As you can imagine, there's more to epidemiology than this. I'm a big fan of letting the qualified people do their work, while we do ours: shut up and do not spread misinformation.
In fact, at the risk of being rude ill even go so far and say that it's quite arrogant to pretend to pontificate about this when your knowledge about this issue is 0, and also when one major problem in combating this pandemic is the "epidemic of misinformation".
I made it clear the limits of my knowledge - which may still not excuse me, however I have to ask if you are an epidemiologist and if so, please tell me how to model the data better. And I don't mean randomly google up some papers to dump here and pretend you are an expert (unless you are).
How far can I extrapolate e^x before it becomes useless? What are the confidence limits I can use in the extrapolation? How much can I approximate the logistic function with an exponential, when as now, the cases are currently a small fraction of the population? Why is this site http://nrg.cs.ucl.ac.uk/mjh/covid19/ done by experts using log axis any different from mine using curve fitting with exponentials? Why does http://nrg.cs.ucl.ac.uk/mjh/covid19/#w say the USA is 11.5 days behind Italy whereas my amateur attemp suggest ~9 days? (and yes, I wish I'd seen these graphs before I posted) What else have I missed? Criticise freely, with reason, don't just accuse me of misinformation because I posted my fears, which others have done too, only I did the maths.
Now please excuse me while I look at the logistics curve and compare its early stages with exponentiation.
There is no "actual data" in your comment above. There is a claim that you make that you have graphed some data, but no graphs and no data.
Also, it doesn't take an epidemiologist to know that a non-epidemiologist should not play at epidemiology. The other poster doesn't have any responsibility to tell you "how to model the data better".
There is in http://nrg.cs.ucl.ac.uk/mjh/covid19/#w which I said matches what I've found rather well.
> The other poster doesn't have any responsibility to tell you "how to model the data better".
Yes he does,
There may be data there, but if I understand you correctly you did not have it when you made your first post.
It is therefore inadmissible evidence as to whether your first post was a reasonable thing to write.
> Yes he does,
If you have decided you want to be an epidemiologist, or at least to learn their skillset, whose responsibility is it to fulfill that desire?
I don't think there is a reasonable answer to that other than "mine."
But I'll try to help. You're looking at a graph of confirmed cases, in other words, positive test results.
That may be due to an increase in the number of infected people; or because the number of tests being performed is increasing, and therefore the percentage of infected people reported as confirmed cases is increasing; or because some positive test results (even with confirmation) are false positives. And there may be other causes that I don't know, because I'm not an epidemiologist either, just a statistics grad student.
Figuring out the the contribution of each cause is a job for the professionals.
May I ask, what area do you work in, numerical, discrete, logic, other?
Rather than admonish people to shut up, it would be more constructive to point out the flaws in their methods, so we could all learn and make better judgements.
Yes, but that's the thing isn't it? We're not informed, and fitting ke^x to data doesn't make us informed.
This reeks of Dunning-Kruger. I get that the intention is good, but we have to have some humility and not go all "hold on! I'll save the day!"
> and fitting ke^x to data doesn't make us informed
Well, yours is an interesting post but let me ask if curve fitting doesn't make us more informed then what does? There's an update to the data for the US in the past few hours and it doesn't improve things.
I very carefully stated out my limitations, and then pointed them out again when someone critiqued my approach (see edit on https://news.ycombinator.com/item?id=22645793). I have done my best to accept my limitations, but still I get people like you hammering me.
So the question is why are we even posting anything on HN? Bottom line is what is HN for - non-factual debate, or factual debate?
I strongly get the impression that much criticism isn't based on my admittedly trite analysis but because a lot of people just don't know how to handle really bad news and are trying to blank a nasty reality. Doubly so when I'm accused of of "hold on! I'll save the day!" which is purely your invention.
No, you just made a large number of flaws in your initial analysis, and it made people despair of going to the effort to point out where you went wrong.
E.g. https://news.ycombinator.com/item?id=22648730
You're producing a future estimate, assuming that all properties of your dataset are consistent (they aren't) and will continue to hold (they won't).
As a few examples: (1) varying ratio of confirmed cases to actual total, (2) % increase in testing per day, day-over-day, (3) varying time offset according to disease progression and symptoms presentation (aka testing).
You seem to be of the opinion that any information is better than no information. In scenarios like this, I'm of the counter opinion that bad information is actively harmful and inferior to no information.
