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Comment by throwaway_pdp09 | Hacker News Reader

This is bad. I've been graphing the exp curve for Italy, UK and USA and the USA curve is fearsomely steep. Italy has more cases now but the USA is growing fastest and looks like it will match Italy's on ~March 28th (looking at the most recent 10 points, see below)

The rate of growth in the USA, extrapolated, is just huge. Looking at the whole curve it does not match an exponential well, I think the early part is skewing it. If we take the last 10 data points, they fall very well onto the curves for all 3 countries, and that curve for the USA is horrific. I'm not an epidemiologist nor statistican so it would be irresponsible to put my predicted figures here, but christ, if I'm right the US is going to be reeling in just a few day.

Edit: @mnl below has pointed out that infection is not an exponential but on the whole a logistics curve (s-shaped curve https://en.wikipedia.org/wiki/Logistic_function). He is correct. However in early stages I believe an exponential MAY be a decent approximation (and I'm reading up on it now). However, the inflection in the 'S' (where new infections would start to slow down) would be expected to be at far higher figures than those I'm looking at - a few tens of thousands of reported infections per country currently - and it's not even close to that (populations of multiple millions in each country).

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> the USA curve is fearsomely steep

This analysis is difficult because it is related to testing capacity. We all know testing capacity was limited early on. It remains limited, but is growing. The faster capacity grows, the steeper the curve will be. Want to have a less steep curve? Lower the amount of testing.

In another comment, you claim looking at the curves leaves out politics completely, but it doesn't. Any politician can make the situation look worse on the curve, but better serve their people, by increasing testing. They can also make their curve look better, but do worse for their people, by claiming their state is unable to purchase tests. This can be a very political game.

Yep. Ventilator usage [if available] and deaths are more reliable indicators, but they're also lagging ones [and even deaths aren't entirely consistently measured since many of the most severely affected patients exhibit comorbities, and many nations don't test the dead]

> deaths are more reliable indicators

They are more reliable than the simple number of cases but in the Italian province of Bergamo (and I suppose in neighbouring Brescia, too) lots of people have started dying before the ambulance even arrives at their homes. Those people will not show up on the official statistics of covid victims, at least not now.

A similar thing had started happening in Wuhan once the situation had become really dire, and the closest valid indicator people could find was to look at the number of incinerators and how busy they were (mostly looking at the number, because the incinerators were busy 24/7 anyway).

Others do, and that skews the results as well (at the very least, it makes numbers incomparable).

once people's minds are made up, it's incredibly difficult to walk them back to a different reasoning. Apart from the lack of early tests the problem is therefore exacerbated by the downplaying of the problem during the critical early stages. It wasn't just lost-time that needs to be made up for but also that mitigations will now become harder to swallow!

The death curve is also quite steep. Seven days ago we were at 57 total deaths, now it's 276. Daily deaths has gone from eight people seven days ago to 49.

https://www.worldometers.info/coronavirus/country/us/

Here's a good article on estimating current number of cases from number of deaths:

https://medium.com/@tomaspueyo/coronavirus-act-today-or-peop...

The death curve is also deceptive - there were likely many patients who were recorded as dying of the flu, or of influenza-like illness, that actually died of COVID-19, due to a lack of testing. The bottom line is that it’s very difficult to make a conclusive statement based on the data collected prior to widespread testing.

As for that Medium article, it’s written by a tech exec with zero education or professional experience in anything related to medicine or the life sciences, so I would treat it with a heavy dose of skepticism. Many of the deluge of preprint articles from academic institutions written by epidemiologists or public health experts would likely be a far better source of info.

Fivethirtyeight has been surveying epidemiologists:

https://fivethirtyeight.com/features/infectious-disease-expe...

