Marc Bevand
423 lines
32 KiB
Markdown
423 lines
32 KiB
Markdown
*Updated: 22 Nov 2020*
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Author: Marc Bevand
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This project studies the age-stratified infection fatality ratio (IFR) of COVID-19:
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* compare COVID-19 to seasonal influenza (flu)
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* calculate the expected overall IFR based on countries' population pyramids
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* calculate the age-stratified IFR of COVID-19 from the Spanish ENE-COVID serosurvey
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# Comparing COVID-19 to seasonal influenza
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The above chart compares the IFR of COVID-19 to the IFR of seasonal influenza. We
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find that COVID-19 is definitely significantly more fatal than influenza at all
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ages above 30 years. The source code producing this chart is
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[covid_vs_flu.py](covid_vs_flu.py).
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The vertical indicators represent the difference in fatality between COVID-19
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and influenza at various ages, from 30 to 80 years at 10-year intervals. The
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top/bottom of the indicators are anchored at the geometric means of the
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COVID-19/influenza IFR estimates.
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The COVID-19 IFR curves represent these estimates:
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1. ENE-COVID Spanish serosurvey (calculated by `calc_ifr.py`, see [this section](#calculating-the-age-stratified-ifr-of-covid-19-from-the-spanish-ene-covid-study))
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1. [US CDC COVID-19 Pandemic Planning Scenarios](https://web.archive.org/web/20200911222029/https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html) (table 1);
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which is based on [Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003189)
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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/10.1101/2020.07.23.20160895v7) (table 3)
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1. [Perez-Saez et al.: Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30584-3/fulltext)
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1. [Poletti et al.: Age-specific SARS-CoV-2 infection fatality ratio and associated risk factors, Italy, February to April 2020](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.31.2001383) (table 1, column "Any time")
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1. [Picon et al.: Coronavirus Disease 2019 Population-based Prevalence, Risk Factors, Hospitalization, and Fatality Rates in Southern Brazil](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493765/) (table 2)
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1. [Gudbjartsson et al.: Humoral Immune Response to SARS-CoV-2 in Iceland](https://www.nejm.org/doi/full/10.1056/NEJMoa2026116),
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specifically [Supplementary Appendix 1](https://www.nejm.org/doi/suppl/10.1056/NEJMoa2026116/suppl_file/nejmoa2026116_appendix_1.pdf) (table S7)
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1. [PHAS - Public Health Agency of Sweden: The infection fatality rate of COVID-19 in Stockholm – Technical report](https://www.folkhalsomyndigheten.se/contentassets/53c0dc391be54f5d959ead9131edb771/infection-fatality-rate-covid-19-stockholm-technical-report.pdf) (table B.1)
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1. [O’Driscoll et al.: Age-specific mortality and immunity patterns of SARS-CoV-2](https://www.nature.com/articles/s41586-020-2918-0) (table S3)
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1. [Ward et al.: Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults](https://www.medrxiv.org/content/10.1101/2020.08.12.20173690v2),
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specifically [Supplementary Appendix](https://www.medrxiv.org/highwire/filestream/93745/field_highwire_adjunct_files/0/2020.08.12.20173690-1.docx) (table S2a, column "Based on confirmed COVID-19 deaths")
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1. [Yang et al.: Estimating the infection fatality risk of COVID-19 in New York City during the spring 2020 pandemic wave](https://www.medrxiv.org/content/10.1101/2020.06.27.20141689v2) (table 1)
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1. [Molenberghs et al.: Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates](https://www.medrxiv.org/content/10.1101/2020.06.20.20136234v1) (table 6)
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The seasonal influenza IFR curves represent data from the US CDC on multiple seasons of flu:
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1. [2019-2020 influenza burden](https://www.cdc.gov/flu/about/burden/2019-2020.html)
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1. [2018-2019 influenza burden](https://www.cdc.gov/flu/about/burden/2018-2019.html)
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1. [2017-2018 influenza burden](https://www.cdc.gov/flu/about/burden/2017-2018.htm)
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1. [2016-2017 influenza burden](https://www.cdc.gov/flu/about/burden/2016-2017.html)
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1. [2015-2016 influenza burden](https://www.cdc.gov/flu/about/burden/2015-2016.html)
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1. [2014-2015 influenza burden](https://www.cdc.gov/flu/about/burden/2014-2015.html)
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However, these CDC statistics (eg. table 1 in "2018-2019 influenza burden",)
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only give the estimated number of symptomatic illnesses. We must account for
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asymptomatic ones as well to calculate the IFR.
