Coronavirus 3 Day Prediction | Hacker News Reader

Coronavirus 3 Day Prediction

coronaviruschart.com

44 pointsvackosar59 comments

In gawk, as of 28 Jan, daily for 2 weeks, weekly for 100 days:

    #!/usr

BEGIN { # Data as of 2020-1-28 doublerate = 1.71 dailyrate = 2/doublerate cases = 6063 deaths = 132

now = mktime(

maxdays = 14 interval = 1

printf( "| printf( "|%-5 for(day=1; date = strftime( printf( "| }

printf( "\n\n" )

maxdays = 100 interval = 7

printf( "| printf( "|%-5 for(day=1; date = strftime( printf( "| }

printf( "\n" )

}

Chart is formatted Use an alcohol-based Avoid touching your Cover your cough Clean and disinfect You can try out the And had error bars on the prediction. And gave you access /bin/gawk -f "2020 01 28 00 00 00" ) %3s | %-12s | %12s | %14s |\n", "day", "date", "deaths", "cases" ) s|%14s|%14s|%16s|\n", ":---", "------------:", "------------:", "-------------:" ) day<=maxdays; day+=interval ) { "%b %d, %Y", now + day * 86400 ) %3i | %-12s | %'12i | %'14i |\n", day, date, deaths * (dailyrate^day), cases * (dailyrate^day) ) %3s | %-12s | %12s | %14s |\n", "day", "date", "deaths", "cases" ) s|%14s|%14s|%16s|\n", ":---", "------------:", "------------:", "-------------:" ) day<=maxdays; day+=interval ) { "%b %d, %Y", now + day * 86400 ) %3i | %-12s | %'12i | %'14i |\n", day, date, deaths * (dailyrate^day), cases * (dailyrate^day) ) class="pb-1">

Output. Keep in mind that low-side deviation is anticipated and is a measure of containment success.

    | day | date         |       deaths |          cases | ------------:|  -------------:| 154 |          7,091 | 180 |          8,293 | 211 |          9,700 | 247 |         11,345 | 288 |         13,269 | 337 |         15,519 | 395 |         18,152 | 462 |         21,230 | 540 |         24,830 | 632 |         29,041 | 739 |         33,967 | 864 |         39,727 | 1,011 |         46,465 | 1,183 |         54,345 | |       deaths |          cases | ------------:|  -------------:| 154 |          7,091 | 462 |         21,230 | 1,383 |         63,561 | 4,143 |        190,297 | 12,403 |        569,731 | 37,135 |      1,705,718 | 111,181 |      5,106,750 | 332,865 |     15,289,096 | 996,564 |     45,774,017 | 2,983,613 |    137,042,800 | 8,932,639 |    410,292,352 | 26,743,422 |  1,228,374,011 | 80,067,114 |  3,677,628,161 | 239,712,883 | 11,010,448,584 | 717,676,247 | 32,964,174,931 |

Extend the model out just a bit farther and you discover the true threat of coronavirus that we need to be worried about is when the infected and dead are sufficiently numerous that we need to start worrying about their Schwarzchild radius. Truly... dark times.

There's a reason all growth curves tend to sigmoid (for instances / total births or cases w/ time), and steady-state, cyclic, or boom-bust. And ultimately, of course, extinction.

OTOH, 2019-nCoV might be the answer to self-sustaining fusion we've been looking for.

I like to put upper bounds on the worst case, so I checked out the Wikipedia page on the 1918 flu, and it appears that this virus is only about 1/5th as lethal, and lacking a world war, shouldn't spread as easily.

No world war however we now have airlines and modern travel.

What is the source for the doublerate ? Related to R0 ?

From the Wikipedia Wuhan 2019-nCoV article, semi-log graph. Caption:

Semi-log plot of confirmed cases and deaths indicates the epidemic is in an exponential phase. Doubling time is 1.71 days (95% confidence interval 1.64 to 1.79) for case numbers

https://en.m.wikipedia.org/wiki/2019–20_Wuhan_coronavirus_ou...

Since there are low and high estimates, I could include those values as well in my model.

OK, done. Note that this really shows how sensitive growth rates are to even modest changes in doubling rate. Note that the 'lo' column refers to the time to doubling, not the rate of doubling, which may otherwise be counterintuitive.

In the 100-day run, the difference between a 1.64 day doubling rate vs. a 1.79 day doubling rate is total global infection by April 8, vs. still < 360 million infected by May 6.

