Edit: In order to make this a little less dismissive, the actual argument in that Twitter post is that due to lag between first symptoms appearing and death, you can't divide fatalities by total cases to get the mortality rate, and should instead use total cases N days ago. Makes sense superficially, but
* cases that get reported are biased towards people who get more sick
* the 20-56% mortality rate claimed based on guessing N=6 is inconsistent with the mortality rates outside of China, where no one has died so far.
But I'm no epidemiologist, so don't believe me either.
Data gets aggregated and used in mathematical models that can predict outcomes in various hypothetical scenarios, but given that this is a real-world situation, the professionals are going to be looking at the actual data first to find out what’s happening. They can directly answer questions such as, of the patients diagnosed 1 week ago, what’s their status today?
Anyone looking at only total numbers of cases per day simply doesn’t have the data required to compute the things they’re trying to compute with much accuracy. It’s possible to produce estimates with confidence intervals based on sensitivity analysis, but nobody doing that with any care using aggregate data is going to come out with a number as high as 20% for a lower bound. Given that N=6 is a guess, the range reported should be bounded by plugging in plausible extreme values for your guess of N, not assuming your guess of N=6 is exactly correct.
True, the data is biased.
«the 20-56% mortality rate claimed based on guessing N=6 is inconsistent with the mortality rates outside of China, where no one has died so far»
It's not statistically inconsistent. When China had 100 cases confirmed, they had reported only 2 deaths. Now there are 100 cases outside China, so statistically we should expect 2. 0 actual vs 2 expected is statistically insignificant. Also the rest of the world is so much more prepared after the wake-up alarm call from China that out of the 100 confirmed outside China, they are probably receiving superior healthcare than overwhelmed Chinese hospitals. I would expect the mortality rate outside China to be slightly inferior.
It also means though, that the first death outside China should happen soon. I predict in the next week.
My point here was that the China data is probably biased because the medical system there is overtaxed, but outside of China it isn't. Therefore, estimating mortality based on non-China data seems safer, and no one outside of China has died so far.
If I take your low end mortality estimate, 20%, and apply that to the number of non-China cases 6 days ago (12), you get only a 6% probability of all of these people surviving. Note that this already contains a lot of assumptions (going back 6 days, taking your lowest mortality estimate) that work in favor of your alarmism.
I think that "going back 6 days" is insufficient, so your conclusion of a 6% probability is unsupported. Given the extreme state of paranoia around the world (eg. authorities testing people coming out of airplanes) it is likely that cases outside China are detected very early after the onset of symptoms. As such, death, when it takes place, will probably take more than 6 days after a confirmed case is detected.
In fact, the studies in The Lancet document cases of patients that take much more than 6 days to die.
Everybody is taking this seriously, but playing prophet of doom is really shitty and has all sorts of potential negative consequences in the real world.
You tried to point out 1 inconsistency, and I replied back saying it wasn't an inconsistency ("0 actual vs 2 expected is statistically insignificant.") It's too early to tell if the CFR outside China will differ from the CFR in China.
* Zhejiang: 428 confirmed; 0 deaths; 4 recovered
* Guangdong: 354 confirmed; 0 deaths; 10 recovered
* Henan: 278 confirmed; 2 deaths; 2 recovered
* Hunan: 277 confirmed; 0 deaths; 2 recovered
* Anhui: 200 confirmed; 0 deaths; 2 recovered
* Chongqing: 182 confirmed; 0 deaths; 1 recovered
* Jiangxi: 162 confirmed; 0 deaths; 5 recovered
* Shandong; 158 confirmed; 0 deaths; 1 recovered
* Sichuan: 142 confirmed; 1 deaths; 1 recovered
* Jiangsu; 129 confirmed; 0 deaths; 1 recovered
Although there's a clearly a lag between confirmed cases and death, there's likely to be less lag in outcomes between patients who recover and patients who die.
On this admittedly small sample, we get 29 recovered and 3 deaths, or about a 10% death rate. That's still pretty high, but it's nowhere near the 20-56% death rate.
If people go from being infected to dying more quickly than they go from being infected to being declared to have recovered, then that would lower the rate. The recovered rate seems to have been increasing faster than the death rate in the last couple of days, which might indicate this is the case. In addition, these cases obviously only include patients who present with symptoms. If some people are asymptomatic, that would also lower the death rate.
Nitpick: I don't claim "20-56%" death rate, but "9-56%", thus your observation of a 10% CFR is in line with my claim.
* 29th: deaths 133, recovered 126
* 30th: deaths 171, recovered 143
But for Hubei, we get:
* 29th: deaths 125, recovered 88
* 30th: deaths 162, recovered 90
Which looks like the death rate is going up in Hubei.