Responding on March 16 and 17 to the question, "how many cases will the US report by March 29", their median prediction was 19,000. As of this moment, early on the 21st, that figure has ALREADY been exceeded (per Worldometer). Based on current growth rates, it appears virtually certain that the actual figure on the 29th will exceed the "best-guess" prediction of every single expert surveyed, and the "high-end" prediction of all but one or two. They've completely whiffed a prediction just a few days out.

This is a novel (no pun intended) situation, even for epidemiologists. To understand it, critical thinking skills are at least as important as subject matter expertise. My read is that the Medium article represents some of the best critical thinking on the subject so far.

Here is a report from a team of epidemiologists at Imperial College, led by Neil Ferguson. The title speaks for itself: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand. The paper is sobering reading. But of course, all the numbers plugged into the model are saddled with huge uncertainty.

https://www.imperial.ac.uk/media/imperial-college/medicine/s...

We have plenty of flu tests. Covid-19 is also pretty distinctive on chest CT scans, if it's advanced enough to kill you.

The numbers may be wrong in that article but the math itself seems straightforward. It's certainly true that we need more tests to be sure of the numbers.

Here's a pretty interesting simulation from a research group at the University of Basel: https://neherlab.org/covid19/

> Covid-19 is also pretty distinctive on chest CT scans, if it's advanced enough to kill you.

I guess you're suggesting that when people die of Covid-19, we'd know it and count it?

That's not true.

First, CT scans (and other expensive tests) are not generally performed on people who have died of pneumonia, apparently due to the flu (or other causes). Second, the radiologists reading those studies would have to train to make the distinction.

Now, going forward, we have an increasing need to know whether the virus that caused the pneumonia that killed the patient was Covid-19 or not, so maybe such testing will become routine. (I doubt it will be CT scans though, because there will be much cheaper and reliable ways to do it.)

But so far we've been undercounting Covid-19 deaths for the same reason we've been undercounting cases: lack of testing.

I was more thinking that CT scans would happen in advanced cases before the patients die. I did a bit of googling and found CT scans are not a definitive diagnosis but they are distinct from flu at least (just as koheripbal said).

But it's a good point that we might have more deaths than we've counted, and this might just mean we're further along that we realize. The rate of growth is probably not just an artifact of more testing; it's similar to the growth rates in other countries that haven't gotten control of the disease. And we definitely don't have it under control.

I expect that the number of people who die monthly for a certain condition to be relatively stable over time.

We can compare the usual number of people who die of pneumonia with the current one, and any significant difference can be attributed to this novel virus.

It's not distinctive from other lower respiratory pneumonia cases - it's only distinctive from the flu, so it could have been confused with other infections.

The death curve is a lot less deceptive than the number of cases.

Overall death rate is much less deceptive than known overall virus deaths.

not to downplay any death but there are roughly 100 deaths/day from car accidents and similar number is also from gun-related just to have some perspective. Unfortunate/scary thing is that the virus has the potential to eclipse those numbers by 10x easily

I am worried that we are choosing the second route with articles like this one: https://www.latimes.com/california/story/2020-03-20/coronavi...

If I understand article right, it is not about choosing. It is about not having resources and ability to do the other route.

The situation is exactly the same in Norway now, for the same reason: Lack of resources.

> Want to have a less steep curve? Lower the amount of testing.

That works only at the beginning.

A more interesting view would be the number of positive tests over the number of tests preformed.

Often tests are not performed on a random sample of the population but only on people that are symptomatic.

But not all symptomatic people receive tests.

Knowing those two number (number of symptomatic people who would have qualified for a test, were it available; number of test performed) could paint q better picture.

Until recently Ontario had very stringent testing pre-requisites: 2 or more symptoms, history or travel in the last 14 days. Even then, on average, only 2% of tested people tested positively for SARS-Cov-2. In my opinion that makes the notion of much more larger population having it and not noticing due to the lack of testing, at least at this stage, highly unlikely.

They should be doing some tests via random sampling anyways just to make sure.