The real lesson here is that growth rate matters vastly more than starting quantity. That's something any investor should recognise innately, but it applies accross numerous other fields. Including pandemic spread, population growth, and resource utilisation rates of increase.

    | day | date         |       deaths |          cases |          @ lo dbl |       @ hi dbl | ------------:|  -------------:|     -------------:|  -------------:| 154 |          7,091 |             7,393 |          6,774 | 180 |          8,293 |             9,016 |          7,569 | 211 |          9,700 |            10,996 |          8,457 | 247 |         11,345 |            13,410 |          9,449 | 288 |         13,269 |            16,353 |         10,557 | 337 |         15,519 |            19,943 |         11,796 | 395 |         18,152 |            24,321 |         13,180 | 462 |         21,230 |            29,660 |         14,726 | 540 |         24,830 |            36,171 |         16,454 | 632 |         29,041 |            44,111 |         18,384 | 739 |         33,967 |            53,794 |         20,541 | 864 |         39,727 |            65,602 |         22,951 | 1,011 |         46,465 |            80,003 |         25,644 | 1,183 |         54,345 |            97,564 |         28,652 | |       deaths |          cases |          @ lo dbl |       @ hi dbl | ------------:|  -------------:|     -------------:|  -------------:| 154 |          7,091 |             7,393 |          6,774 | 462 |         21,230 |            29,660 |         14,726 | 1,383 |         63,561 |           118,981 |         32,014 | 4,143 |        190,297 |           477,290 |         69,595 | 12,403 |        569,731 |         1,914,634 |        151,293 | 37,135 |      1,705,718 |         7,680,489 |        328,895 | 111,181 |      5,106,750 |        30,810,015 |        714,985 | 332,865 |     15,289,096 |       123,593,305 |      1,554,303 | 996,564 |     45,774,017 |       495,790,250 |      3,378,893 | 2,983,613 |    137,042,800 |     1,988,845,356 |      7,345,363 | 8,932,639 |    410,292,352 |     7,978,184,014 |     15,968,056 | 26,743,422 |  1,228,374,011 |    32,004,207,853 |     34,712,893 | 80,067,114 |  3,677,628,161 |   128,383,767,336 |     75,462,218 | 239,712,883 | 11,010,448,584 |   515,007,020,041 |    164,047,010 | 717,676,247 | 32,964,174,931 | 2,065,932,759,210 |    356,621,130 | as a Markdown chart, BTW.

"growth rate matters vastly more"Only in the case where you deny the existence of uncertainty in that number. Also, it's generally considered that infectious and lethal diseases rapidly evolve to become less virulent, since the people that are sickest tend to be more isolated.

No.The point is that initial quantity has virtualy no impact on final result, as compared with growth rate.
With a doubling time of under two days, a misestimation by a factor of two ... is resolved in a day.  By a factor of 10, in about 3 days.  By a factor of 100, in six.  By a factor of 1,000, in 12.
It's not uncertainty about the growth rate, but change which matters, and which epidemiological containment is concerned with.  The goal is to change the transmission rate (R factor), and through that, the rate of growth of the epidemic.
Epidemics of the past which have proved highly lethal (look through your favourite list of historical plagues) have killed from 10% to half of local populations, often through a combination of high mortality but slow onset.  Spreading before* symptoms are clearly expressed will suffice.
The Black Death spread throughout Europe from 1346 - 1353, over eight years, killing up to 60% of the population.  It returned multiple times, through 1667, with recurrence mortalities of 10-20%.  It continued through the 19th century in the Arabic world.
Expecting a novel virus to attenuate markedly in a few days or weeks seems wishful thinking.

"It's not uncertainty about the growth rate, but change which matters"You're saying growth rate is not rate of change and the rate of change is important, but the difference between two different rates of change is insignificant?
My point was that the R factor is neither constant nor precisely known, whatever people do. If the outcome is very sensitive to the number, then it seems to me obvious that it is very important to look at the outcome over the range of possibilities, because it's going to be a large range. Whenever professionals make long term predictions, they usually depict them as a cone. How big it is and how it widens is important.
Also, I thought the plague was bacterial, so I'm doubtful of the inference that a virus won't evolve rapidly. Previous flu epidemics did, right?

I'm sorry, but I appear to be having difficulty in expressing myself clearly.Growth rate is obviously a rate of change.  Specifically:  of growth.
The growth rate of an epidemic, disease, population, or any other phenomenon, is not a constant over all time.  It can however be assessed at any given point in time.   And frequently remains largely similar for period of time.  Which is to say:  the rate of change is itself subject to change.
By analogy, speed is a rate of change, but speed itself can be subject to change through acceleration.
You seem to be arguing (though I'm understanding your argument poorly as well) that growth rate doesn't matter.  You've not indicated what it is that you do think matters.
My argument is that of the two measures, initial population and growth rate, the first matters little, and the second effectively entirely defines observed behaviour, that is, growth.
I am not arguing, though you seem to understand I am, that the growth rate will be constant for all time.  The growth rate will change. That is the entire purpose of epidemiological intervention.
I AM arguing that the change in the growth rate is everything that matters about the 2019-2020 Wuhan Coronavirus Outbreak, and its eventual resolution.
Any uncertainty about the growth rate still affects the rate of growth, and to that extent, is material.  Uncertainty does not change the fact that rate of growth entirely determines the course of the epidemic.
I do hope that's clear.

s/specifically of growth/specifically of population or count/

"The prediction is based on a very simple assumption that the counts will follow an exponential curve"This reminds me of news reports from the 90s, where the big fear was that Chinese companies would totally take over Europe. The big Chinese breakthrough would happen 'next week', 'next year', 'next some arbitrary buffer that puts us just outside of the data'.
Corona might go exponential, but with the amount of effort being put into containment, I doubt it.