Subtracting Hubei though, for the rest of the world (but mostly China) we get:
* 29th: deaths 8, recovered 38
* 30th: deaths 9, recovered 53
* difference: deaths 1, recovered 15
Of course these numbers are too small to have real significance, but they're worth watching over the next day or two. It looks like outcomes are very different outside of Hubei from in Hubei.
Other possible explanations are that the healthcare system in Hubei is overwhelmed, so more patients die, or alternatively they're just too busy to accurately report recoveries.
Edit: Oh you mentioned this yourself in another comment.
It remains to be seen if the rest of the world can keep the infection rate low enough to not repeat the Hubei progression.
True, however I fail to see how this point is relevant when comparing statistics with past diseases - as the same could be said of our historical data we have for SARS or anything else. In any case, we only have reported confirmed cases. Doesn't seem to make sense to me to factor in a unknowable variable here but not in other cases...
Even when compared to SARS in that chart it looks like it has a lower R0 value and about the same fatality rate, minus some odd outliers and what it likely fake news (it doesn't have a fatality rate of 56%). And the citation for that 56% number is a link another link to Twitter… that is self referential. And actually I think YOU are also that person.
When people talk about "fake news" this is what they are talking about. This is like 3-layers deep of fake news. You are linking to yourself in a post that uses yourself as a citation.
However, for your information, the 56% case fatality ratio is calculated according to an epidemiological method known to produce a good estimate. See https://pdfs.semanticscholar.org/ebf2/48c9fc0a1a23d1778b9408..., section Simple Estimators, specifically this formula:
e₂(s)=D(s)/{D(s)+R(s)} which is: deaths/(deaths+recoveries)
And the paper concludes: "The second simple estimate based on the ratio of deaths of those for whom the outcome is known, e₂, is reasonable at most points in the epidemic" ie. produces a good estimate of the eventual observed case fatality rate.
You are creating and spreading fake news. You might not be a bad person, but right now you are being a bad person.
That's a non-answer. My data is valid. I challenge you to point out specific errors.
My data comes from two sources:
1) Initially I replicated the chart by Dr. Melvin Sanicas, a vaccinologist and public health physician, that he posted on twitter (here: https://twitter.com/Vaccinologist/status/1220469109378502658). He had no citations for his numbers. So I performed light fact-checking but didn't bother documenting citations for each number because (a) he is a doctor and (b) his chart got massive reviews on twitter and no one pointed out significant errors.
2) For the rest of the number, I plugged them in myself WHILE adding proper citations to either peer-reviewed papers or to the best estimates available today.
As to empty cells, they are empty when the data is not yet researched, or not known, or because it is irrelevant (eg. for most older diseases that have vaccines available I didn't bother researching if asymptomatic transmission is possible because it is irrelevant: most people in developed countries are vaccinated as such such diseases are no longer prevalent.)
If you're not a troll you should take a step back and actually THINK about what you are doing. You are creating misinformation, posting it, then reposting it elsewhere and citing yourself.
Stop it. Stop being a garbage human trying to scare people with nonsense. Be better. No "what ifs" or "whatabouts". Delete your comments, delete your tweets and try to be a better person.
Another non-answer. It is perfectly valid to compare various epidemiological characteristics like I did, and like Dr. Melvin Sanicas did (are you going to criticize him too?) A lot of us on HN are sufficiently educated to have decent discussions about topics we are not experts in. If you have constructive criticism, give it to me instead of your non-answers.
A: The entire table is misinformation.
It is not valid to do what you did. You are wrong. You are misleading people. The “question” is whether you are doing it intentionally or not. I don’t have that answer, only you do.
Would you consider fucking off?
This is your third or fourth bullshit post on this thread combining a sensationalist falsehood with a link to your Twitter feed.
If you have something substantial to post, then do it, otherwise please respect the guidelines of this site ...
This is why the longer incubation time of this coronavirus is such a concern. It gives a potentially fatal disease more time to spread before it self limits. Also, I've heard reports that some infected are asymptomatic, so it may continue to spread even while taking out some people.
https://evolution.berkeley.edu/evolibrary/news/071201_adenov...
https://www.pnas.org/content/pnas/early/2014/11/26/141333911...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2639916/pdf/896...
http://hedrick.ucsd.edu/BICD136/PDF-links/Galvani-Epid-evol....
I understand the basics of evolution. I also don’t have any trouble understanding the trades between transmission and virulence. However, this is not the same as saying that the typical virus becomes less virulent over the short term during a major outbreak.
Serious, yes. But it's quite a hurdle to go from what we're seeing now to "worse than seasonal influenza".