Ideally, yes. But given the limited testing capability that is a politically hard thing to do. Even if 50-75% of cases are asymptomatic (and that is a very big if), that would mean that on average you have a number of asymptomatic people that is a factor of 1 to 3 of confirmed cases. While it would be great to isolate those people, at this point that is still not a huge number that means anything for the response.

Why should we assume different states have the same starting conditions?

The graph I think would be helpful would show how many positive tests per day out of a random sample of 1,000 (or whatever) people (non-symptomatic).

>I've been graphing the exp curve

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".

And I am having serious misgivings about posting it, nonetheless, many people have expressed deep concerned, and I'm doing the same, only with actual data.

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.

>> And I am having serious misgivings about posting it, nonetheless, many people have expressed deep concerned, and I'm doing the same, only with actual data.

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 no "actual data" in your comment above

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,

People spend years studying epidemiology. No one can write a comment here that will teach you how to do it properly.

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.

I don't need to be an aeronautic engineer to know that a person with no such background should not throw hunches about designing planes.

> There is in http://nrg.cs.ucl.ac.uk/mjh/covid19/#w which I said matches what I've found rather well

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."

You are absolutely right that the early phase of logistic growth is practically indistinguishable from exponential growth. As to what extent this observation is useful when applied to the current situation, I don't know – I am a mathematician, not an epidemiologist. But as a rough guide to aid understanding, I think it probably has some merit.

Thanks, it's nice to have an expert around! I was going to plot the divergence between the two but got seriously despondent and hit the bottle. Trying to sober up now.

May I ask, what area do you work in, numerical, discrete, logic, other?

The "behind Italy" number must refer to the number of days after Italy the area reached 100 confirmed cases. Because those numbers obviously don't reflect an extrapolation of the exponential function even 10 days into the future, even corrected for a reduction in the coefficient when numbers grow above 10.000.

I get that your comment is coming from a good place, because the threat of misinformation is real. But this is an “Appeal to Authority” argument. I’d rather have an informed discussion among smart people, even if they’re not experts in the specific field. I’d rather be able to weigh the input of many people. Especially in a case where the experts haven’t done a great job at communicating or understanding the severity of this.

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.

Thank you. To me, that's what it's about. I'd rather try and fail and learn from that, but people just telling me to put a sock in it isn't something I can learn from.

>I’d rather have an informed discussion among smart people

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!"

Original poster here.

> 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.

> 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.

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.

> No, you just made a large number of flaws in your initial analysis

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.

This conversation can serve no useful purpose anymore. It's clear that you will never admit that there's anything wrong with your "analysis" (too big a word for pasting data in a spreadsheet), as far as you are concerned, you are a bloody genius (and helping people!) by plotting a bunch of points in a log scale. And of course any criticism of your methods can only be persecution and for "psychological reasons"... I wonder why anybody would post in a discussion forum if they are going to completely ignore any feedback or criticism (and in a very Trump manner say they're victims of personal persecution). Maybe just validation? I don't know, I'm not a psychologist.

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.

It does seem to serve no good purpose when we are talking across each other. For example I clearly and immediately modified my original post to state there was a risk of error because I was using the wrong curve (https://news.ycombinator.com/item?id=22645793).

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?

aaaand silence. I should have known better than to ask.

counter points:

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!

>> Many doctors lack training in epidemiology. Many epidemiologists aren't actual doctors.

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 would suggest epidemiologists provide some set of problems all this useless but willing pile of software engineers could apply themselves to. For example, scraping data from mismatched data sources or implementing specific hypothetical models. Even if it's only marginally useful, it would give them something to do other then misinformation.

That is what epidemiologists do nowdays and did from the beginning. They don't calculate models on paper anymore.

It's likely that epidemiologists already use those tools. They are, after all, a sort of applied statisticians.

Indeed, you have a point. But in the US at least we have a serious trust issue with what we hear. I believe Fauci, but he still has to play by somebody else's rules.

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.