On the contrary, isn't the expected behaviour for most types of growth that it will be exponential in the initial phase, then transition to an S-shaped curve?Whether it be expansion into a new market or spread of a virus, in the early days there is little resistance. Then when (competitors|health services) start noticing and working against it, the spread slows and plateaus. How quickly that happens and what level of spread the plateau is at depends on the speed and efficiency of the countermeasures.

At some point everyone an infected person comes into contact with is either already infected or has previously been infected( and potentially immune).  So I would think you would be exactly correct, at some point the number of infected people will peak and begin to fall.  The number of dead and the number of recovered will form an sigmoid curve.

I'm pretty sure Gartner predicted that by 2010 there would be 15,000,000,000 Palm Pilot users.

For clarification, the coronavirus is currently exponential with a R^2 value very close to one. The question is whether of not it will stay exponential, or go sigmoidal soon.

I appreciate the text below the graph was probably written by a non native English speaker but can someone please decipher this..Question 2
However, as a reminder, CDC always recommends everyday preventive actions to help prevent the spread of respiratory viruses, including: Wash your {{ ... }}.
(a) hands, give every bite a chance to keep your nose open
Question 3 hand sanitizer that contains {{ ... }}.
(a) antifungal immunoprecipients, not salt; run an inflatable swimming pool
Question 4 eyes, nose, and {{ ... }}.
(b) neck, especially when wearing your shoes
Question 7 or sneeze with a tissue, then throw {{ ... }}.
(a)    your hands up, arms, hands, face
Question 8 frequently touched {{ ... }}.
(c)    tissues with dental iron

Thanks for asking. Those are automatically generated answers from quiz assisted app Quizrecall.com. The strategy of the quiz generation there is to generate at least distraction answers while good alternative answer generation remains kind of hard. app on your favorite wiki summary at https://Quizrecall.com

If I am reading the tea leaves correctly, based on the fact that quizrecall.com was registered December 30th, 2019, and my (adjusts monocle, clears throat ostentatiously) extensive experience reading /r/SubSimulatorGPT2, it appears to be a new website that tries to automatically create quizzes from given material by blanking out portions of sentences and using the (probably stock, probably the smaller model) GPT-2 text generator to fill in the "wrong" answers.Neat idea. Not sure it's quite useful yet, but neat idea.

Given the topic, might be better to choose something written by a human to avoid giving incorrect advice.

Despite the text implying it's all an exponential curve forecast model, the wiggles seem to point to actual data being plotted too.I wish they differentiated data points from forecast points in the actual chart. Text says 1/29 update, but I think only the 1/28 point seems to match news reports.

And cited sources for their data. to historical predictions.

Historical case counts don't really have error bars... and it's difficult to come up with meaningful error bars based on goodness of fit of a function.

But the graph is also looking three days into the future, presumably based on fitting some model to the historical data. The output of any worthwhile model is a probability distribution over possible future infection and death counts.  As a consumer of that graph, I should be able to see not just the value just a model thinks is most likely, but at least some indication of how tight a bound the model is placing on those values.

The lack of which suggests that the author (and who is that, exactly?) may not have given a moment's thought to any kind  of responsibility they might have in the midst of a health crisis.Oh, what the hell.  Let's write some Javascript!  And slap a tweet button on it!

If you just want the historical data, try https://news.sina.cn/zt_d/yiqing0121 Of course the labels and data sources are all in Chinese...

It seems that actual are integer values while predictions are float numbers.

It could be clearer, but, the way I read it was that it's the historical data plotted + 3 days of predictions.

Which is 100% misleading, and strips all accountability from the predictor. Show us your prediction and the difference between the actual and predicted results to see how well you did.Stop moving the goalposts too. Make the prediction for all days in the future once, and don't change it based on the current numbers.

https://news.ycombinator.com/item?id=22185174Though near-term projection based on current trend is useful.

Eventually more humans will die from it than have ever been born! ;)

It's the Four Yorkshiremen Epidemic.