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In [Key Facts About Influenza
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(Flu)](https://www.cdc.gov/flu/about/keyfacts.htm) the CDC implies 55-60% of
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illnesses are symptomatic:
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> «on average, about 8% of the U.S. population gets sick from flu each season,
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> with a range of between 3% and 11%, depending on the season.
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> [...]
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> The commonly cited 5% to 20% estimate was based on a study that examined both
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> symptomatic and asymptomatic influenza illness, which means it also looked at
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> people who may have had the flu but never knew it because they didn’t have
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> any symptoms. The 3% to 11% range is an estimate of the proportion of people
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> who have symptomatic flu illness.»
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Thus, the CDC acknowledges that 55-60% of illnesses are symptomatic (3/5 = 60%,
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and 11/20 = 55%.) We use the mid-point, 57.5%, to infer the number of asymptomatic
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illnesses:
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total_illnesses = symptomatic_illnesses / .575
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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 for COVID-19 and the seasonal flu and
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applies them to countries' population pyramids, to find their expected average
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IFR. The calculation assumes equal prevalence of the disease among all age groups.
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IFR estimates are a subset of the same sources as in `covid_vs_flu.py`. The flu
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IFR is from the US CDC (last flu season, 2019-2020.)
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The real-world overall IFR will, of course, dependent on many factors: varying
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prevalence among age groups, underlying health conditions, access to
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healthcare, socioeconomic status, ethnicity, etc.
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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 overall expected IFR percentages are summarized in this table (sorted on the
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ENE-COVID column):
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| ENE-COVID | COVID: US CDC | COVID: Verity | COVID: Levin | Flu: US CDC | Region |
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| ------------- | ------------- | ------------- | ------------- | ------------- | ------ |
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| 1.274 | 1.311 | 1.605 | 2.660 | 0.125 | Japan |
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| 1.065 | 1.092 | 1.382 | 2.177 | 0.106 | Italy |
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| 1.041 | 1.043 | 1.339 | 2.135 | 0.102 | Greece |
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| 0.993 | 1.012 | 1.305 | 1.997 | 0.100 | Germany |
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| 0.984 | 1.045 | 1.320 | 2.027 | 0.104 | Portugal |
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| 0.931 | 0.985 | 1.270 | 1.938 | 0.100 | Martinique |
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| 0.919 | 0.933 | 1.221 | 1.953 | 0.095 | Lithuania |
