0.920 | 0.508 | Aruba |
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| 0.548 | 0.659 | 0.857 | 0.941 | 0.533 | Albania |
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| 0.561 | 0.649 | 0.839 | 0.957 | 0.559 | Cyprus |
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| 0.563 | 0.653 | 0.838 | 0.972 | 0.559 | Ireland |
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| 0.582 | 0.667 | 0.891 | 0.989 | 0.567 | Montenegro |
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| 0.604 | 0.709 | 0.935 | 1.059 | 0.580 | Slovakia |
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| 0.605 | 0.683 | 0.888 | 1.061 | 0.599 | Iceland |
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| 0.607 | 0.670 | 0.896 | 1.002 | 0.598 | Georgia |
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| 0.615 | 0.668 | 0.881 | 1.078 | 0.616 | Luxembourg |
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| 0.624 | 0.657 | 0.921 | 1.082 | 0.606 | Belarus |
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| 0.624 | 0.663 | 0.916 | 1.030 | 0.606 | Russian Federation |
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| 0.628 | 0.686 | 0.938 | 1.100 | 0.599 | China, Taiwan Province of China |
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| 0.633 | 0.725 | 0.963 | 1.090 | 0.606 | Republic of Korea |
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| 0.641 | 0.877 | 1.059 | 1.152 | 0.608 | United States Virgin Islands |
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| 0.644 | 0.735 | 0.941 | 1.131 | 0.630 | Cuba |
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| 0.654 | 0.742 | 0.945 | 1.137 | 0.645 | New Zealand |
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| 0.659 | 0.757 | 1.011 | 1.088 | 0.620 | Bosnia and Herzegovina |
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| 0.669 | 0.738 | 0.958 | 1.169 | 0.654 | United States of America |
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| 0.671 | 0.733 | 0.983 | 1.103 | 0.651 | Ukraine |
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| 0.671 | 0.736 | 0.944 | 1.174 | 0.670 | Australia |
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| 0.675 | 0.745 | 0.967 | 1.181 | 0.660 | Northern America |
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| 0.676 | 0.694 | 0.904 | 1.193 | 0.686 | Uruguay |
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| 0.680 | 0.800 | 1.029 | 1.160 | 0.659 | Serbia |
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| 0.709 | 0.802 | 1.007 | 1.239 | 0.699 | Norway |
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| 0.713 | 0.808 | 1.038 | 1.210 | 0.694 | Curaçao |
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| 0.719 | 0.756 | 0.998 | 1.259 | 0.705 | Barbados |
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| 0.734 | 0.810 | 1.047 | 1.288 | 0.714 | Canada |
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| 0.738 | 0.878 | 1.092 | 1.302 | 0.717 | Czechia |
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| 0.739 | 0.828 | 1.059 | 1.315 | 0.737 | Channel Islands |
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| 0.745 | 0.800 | 1.064 | 1.285 | 0.724 | Poland |
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| 0.753 | 0.854 | 1.101 | 1.323 | 0.732 | Hungary |
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| 0.759 | 0.830 | 1.090 | 1.285 | 0.745 | Romania |
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| 0.791 | 0.811 | 1.102 | 1.367 | 0.761 | China, Hong Kong SAR |