It's just an exponential fit based on the current number.  You can run that same fit for earlier days and eyeball the graph, but we have a fancier tool for that called a "correlation coefficient".> Stop moving the goalposts too. Make the prediction for all days in the future once, and don't change it based on the current numbers.
So, don't ever improve the prediction based on new data.  Mkay.
Extrapolating out into the future is always errorprone.  Exponents don't ever continue forever and they look like great models until they don't.

You can definitely improve your prediction based on new data, but the most important part is to show your historical predictions too. FiveThirtyEight does this pretty well by showing the probability a candidate gets elected in a time series chart.Predicting is serious business, and you have to be accountable for past predictions, especially if they were wrong.
This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true.
>> Extrapolating out into the future is always errorprone.
You're absolutely right. And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Because they'd know from past experience that past models were mostly wrong!

> This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. Instead, if readers saw your past predictions were mostly wrong, they wouldn't have this false perception.Just hit the button to toggle to the log scale version of it.  A exponential fit is appropriate and past predictions using an exponential fit would be good.  :P  Do you understand how to fit data series?  My fifth grader could hold up a ruler-- align it the the beginning of the yellow line and each subsequent point and see how reasonable of a prediction they are of future points.  He'd say "Yup, it's pretty straight!"  And even for the red line, though a little less so.
Yah, it would be cool if they'd tell us r^2.
> And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found.
Note that exponential models predict everyone will get the disease and everyone will die if you extrapolate out far enough.  (Actually, -more- than everyone heh).  Obviously we don't think that's the case.  But it's a pretty damn reasonable way to see 3 days forward in the early phases of an epidemic.

agreed. I mention there that this is simplistic. I may look into the sigmoid shape s-curve once it is time for it. If the site is still interesting to somebody at that time.

Yes you are right. I moved the data one day forward as it seems more natural to call data that we received today as todays data and not yesterday's, but that was a bad idea. I fixed that.Also I tried to add as much info about the sources I had now. I will try to improve on that later. The site mentioned here https://news.sina.cn/zt_d/yiqing0121 really looks like finally one good source of the data I was looking for.
I need to get some sleep now.

Mostly I was hoping for a different color and/or symbol for data vs prediction points as a way to more quickly understand what the chart curve represented.Another interesting comparison would be to draw a model curve (or curves) further back - the difference vs data points might give you a visual indicator of how the virus is spreading vs effect of active/natural influences vs the model.
Thanks for the update, don't be too hard on yourself, there are infinite ways to improve, but I appreciate the quick look data.

I colored the prediction points in blue.

Why do this when there are strong models from epidemiology and spreading processes? Like SEIRS?

To promote their (not very good) quiz generation service.

Can you provide any links to sites that take current data and use better models to forecast? This site strikes me as a useful first step. But clearly there are better approaches, I have only seen predictions in papers, not in an equivalent site.

There are a few around. Try this one https://www.buyupside.com/calculators/sir.php

I think the current forecast is a reasonable first approximation for a three day forecast. The model you propose is more accurate but requires many more parameters, each with their own error bands. I think you are looking at a very complex rapidly evolving situation characterized by an eventual transition between exponential growth and S-curve transition that is very hard to predict. The chart now indicates the forecast points and assumes 3 days of exponential growth, which until the virus is contained is a reasonable three day forecast.I have no affiliation with the author of the chart but appreciate the thought and effort that went into it.

At the time I haven't see any chart like that on the net. I also wanted to experiment a bit to see how many views one can get. (It is my third website or so)

This would be more informative if it had historical predictions. E.g, https://www.reddit.com/r/dataisbeautiful/comments/evlmqr/oc_...

can someone please run this again, it's been so accurate!

What TZ is being used for day cut-off?  Details like that are important.More so if you want a comparison with actualities like :- https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.h...  (which is EST as you can see - bottom right).

WHO provides status report here: https://www.who.int/emergencies/diseases/novel-coronavirus-2...Seems to provide some useful data

This page layout is messed up. Too wide for my window, and somehow it defeats horizontal scrollbar. Things are cut off along both the left and right sides, and there's no way (short of making the window bigger) to see them.(xwininfo says my Chrome window is 1114 pixels wide.)

Thanks a lot for the bug report. I on mine Firefox and Chromium is seems fine. I will try to fix it. If you have any tips let me know.

This is crap. Data is outdated and off, the assumption of an exponential curve is way too simplistic.

Relevant xkcd: https://xkcd.com/605/

I think the only question at this point is will Coronavirus kill all of us or just most of us.

Or be contained like pretty much all of the epidemics we've hit so far and fizzle out.  Steps need to be taken and it is dangerous - but I also have bird flu fatigue.

At worst, based on current stats it will kill around the weakest 1% of us. That's still a massive problem, and awful tragedy, but nowhere near killing "most" of us.

Is there any demographic data on that 1%?

Reply on news.ycombinator.com