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| 0.916 | 0.942 | 1.207 | 1.950 | 0.093 | Spain |
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| 0.914 | 0.941 | 1.201 | 1.899 | 0.095 | France |
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| 0.899 | 1.008 | 1.248 | 1.819 | 0.102 | Finland |
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| 0.881 | 0.919 | 1.192 | 1.910 | 0.095 | Latvia |
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| 0.875 | 0.957 | 1.205 | 1.789 | 0.096 | Puerto Rico |
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| 0.868 | 0.892 | 1.171 | 1.773 | 0.093 | Estonia |
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| 0.865 | 0.932 | 1.209 | 1.733 | 0.098 | Croatia |
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| 0.855 | 0.965 | 1.205 | 1.716 | 0.099 | Malta |
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| 0.846 | 0.881 | 1.137 | 1.740 | 0.089 | Belgium |
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| 0.843 | 0.905 | 1.181 | 1.740 | 0.095 | Slovenia |
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| 0.840 | 0.943 | 1.154 | 1.722 | 0.093 | Sweden |
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| 0.836 | 0.910 | 1.149 | 1.709 | 0.090 | Austria |
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| 0.825 | 0.897 | 1.132 | 1.697 | 0.089 | Switzerland |
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| 0.810 | 0.861 | 1.117 | 1.633 | 0.089 | Europe |
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| 0.802 | 0.909 | 1.141 | 1.625 | 0.093 | Netherlands |
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| 0.801 | 0.949 | 1.181 | 1.592 | 0.098 | Bulgaria |
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| 0.798 | 0.864 | 1.113 | 1.637 | 0.090 | Guadeloupe |
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| 0.797 | 0.935 | 1.136 | 1.593 | 0.093 | Denmark |
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| 0.794 | 0.868 | 1.089 | 1.622 | 0.087 | United Kingdom |
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| 0.791 | 0.811 | 1.102 | 1.607 | 0.087 | China, Hong Kong SAR |
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| 0.759 | 0.830 | 1.090 | 1.494 | 0.089 | Romania |
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| 0.753 | 0.854 | 1.101 | 1.534 | 0.093 | Hungary |
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| 0.745 | 0.800 | 1.064 | 1.502 | 0.087 | Poland |
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| 0.739 | 0.828 | 1.059 | 1.538 | 0.084 | Channel Islands |
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| 0.738 | 0.878 | 1.092 | 1.510 | 0.092 | Czechia |
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| 0.734 | 0.810 | 1.047 | 1.505 | 0.085 | Canada |
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| 0.719 | 0.756 | 0.998 | 1.480 | 0.079 | Barbados |
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| 0.713 | 0.808 | 1.038 | 1.403 | 0.084 | Curaçao |
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| 0.709 | 0.802 | 1.007 | 1.447 | 0.082 | Norway |
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| 0.680 | 0.800 | 1.029 | 1.337 | 0.088 | Serbia |
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| 0.676 | 0.694 | 0.904 | 1.407 | 0.071 | Uruguay |
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| 0.675 | 0.745 | 0.967 | 1.379 | 0.079 | Northern America |
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| 0.671 | 0.736 | 0.944 | 1.374 | 0.076 | Australia |
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| 0.671 | 0.733 | 0.983 | 1.271 | 0.080 | Ukraine |
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| 0.669 | 0.738 | 0.958 | 1.365 | 0.078 | United States of America |
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| 0.659 | 0.757 | 1.011 | 1.243 | 0.085 | Bosnia and Herzegovina |
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| 0.654 | 0.742 | 0.945 | 1.323 | 0.077 | New Zealand |