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| 0.794 | 0.868 | 1.089 | 1.384 | 0.794 | United Kingdom |
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| 0.797 | 0.935 | 1.136 | 1.370 | 0.782 | Denmark |
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| 0.798 | 0.864 | 1.113 | 1.399 | 0.779 | Guadeloupe |
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| 0.801 | 0.949 | 1.181 | 1.377 | 0.779 | Bulgaria |
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|
| 0.802 | 0.909 | 1.141 | 1.394 | 0.785 | Netherlands |
|
||||||
|
| 0.810 | 0.861 | 1.117 | 1.396 | 0.802 | Europe |
|
||||||
|
| 0.825 | 0.897 | 1.132 | 1.447 | 0.819 | Switzerland |
|
||||||
|
| 0.836 | 0.910 | 1.149 | 1.462 | 0.830 | Austria |
|
||||||
|
| 0.840 | 0.943 | 1.154 | 1.471 | 0.838 | Sweden |
|
||||||
|
| 0.843 | 0.905 | 1.181 | 1.489 | 0.829 | Slovenia |
|
||||||
|
| 0.846 | 0.881 | 1.137 | 1.478 | 0.847 | Belgium |
|
||||||
|
| 0.855 | 0.965 | 1.205 | 1.471 | 0.835 | Malta |
|
||||||
|
| 0.865 | 0.932 | 1.209 | 1.487 | 0.855 | Croatia |
|
||||||
|
| 0.868 | 0.892 | 1.171 | 1.516 | 0.870 | Estonia |
|
||||||
|
| 0.875 | 0.957 | 1.205 | 1.528 | 0.857 | Puerto Rico |
|
||||||
|
| 0.881 | 0.919 | 1.192 | 1.624 | 0.870 | Latvia |
|
||||||
|
| 0.899 | 1.008 | 1.248 | 1.556 | 0.883 | Finland |
|
||||||
|
| 0.914 | 0.941 | 1.201 | 1.606 | 0.913 | France |
|
||||||
|
| 0.916 | 0.942 | 1.207 | 1.648 | 0.921 | Spain |
|
||||||
|
| 0.919 | 0.933 | 1.221 | 1.658 | 0.918 | Lithuania |
|
||||||
|
| 0.931 | 0.985 | 1.270 | 1.648 | 0.918 | Martinique |
|
||||||
|
| 0.984 | 1.045 | 1.320 | 1.726 | 0.986 | Portugal |
|
||||||
|
| 0.993 | 1.012 | 1.305 | 1.702 | 0.994 | Germany |
|
||||||
|
| 1.041 | 1.043 | 1.339 | 1.806 | 1.057 | Greece |
|
||||||
|
| 1.065 | 1.092 | 1.382 | 1.847 | 1.068 | Italy |
|
||||||
|
| 1.274 | 1.311 | 1.605 | 2.247 | 1.283 | Japan |
|
||||||
|
| ------- | ------ | ------ | ----- | ------- | ------ |
|
||||||
|
| ENE-COV | US_CDC | Verity | Levin | Iceland | Region |
|
||||||
|
|
||||||
|
## 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
|
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
|
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
|
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
|
June to calculate the overall and age-stratified IFR of COVID-19 with the
|
||||||
Python script `calc_ifr.py`:
|
Python script [calc_ifr.py](calc_ifr.py):
|
||||||
|
|
||||||
```
|
```
|
||||||
$ ./calc_ifr.py
|
$ ./calc_ifr.py
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
Executable
+171
@@ -0,0 +1,171 @@
|
|||||||
|
#!/usr/bin/python3
|
||||||
|
#
|
||||||
|
# Apply various estimates of the age-stratified Infection Fatality Ratio of COVID-19 to
|
||||||
|
# countries' population pyramids in order to calculate their overall IFR.
|
||||||
|
# Author: Marc Bevand — @zorinaq
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
# Pyramid data is from the United Nations: this file is a CSV export of the first sheet
|
||||||
|
# of "Population by Age Groups - Both Sexes" linked from:
|
||||||
|
# https://population.un.org/wpp/Download/Standard/Population/
|
||||||
|
# Direct link:
|
||||||
|
# https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx
|
||||||
|
file_pyramids = 'WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.csv'
|
||||||
|
|
||||||
|
maxage = 100
|
||||||
|
|
||||||
|
# Age groups defined in the CSV file
|
||||||
|
age_groups = [(0,4), (5,9), (10,14), (15,19), (20,24), (25,29), (30,34), (35,39), (40,44), (45,49), (50,54), (55,59), (60,64), (65,69), (70,74), (75,79), (80,84), (85,89), (90,94), (95,99), (100,maxage)]