Seeing as you don't have access to my analysis, you can't say that.
> You're producing a future estimate, assuming that all properties of your dataset are consistent (they aren't) and will continue to hold (they won't).
This is just vague stuff to put me down. I worked by confirmed cases in publicly available data.
> varying ratio of confirmed cases to actual total
I'm not sure what this mean but I went by the total confirmed cases, day by day. As for your 'increased testing', I'm fully aware the US is finally getting going.
> varying time offset according to disease progression and symptoms presentation (aka testing).
Why would the progress at different rates at different times in different individuals make any difference? I'm going by confirmed cases anyways so unless you know of a new strain with different characteristics that's starting to distort the figures (and there are possibly 2) then it makes no difference.
No, save your slapdowns for the people who push garlic as treatment. You are objecting to me for psychological reasons, not logical ones. You can go to the wiki page and see the growth day by day but you don't want to because it's bad news and you don't like that. Well, get used to it. Your life is going to change, nothing will stop that and the sooner you face it the safer you and your country will be. Hard times ahead. Good luck.
Like other people have said through this thread, it's rather simple: you don't know what you are doing, and at this time bad information is worse than no information, and it will hurt people. And reiterating my remarks on my first post: stop being so arrogant, this is really the only word to describe your attitude as far as I'm concerned.
And "I am a bloody genius [...] by plotting a bunch of points in a log scale" - I actually used the words 'admittedly trite' about what I did.
Whatever. This gets us nowhere. Your profile says you have a background in physics and logic/automated reasoning. I'm just a general purpose back-end dev. We're facing something that may kill more people than WW2, let's concentrate on that. Can we work together to do something useful? Thoughts?
Many doctors lack training in epidemiology. Many epidemiologists aren't actual doctors.
Healthcare in general is horrible with generating accurate raw data (JHU data comes often from scraping websites and the CDC itself has stopped publishing number of daily tests weeks ago). <-- this in fact is a technical problem not a medical one!
You don't have to be a Dr. med in order to see gaps and problems with statistics and a large part of the conjecturing actually comes from gaps and flaws in the sources and the way data is presented. A background in an isolated discipline (only med, only statistics, only Math, etc) is not enough to harness and make sense of complexity.
Finally research papers themselves are all pre-print and not peer-reviewed which is a trade-off in accuracy for speed.
We're all finding solutions as we go and the more eye-balls from different discipline this gets, the better the outcome!
Yes, and this is why doctors aren't being asked to do epidemiology and epidemiologists aren't being asked to care for patients.
And (likely) sofware engineers are not asked to do either.
I too am tracking the numbers. I seriously and honestly expect them to become unavailable- or at least very hard to get to, if things go very very bad.
I expect to see a bump in the "infection rate" in the next few days as testing becomes more widespread. In fact, I expected to see the start of that already, but if it's happened it's not obvious. The number of cases continues to double every two days and change.
throwaway_pdp09 isn't spreading unnecessary panic and their attitude has been anything but arrogant, pompous, or dogmatic. They are very open in admitting that they are not an epidemiologist. Saying f(x) = 9 and asking why their 'f' is wrong, and wanting to understanding its shortcomings and how to improve it, seems entirely fine to me. You are going to have to be far more specific on the harm caused by a lay-person projecting that we are fucked in 9 days, vs an expert's opinion that we are going to be fucked in 11.
If it helps, pretend this question is being presented and asked at TA office hours during Epidemiology 101 at university by someone who is considering going into epidemiology.
We are all here to learn.
Meanwhile, there's actual, harmful nonsense out there to rail against. Half-remembered gossip misinformation that spreads like an infectious disease with an R0 of 5. Complete crap, like holding your breath for 10 seconds will test for Covid-19, or that essential oils cure Covid-19 or that its eventual vaccine will cause autism. Fight that misinformation, not people who's latent interest in epidemiology has been awakened. Who knows, a reader here could be deciding to get a degree in epidemiology because of an interest sparked because an exp curve is too simple.
I'm just trying to make sense of things, same as anybody else. I don't think anybody's fucked either in 9 or 11 days, I actually have a great deal of faith in the United States and yes, I mean it, but bad decisions from high will cause a terrible amount of pain too soon. I just want to wish the US the best because they'll need it now more than any other time, probably in all it's history. I don't think WW2 stressed you as much as you are about to be now. Buckle up yanks and pull together, I believe in you! You will get through this.