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| 0.644 | 0.735 | 0.941 | 1.314 | 0.077 | Cuba |
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| 0.641 | 0.877 | 1.059 | 1.308 | 0.094 | United States Virgin Islands |
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| 0.633 | 0.725 | 0.963 | 1.259 | 0.077 | Republic of Korea |
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| 0.628 | 0.686 | 0.938 | 1.280 | 0.077 | China, Taiwan Province of China |
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| 0.624 | 0.663 | 0.916 | 1.191 | 0.074 | Russian Federation |
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| 0.624 | 0.657 | 0.921 | 1.252 | 0.075 | Belarus |
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| 0.615 | 0.668 | 0.881 | 1.261 | 0.070 | Luxembourg |
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| 0.607 | 0.670 | 0.896 | 1.155 | 0.073 | Georgia |
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| 0.605 | 0.683 | 0.888 | 1.236 | 0.073 | Iceland |
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| 0.604 | 0.709 | 0.935 | 1.222 | 0.079 | Slovakia |
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| 0.582 | 0.667 | 0.891 | 1.138 | 0.075 | Montenegro |
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| 0.563 | 0.653 | 0.838 | 1.126 | 0.070 | Ireland |
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| 0.561 | 0.649 | 0.839 | 1.106 | 0.069 | Cyprus |
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| 0.548 | 0.659 | 0.857 | 1.079 | 0.071 | Albania |
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| 0.531 | 0.652 | 0.874 | 1.049 | 0.072 | Aruba |
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| 0.518 | 0.580 | 0.754 | 1.052 | 0.062 | Oceania |
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| 0.510 | 0.588 | 0.795 | 1.017 | 0.065 | Thailand |
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| 0.500 | 0.569 | 0.763 | 1.020 | 0.063 | Réunion |
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| 0.499 | 0.545 | 0.710 | 1.021 | 0.060 | Israel |
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| 0.497 | 0.522 | 0.748 | 0.915 | 0.059 | Armenia |
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| 0.497 | 0.616 | 0.816 | 0.968 | 0.070 | North Macedonia |
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| 0.493 | 0.550 | 0.736 | 1.001 | 0.061 | Chile |
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| 0.479 | 0.550 | 0.796 | 0.952 | 0.067 | Singapore |
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| 0.454 | 0.515 | 0.671 | 0.917 | 0.056 | Argentina |
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| 0.449 | 0.514 | 0.738 | 0.887 | 0.062 | China, Macao SAR |
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| 0.447 | 0.544 | 0.732 | 0.885 | 0.063 | Mauritius |
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| 0.435 | 0.501 | 0.721 | 0.876 | 0.062 | Republic of Moldova |
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| 0.415 | 0.511 | 0.681 | 0.820 | 0.058 | Trinidad and Tobago |
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| 0.413 | 0.475 | 0.638 | 0.823 | 0.053 | Costa Rica |
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| 0.413 | 0.468 | 0.626 | 0.818 | 0.051 | Saint Lucia |
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| 0.409 | 0.507 | 0.695 | 0.806 | 0.061 | China |
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| 0.394 | 0.497 | 0.648 | 0.822 | 0.052 | Antigua and Barbuda |
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| 0.387 | 0.495 | 0.651 | 0.762 | 0.056 | Sri Lanka |
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| 0.377 | 0.487 | 0.635 | 0.706 | 0.050 | Dem. People's Republic of Korea |
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| 0.376 | 0.437 | 0.594 | 0.743 | 0.050 | Brazil |
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| 0.365 | 0.464 | 0.611 | 0.703 | 0.052 | Guam |
