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mbevand
covid19-age-stratified-ifr
Python2020–2026
mbevand / covid19-age-stratified-ifr

covid19-age-stratified-ifr

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

4Distinct people
5Mentions
0Explicit recommenders
107GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

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

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year5 dated mentions

Observed mentions: 2020-11-10 10:09:55 UTC to 2026-05-17 20:21:05 UTC.

0122020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

5 sources
The best data summary I’ve seen [1] suggests that COVID-19 IFR for kids is 0.01% or less which is in line with the COVID-19 stats you cite. The same data summary [1] suggests that flu IFR for kids is probably the same order of magnitude but not “vastly less”. I think COVID-19 vs. flu has gotten way too much press though and it’s worth noting that IFR in kids is low for both relative to IFR in adults. What I would really want to learn more about are the long-term effects of disease and how that varies by age. We know a bit about that for flu or polio or RSV but not so much for COVID-19 because it’s still so new. [1]: https://github.com/mbevand/covid19-age-stratified-ifr
In The cruel, ridiculous reality of 'virtual learning'
Co-morbidities certainly have an impact on outcomes and explains why the IFR is much worse in certain parts of the world but IMHO it doesn't fully explain the magnitude lower attack rate/spread (50-100X worse in the US). That's still a mystery for which there are many plausible contributors, but I don't think it's so easy to just say it's obesity. In the US, this n=5700 NYC study showed 41.7% were obese [1] (not so different from the current US adult obesity rate is currently of about 42% [2]). Note that age is actually the most reliable predictor for hospitalization and death with COVID-19 [3][4] although you can make more accurate estimates with some other factors as well [5]. Note, based on the age-stratified pyramids, Japan's expected IFR sits right at the top [6], so even if you discount the undercounting of hospitalizations and deat
Read the rest
hs, there's still a huge disparity to make up. BTW, your argument on flu outbreaks actually lends credence that the mask mitigations have been extremely effective. Here's a recent NHK report on the flu numbers this year [7]: - "The number of patients with seasonal flu in Japan was below 100 during the first seven days of February. That's far below the levels seen before. ... Usually, seasonal flu peaks at this time of year, with the number of flu patients per week ranging between 100,000 and 200,000 people." Flu cases are 1000-2000X less than normal. I'm in Tokyo myself and paying close attention to how people are behaving (and comparing it to the footage and media I'm seeing from the US and other countries) and there's a huge difference in behavior from what I've seen over this past year. If you're disappointed by how the general population behaves here, I suspect you'd be horrified/stupefied by what's going on in the rest of the world. My conclusion is that on a population/public health level, it actually doesn't take all that much effort to contain the spread of the virus (elimination is much harder, although several countries have shown that it can be done, and have reaped the economic & social benefits of doing so). [1] Richardson, Safiya, Jamie S. Hirsch, Mangala Narasimhan, James M. Crawford, Thomas McGinn, Karina W. Davidson, and the Northwell COVID-19 Research Consortium, et al. “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.” JAMA 323, no. 20 (May 26, 2020): 2052. https://doi.org/10.1001/jama.2020.6775. [2] https://www.cdc.gov/obesity/data/adult.html [3] O’Driscoll, Megan, Gabriel Ribeiro Dos Santos, Lin Wang, Derek A. T. Cummings, Andrew S. Azman, Juliette Paireau, Arnaud Fontanet, Simon Cauchemez, and Henrik Salje. “Age-Specific Mortality and Immunity Patterns of SARS-CoV-2.” Nature 590, no. 7844 (February 2021): 140–45. https://doi.org/10.1038/s41586-020-2918-0. [4] Levin, Andrew T., William P. Hanage, Nana Owusu-Boaitey, Kensington B. Cochran, Seamus P. Walsh, and Gideon Meyerowitz-Katz. “Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Systematic Review, Meta-Analysis, and Public Policy Implications.” European Journal of Epidemiology 35, no. 12 (December 1, 2020): 1123–38. https://doi.org/10.1007/s10654-020-00698-1. [5] Ghisolfi, Selene, Ingvild Almås, Justin C Sandefur, Tillman von Carnap, Jesse Heitner, and Tessa Bold. “Predicted COVID-19 Fatality Rates Based on Age, Sex, Comorbidities and Health System Capacity.” BMJ Global Health 5, no. 9 (September 9, 2020). https://doi.org/10.1136/bmjgh-2020-003094. [6] https://github.com/mbevand/covid19-age-stratified-ifr#age-st... [7] https://www3.nhk.or.jp/nhkworld/en/news/20210215_26/
In Japan to stage Tokyo Olympics without overseas spectators

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