|
||||||
|
|
||||||
|
# This will hold parsed pyramid data. Example to get the number of people in the
|
||||||
|
# age group 20-24 in France: pyramid['France'][(20,24)]
|
||||||
|
pyramid = {}
|
||||||
|
|
||||||
|
# Various age-stratified IFR estimates
|
||||||
|
ifrs = [
|
||||||
|
|
||||||
|
# Calculated from Spanish ENE-COVID study
|
||||||
|
# (see calc_ifr.py)
|
||||||
|
('ENE-COV', {
|
||||||
|
(0,9): 0.003,
|
||||||
|
(10,19): 0.004,
|
||||||
|
(20,29): 0.015,
|
||||||
|
(30,39): 0.030,
|
||||||
|
(40,49): 0.064,
|
||||||
|
(50,59): 0.213,
|
||||||
|
(60,69): 0.718,
|
||||||
|
(70,79): 2.384,
|
||||||
|
(80,89): 8.466,
|
||||||
|
(90,maxage): 12.497,
|
||||||
|
}),
|
||||||
|
|
||||||
|
# US CDC estimate as of 10 Sep 2020
|
||||||
|
# https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
|
||||||
|
# (table 1)
|
||||||
|
('US_CDC', {
|
||||||
|
(0,19): 0.003,
|
||||||
|
(20,49): 0.02,
|
||||||
|
(50,69): 0.5,
|
||||||
|
(70,maxage): 5.4,
|
||||||
|
}),
|
||||||
|
|
||||||
|
# Verity et al.
|
||||||
|
# https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext
|
||||||
|
# (table 1)
|
||||||
|
('Verity', {
|
||||||
|
(0,9): 0.00161,
|
||||||
|
(10,19): 0.00695,
|
||||||
|
(20,29): 0.0309,
|
||||||
|
(30,39): 0.0844,
|
||||||
|
(40,49): 0.161,
|
||||||
|
(50,59): 0.595,
|
||||||
|
(60,69): 1.93,
|
||||||
|
(70,79): 4.28,
|
||||||
|
(80,maxage): 7.80,
|
||||||
|
}),
|
||||||
|
|
||||||
|
# Levin et al.
|
||||||
|
# https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v5
|
||||||
|
# (table 3)
|
||||||
|
('Levin', {
|
||||||
|
(0,34): 0.004,
|
||||||
|
(35,44): 0.06,
|
||||||
|
(45,54): 0.2,
|
||||||
|
(55,64): 0.7,
|
||||||
|
(65,74): 2.3,
|
||||||
|
(75,84): 7.6,
|
||||||
|
(85,maxage): 22.3,
|
||||||
|
}),
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
('Iceland', {
|
||||||
|
(0,70): 0.1,
|
||||||
|
(71,80): 2.4,
|
||||||
|
(81,maxage): 11.2,
|
||||||
|
}),
|
||||||
|
|
||||||
|
]
|
||||||
|
|
||||||
|
def ag2str(age_group):
|
||||||
|
if age_group[1] == maxage:
|
||||||
|
return f'{age_group[0]}+'
|
||||||
|
return f'{age_group[0]}-{age_group[1]}'
|
||||||
|
|
||||||
|
def parse_pyramids():
|
||||||
|
df = pd.read_csv(file_pyramids)
|
||||||
|
# ignore labels as they don't contain any data
|
||||||
|
df = df[df['Type'] != 'Label/Separator']
|
||||||
|
# only take rows with data as of 2020
|
||||||
|
df = df[df['Reference date (as of 1 July)'] == 2020]
|
||||||
|
# only parse countries, world, and continents
|
||||||
|
df = df[df['Type'].isin(('Country/Area', 'World', 'Region'))]
|
||||||
|
# remove spaces used as thousands separators, and convert cell values to floats
|
||||||
|
columns = [ag2str(x) for x in age_groups]
|
||||||
|
for col in columns:
|
||||||
|
df[col] = df[col].str.replace('\s+', '').astype(float)
|
||||||
|
regions = list(df['Region, subregion, country or area *'])
|
||||||
|
#regions = ('France',)
|
||||||
|
for region in regions:
|
||||||
|
pyramid[region] = {}
|
||||||
|
df_region = df[df['Region, subregion, country or area *'] == region]
|
||||||
|
for ag in age_groups:
|
||||||
|
# values are in thousands
|
||||||
|
pyramid[region][ag] = 1000 * float(df_region[ag2str(ag)])
|
||||||
|
|
||||||
|
def people_of_age(pyramid_region, age):
|
||||||
|
# Returns the number of people of exact age 'age', given the provided age pyramid
|
||||||
|
for ((a, b), n) in pyramid_region.items():
|
||||||
|
if age in range(a, b + 1):
|
||||||
|
return n / float(b - a + 1)
|
||||||
|
|
||||||
|
def overall_ifr(pyramid_region, ifr_age_stratified):
|
||||||
|
pop = 0
|
||||||
|
deaths = 0
|
||||||
|
for (age_group, ifr) in ifr_age_stratified.items():
|
||||||
|
for age in range(age_group[0], age_group[1] + 1):
|
||||||
|
pop += people_of_age(pyramid_region, age)
|
||||||
|
deaths += people_of_age(pyramid_region, age) * ifr / 100.0
|
||||||
|
assert pop == sum(pyramid_region.values())
|
||||||
|
return 100.0 * deaths / pop
|
||||||
|
|
||||||
|
def calc_overall_ifrs():
|
||||||
|
oifrs = []
|
||||||
|
for region in pyramid.keys():
|
||||||
|
# The overall IFRs are appended to the array in the same order as listed in ifrs
|
||||||
|
tmp = []
|
||||||
|
for i in ifrs:
|
||||||
|
ifr_age_stratified = i[1]
|
||||||
|
# calculate the overall IFR for region, using IFR estimate ifr_age_stratified
|
||||||
|
tmp.append(overall_ifr(pyramid[region], ifr_age_stratified))
|
||||||
|
oifrs.append((region, *tmp))
|
||||||
|
return oifrs
|
||||||
|
|
||||||
|
def show_overall_ifrs(oifrs):
|
||||||
|
def header():
|
||||||
|
for i in ifrs:
|
||||||
|
print(f'{i[0]:>7} ', end='')
|
||||||
|
print('')
|
||||||
|
# Each entry in the oifrs array is a tuple:
|
||||||
|
# (<region_name>, <ifr_according_to_1st_estimate>, <ifr_according_to_2nd_estimate>, ...)
|
||||||
|
# Sort by element index 1, that is by <ifr_according_to_1st_estimate>
|
||||||
|
# To sort by region name, use index 0 (x[0])
|
||||||
|
oifrs.sort(key=lambda x: x[1])
|
||||||
|
header()
|
||||||
|
for region in oifrs:
|
||||||
|
for i in region[1:]:
|
||||||
|
print(f'{i:7.3f} ', end='')
|
||||||
|
print(f' {region[0]}')
|
||||||
|
header()
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parse_pyramids()
|
||||||
|
oifrs = calc_overall_ifrs()
|
||||||
|
show_overall_ifrs(oifrs)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
Reference in New Issue
Block a user