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| 0.364 | 0.425 | 0.569 | 0.725 | 0.047 | Jamaica |
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| 0.361 | 0.454 | 0.605 | 0.764 | 0.051 | Saint Vincent and the Grenadines |
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| 0.359 | 0.399 | 0.540 | 0.724 | 0.045 | Panama |
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| 0.358 | 0.418 | 0.566 | 0.709 | 0.048 | WORLD |
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| 0.356 | 0.410 | 0.555 | 0.710 | 0.047 | Latin America and the Caribbean |
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| 0.355 | 0.412 | 0.560 | 0.707 | 0.047 | Colombia |
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| 0.354 | 0.438 | 0.595 | 0.740 | 0.050 | Grenada |
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| 0.347 | 0.399 | 0.528 | 0.687 | 0.045 | El Salvador |
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| 0.343 | 0.363 | 0.528 | 0.701 | 0.043 | Viet Nam |
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| 0.342 | 0.408 | 0.552 | 0.675 | 0.047 | Turkey |
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| 0.339 | 0.397 | 0.537 | 0.671 | 0.045 | Peru |
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| 0.336 | 0.394 | 0.553 | 0.664 | 0.047 | Tunisia |
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| 0.327 | 0.432 | 0.573 | 0.616 | 0.049 | New Caledonia |
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| 0.325 | 0.394 | 0.541 | 0.640 | 0.047 | Asia |
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| 0.310 | 0.350 | 0.463 | 0.622 | 0.039 | Bolivia (Plurinational State of) |
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| 0.309 | 0.348 | 0.480 | 0.616 | 0.040 | Dominican Republic |
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| 0.307 | 0.348 | 0.503 | 0.574 | 0.042 | Kazakhstan |
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| 0.306 | 0.352 | 0.485 | 0.615 | 0.041 | Mexico |
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| 0.305 | 0.365 | 0.502 | 0.604 | 0.042 | Venezuela (Bolivarian Republic of) |
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| 0.303 | 0.347 | 0.474 | 0.607 | 0.040 | Ecuador |
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| 0.295 | 0.346 | 0.482 | 0.576 | 0.041 | Lebanon |
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| 0.293 | 0.400 | 0.542 | 0.527 | 0.045 | Seychelles |
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| 0.287 | 0.384 | 0.527 | 0.579 | 0.046 | French Polynesia |
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| 0.282 | 0.326 | 0.456 | 0.521 | 0.038 | Guyana |
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| 0.280 | 0.335 | 0.463 | 0.536 | 0.039 | Suriname |
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| 0.273 | 0.313 | 0.474 | 0.515 | 0.039 | Azerbaijan |
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| 0.272 | 0.337 | 0.474 | 0.522 | 0.041 | Morocco |
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| 0.269 | 0.358 | 0.495 | 0.517 | 0.042 | Bahamas |
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| 0.263 | 0.325 | 0.453 | 0.504 | 0.039 | Malaysia |
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| 0.260 | 0.305 | 0.426 | 0.512 | 0.037 | Algeria |
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| 0.257 | 0.307 | 0.418 | 0.500 | 0.037 | Paraguay |
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| 0.251 | 0.297 | 0.403 | 0.486 | 0.035 | Bhutan |
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| 0.246 | 0.301 | 0.431 | 0.476 | 0.037 | Iran (Islamic Republic of) |
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| 0.234 | 0.293 | 0.414 | 0.452 | 0.037 | India |
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| 0.229 | 0.293 | 0.416 | 0.437 | 0.036 | Indonesia |
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| 0.228 | 0.259 | 0.373 | 0.452 | 0.032 | Nicaragua |
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| 0.221 | 0.269 | 0.368 | 0.428 | 0.031 | Bangladesh |
