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calthaus
ncov-cfr
R2020–2020
calthaus / ncov-cfr

ncov-cfr

R code and documentation to estimate case fatality ratio of COVID-19 from cases outside China

2Distinct people
2Mentions
0Explicit recommenders
91GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

R code and documentation to estimate case fatality ratio of COVID-19 from cases outside China

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 day2 dated mentions

Observed mentions: 2020-02-18 17:08:07 UTC to 2020-03-03 07:27:48 UTC.

012020-02-18 00:00:00 UTC to 2020-02-19 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsFeb 182020-02-19 00:00:00 UTC to 2020-02-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-20 00:00:00 UTC to 2020-02-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-21 00:00:00 UTC to 2020-02-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-22 00:00:00 UTC to 2020-02-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-23 00:00:00 UTC to 2020-02-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-24 00:00:00 UTC to 2020-02-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-25 00:00:00 UTC to 2020-02-26 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsFeb 252020-02-26 00:00:00 UTC to 2020-02-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-27 00:00:00 UTC to 2020-02-28 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-28 00:00:00 UTC to 2020-02-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-29 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-03-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-02 00:00:00 UTC to 2020-03-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-03 00:00:00 UTC to 2020-03-04 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 3

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
Feb 182020-02-18 00:00:00 UTC2020-02-19 00:00:00 UTC101
Feb 192020-02-19 00:00:00 UTC2020-02-20 00:00:00 UTC000
Feb 202020-02-20 00:00:00 UTC2020-02-21 00:00:00 UTC000
Feb 212020-02-21 00:00:00 UTC2020-02-22 00:00:00 UTC000
Feb 222020-02-22 00:00:00 UTC2020-02-23 00:00:00 UTC000
Feb 232020-02-23 00:00:00 UTC2020-02-24 00:00:00 UTC000
Feb 242020-02-24 00:00:00 UTC2020-02-25 00:00:00 UTC000
Feb 252020-02-25 00:00:00 UTC2020-02-26 00:00:00 UTC000
Feb 262020-02-26 00:00:00 UTC2020-02-27 00:00:00 UTC000
Feb 272020-02-27 00:00:00 UTC2020-02-28 00:00:00 UTC000
Feb 282020-02-28 00:00:00 UTC2020-02-29 00:00:00 UTC000
Feb 292020-02-29 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 12020-03-01 00:00:00 UTC2020-03-02 00:00:00 UTC000
Mar 22020-03-02 00:00:00 UTC2020-03-03 00:00:00 UTC000
Mar 32020-03-03 00:00:00 UTC2020-03-04 00:00:00 UTC101

What Hacker News says

2 sources
This NEJM article you referenced (https://www.nejm.org/doi/full/10.1056/NEJMe2002387) is a poor estimate of the Case Fatality Ratio. I'm perplexed it passed peer-review (I'm not even an expert, so why am I finding such glaring flaws?) It's citing two references (ref. 4 and ref. 5) that estimate CFR at respectively 2% and 1.4%, however both use the naïve CFR formula (deaths/cases) that's known to underestimate the CFR due to deaths lagging cases in a rapidly growing outbreak. I wrote about this bias and provided 5 peer-reviewed epidemiological papers that also document it, see: https://blog.zorinaq.com/case-fatality-ratio-ncov/ Then it remarks that many mild cases are probably not detected (ie. high underascertainment) but strangely makes no effort at quantifying it, or referencing other papers that do quantify it. The 4 best published stu
Read the rest
dies that correct for these two aspects (naïve CFR bias + underascertainment) are: https://www.mdpi.com/2077-0383/9/2/523 https://www.imperial.ac.uk/media/imperial-college/medicine/s... https://institutefordiseasemodeling.github.io/nCoV-public/an... https://github.com/calthaus/ncov-cfr/blob/d30f02e1c20e06103a... (3 of the 4 above studies are even referenced by the WHO in their Coronavirus disease situation report 30 (references 10, 11 and 12) and in situation report 31: https://www.who.int/docs/default-source/coronaviruse/situati... and https://www.who.int/docs/default-source/coronaviruse/situati... ) And they all estimate the CFR to be between 0.5% and 1.6%, or about 5x to 16x deadlier than the flu (0.1%)
In Coronavirus: an email to my family
You are probably right that the numbers out of Hubei province are inflated and less severe cases are underrepresented. Unfortunately, according to some experts, the case fatality rate of ~2% seems to hold even for cases outside of China. Statistical modeling of the case fatality rate using only cases and deaths reported from outside China was done by Christian L. Althaus (Institute of Social and Preventive Medicine, University of Bern), this estimates the CFR for COVID-19 at 2.1% although with a high uncertainty (95% confidence interval: 0.5%-5.4%). Data and R code for his modeling are available on Github: https://github.com/calthaus/ncov-cfr A comment from Adam Kucharski (Associate Professor, London School of Hygiene & Tropical Medicine) on Twitter: https://twitter.com/AdamJKucharski/status/122970801348691148... "... The upshot: the wide
Read the rest
ly quoted 2% fatality for China is calculated incorrectly, because it's based on data that is under-reported and doesn't account for delays. But, confusingly, these errors may actually cancel out, leading to an estimate that is right for the wrong reasons ..."
In Coronavirus: Largest study suggests elderly and sick are most at risk

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