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| 0.221 | 0.232 | 0.349 | 0.432 | 0.029 | Cabo Verde |
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| 0.217 | 0.284 | 0.375 | 0.342 | 0.032 | Tonga |
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| 0.215 | 0.277 | 0.401 | 0.412 | 0.036 | Myanmar |
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| 0.208 | 0.235 | 0.334 | 0.406 | 0.029 | Belize |
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| 0.206 | 0.235 | 0.331 | 0.407 | 0.029 | Honduras |
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| 0.204 | 0.233 | 0.323 | 0.403 | 0.029 | Guatemala |
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| 0.204 | 0.255 | 0.360 | 0.388 | 0.032 | Philippines |
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| 0.202 | 0.267 | 0.360 | 0.388 | 0.033 | Nepal |
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| 0.197 | 0.249 | 0.381 | 0.373 | 0.032 | Brunei Darussalam |
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| 0.194 | 0.237 | 0.331 | 0.376 | 0.030 | Haiti |
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| 0.193 | 0.251 | 0.355 | 0.366 | 0.032 | South Africa |
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| 0.193 | 0.222 | 0.339 | 0.360 | 0.029 | Turkmenistan |
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| 0.192 | 0.247 | 0.343 | 0.363 | 0.031 | Egypt |
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| 0.192 | 0.262 | 0.376 | 0.354 | 0.033 | Fiji |
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| 0.188 | 0.220 | 0.335 | 0.353 | 0.029 | Kyrgyzstan |
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| 0.188 | 0.245 | 0.353 | 0.392 | 0.031 | French Guiana |
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| 0.187 | 0.219 | 0.340 | 0.359 | 0.029 | Uzbekistan |
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| 0.185 | 0.224 | 0.323 | 0.361 | 0.029 | Syrian Arab Republic |
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| 0.182 | 0.226 | 0.324 | 0.349 | 0.028 | Libya |
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| 0.181 | 0.229 | 0.319 | 0.350 | 0.029 | Lesotho |
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| 0.173 | 0.216 | 0.320 | 0.333 | 0.027 | Mongolia |
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| 0.170 | 0.225 | 0.317 | 0.324 | 0.028 | Djibouti |
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| 0.170 | 0.238 | 0.325 | 0.299 | 0.029 | Samoa |
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| 0.168 | 0.221 | 0.314 | 0.325 | 0.029 | Cambodia |
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| 0.164 | 0.210 | 0.292 | 0.310 | 0.026 | Pakistan |
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| 0.158 | 0.204 | 0.273 | 0.303 | 0.026 | Eritrea |
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| 0.157 | 0.183 | 0.277 | 0.285 | 0.024 | Maldives |
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| 0.157 | 0.194 | 0.268 | 0.324 | 0.024 | Mayotte |
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| 0.156 | 0.199 | 0.279 | 0.294 | 0.025 | Jordan |
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| 0.155 | 0.203 | 0.289 | 0.296 | 0.027 | Botswana |
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| 0.154 | 0.199 | 0.287 | 0.289 | 0.026 | Lao People's Democratic Republic |
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| 0.152 | 0.209 | 0.279 | 0.285 | 0.025 | Timor-Leste |
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| 0.146 | 0.180 | 0.282 | 0.281 | 0.024 | Saudi Arabia |
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| 0.145 | 0.187 | 0.254 | 0.271 | 0.024 | Eswatini |
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| 0.139 | 0.173 | 0.248 | 0.265 | 0.023 | Namibia |
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| 0.135 | 0.176 | 0.302 | 0.246 | 0.024 | Kuwait |
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| 0.134 | 0.173 | 0.244 | 0.254 | 0.023 | Sudan |
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| 0.133 | 0.173 | 0.245 | 0.248 | 0.023 | Gabon |
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| 0.130 | 0.167 | 0.231 | 0.244 | 0.022 | Ethiopia |
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| 0.130 | 0.174 | 0.242 | 0.252 | 0.023 | Solomon Islands |
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| 0.128 | 0.158 | 0.245 | 0.233 | 0.022 | Tajikistan |
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| 0.127 | 0.165 | 0.236 | 0.239 | 0.022 | Africa |
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| 0.125 | 0.157 | 0.229 | 0.240 | 0.022 | Iraq |
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| 0.125 | 0.150 | 0.253 | 0.224 | 0.021 | Bahrain |
|
||
| 0.124 | 0.163 | 0.225 | 0.230 | 0.021 | South Sudan |
|
||
| 0.123 | 0.168 | 0.244 | 0.216 | 0.023 | Vanuatu |
|
||
| 0.122 | 0.165 | 0.247 | 0.227 | 0.023 | Papua New Guinea |
|
||
| 0.121 | 0.157 | 0.226 | 0.228 | 0.021 | Liberia |
|
||
| 0.121 | 0.157 | 0.222 | 0.229 | 0.021 | Benin |
|
||
| 0.120 | 0.155 | 0.224 | 0.224 | 0.021 | Mauritania |
|
||
| 0.120 | 0.157 | 0.222 | 0.220 | 0.021 | State of Palestine |
|
||
| 0.119 | 0.150 | 0.219 | 0.191 | 0.020 | Sao Tome and Principe |
|
||
| 0.118 | 0.178 | 0.278 | 0.250 | 0.027 | Micronesia (Fed. States of) |
|
||
| 0.118 | 0.138 | 0.222 | 0.222 | 0.020 | Oman |
|
||
| 0.118 | 0.160 | 0.260 | 0.214 | 0.024 | Western Sahara |
|
||
| 0.116 | 0.157 | 0.230 | 0.212 | 0.021 | Ghana |
|
||
| 0.116 | 0.146 | 0.216 | 0.218 | 0.020 | Madagascar |
|
||
| 0.113 | 0.149 | 0.220 | 0.217 | 0.021 | Comoros |
|
||
| 0.112 | 0.142 | 0.205 | 0.211 | 0.020 | Zimbabwe |
|
||
| 0.112 | 0.146 | 0.216 | 0.218 | 0.021 | Rwanda |
|
||
| 0.111 | 0.146 | 0.208 | 0.206 | 0.020 | Senegal |
|
||
| 0.110 | 0.144 | 0.203 | 0.205 | 0.019 | Democratic Republic of the Congo |
|
||
| 0.107 | 0.139 | 0.202 | 0.196 | 0.020 | Yemen |
|
||
| 0.106 | 0.141 | 0.203 | 0.195 | 0.019 | Sierra Leone |
|
||
| 0.103 | 0.165 | 0.239 | 0.217 | 0.023 | Kiribati |
|
||
| 0.103 | 0.136 | 0.192 | 0.189 | 0.019 | Mozambique |
|
||
| 0.102 | 0.135 | 0.192 | 0.189 | 0.019 | Somalia |
|
||
| 0.102 | 0.139 | 0.202 | 0.187 | 0.019 | Togo |
|
||
| 0.101 | 0.137 | 0.201 | 0.185 | 0.019 | Congo |
|
||
| 0.101 | 0.133 | 0.191 | 0.186 | 0.019 | Central African Republic |
|
||
| 0.100 | 0.134 | 0.194 | 0.185 | 0.019 | Guinea |
|
||
| 0.100 | 0.135 | 0.197 | 0.184 | 0.019 | Côte d'Ivoire |
|
||
| 0.098 | 0.130 | 0.189 | 0.178 | 0.018 | Cameroon |
|
||
| 0.097 | 0.130 | 0.192 | 0.178 | 0.019 | Guinea-Bissau |
|
||
| 0.097 | 0.129 | 0.183 | 0.176 | 0.018 | Malawi |
|
||
| 0.096 | 0.129 | 0.186 | 0.175 | 0.018 | United Republic of Tanzania |
|
||
| 0.096 | 0.128 | 0.184 | 0.175 | 0.018 | Afghanistan |
|
||
| 0.094 | 0.122 | 0.186 | 0.172 | 0.018 | Kenya |
|
||
| 0.094 | 0.130 | 0.190 | 0.171 | 0.019 | Nigeria |
|
||
| 0.092 | 0.118 | 0.179 | 0.167 | 0.018 | Equatorial Guinea |
|
||
| 0.091 | 0.126 | 0.177 | 0.162 | 0.017 | Gambia |
|
||
| 0.091 | 0.116 | 0.170 | 0.168 | 0.017 | Chad |
|
||
| 0.090 | 0.112 | 0.203 | 0.158 | 0.018 | Qatar |
|
||
| 0.088 | 0.121 | 0.172 | 0.162 | 0.017 | Niger |
|
||
| 0.088 | 0.118 | 0.173 | 0.160 | 0.017 | Burkina Faso |
|
||
| 0.088 | 0.109 | 0.169 | 0.161 | 0.017 | Burundi |
|
||
| 0.087 | 0.117 | 0.169 | 0.162 | 0.017 | Mali |
|
||
| 0.085 | 0.111 | 0.164 | 0.155 | 0.016 | Angola |
|
||
| 0.083 | 0.104 | 0.190 | 0.146 | 0.016 | United Arab Emirates |
|
||
| 0.083 | 0.107 | 0.158 | 0.150 | 0.016 | Zambia |
|
||
| 0.074 | 0.099 | 0.147 | 0.136 | 0.015 | 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%](https://web.archive.org/web/20200712055258/https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html)
|
||
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][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 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/
|