Epidemiologist are working, and they are saying the same as throwaway_pdp09 - that this is bad. If throwaway_pdp09 were claiming otherwise then it would be a different story. No country is going to change their country's healthcare response plan based on some random conjecturing on HN, and regardless of any numbers that pop out here, the instructions are the same - self-isolate until mid-April, at the soonest.

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.

Thank you, I appreciate this a a lot.

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.

Who are these experts you speak of? Are they in US? UK? Italy? Spain?

Some US specific data is at https://covidactnow.org/

China

It is not an exponential, it's a logistic curve, just approximate with the SIS model, it's easier.

Yeah I knew by Sunday that tomorrow we're hitting 30,000 cases in Spain. My assumptions worked. And it's just the tip, because I don't know anything about whether there's been randomized sampling, so who knows about the normalization.

I just can't believe this. No one listens and this is in-your-face obvious.

3blue1brown did a good video explaining the logistic shape of pandemic curves: https://youtu.be/Kas0tIxDvrg

My key takeaway was that once you see the inflection point, you know that you’ll have roughly 2x the number infections by the time it’s done.

I'm wondering if the claim that we will have two times the number infections after the inflection point is seen will actually be worse. I have a feeling when people see there is an inflection point, they will relax their precautions and make it worse.

> I just can't believe this. No one listens and this is in-your-face obvious

Can you clarify. I can modify the top post (mine) if you're saying I'm overstating things. Happy to be wrong here.

I mean, it's just frustration. You have to expect this from a novel disease once you realize its R0 has been characterized, it's greater than 2, it's airborne and asymptomatic patients can transmit it. What have been people expecting? I'm a physicist, I've seen this kind of growth. We also have this totalitarian principle which states that "Everything not forbidden is compulsory", I see no fundamental difference, it has to happen. Just don't act like in ordinary politics where problems can be waved away, Nature doesn't care about that.

Oh heck yes. ISWYM and that's the way I treated it - seriously, starting 2 months ago, for exactly those reasons. I just don't get it either, it's like head-meets-wall every time you try to tell anybody. It's so depressing.

(what I thought you might have meant was a criticism of me misusing maths and you were getting pissed off telling people it was a sigmoid not an exponential).

ISWYM - I see what you mean?

Yes. See http://muller-godschalk.com/acronyms.html for an excessively long list - I've not seen 90% of these but a few are useful.

OK but how much larger than 2? Is it 2,5 or is it something like 5? Because the narrative so far doesn't fit the reality.

Nobody knows for sure, of course, but my understanding is that it is thought to be between 2.0 and 2.6 in the “do nothing” scenario. Which is scary big: See the Ferguson paper that I referenced in a different comment.

Thanks, will do.

I realise it's actually a sigmoid but an early decent approximation is an exponential, no?

(edit: good catch though)

Apparently not with this when you're early. I took it seriously when a third degree polynomial fit worked too well and then suddenly didn't.

OK, looking at this, the curve is upwards without inflection. What would you expect to get if you fitted a D3 poly to it? Why would you even try? Serious Q.

First thing you do is try to make sense of the data, if I can model 15 points with 2, 3, 4 parameters and further ones don't add any improvement I'm onto something. I've always done polynomials first, you can see if there's something much simpler than Lagrange interpolation, which in my experience means you have to go straight to the literature about that kind of problem.

(Of course this works as long as you aren't seeing any peaks)

oefrha, for some reason I can't reply to your comment just yet so I'll post it here: Of course. This is just my own criterion when I start with a bunch of data I know nothing about. Is there a complicated law behind them or maybe not? Am I seeing noise or there's a signal? You can fit anything smooth with a low order polynomial, so maybe there's a differentiable function right in front of you. It's just something I do and if you see no reason for it I might even agree with you on that. It never ends up in the model, though. It's just something I find useful at a preliminary stage.

OK, We're not being responsible doing this and I'm not qualified to do it. I just have been fitting the data and maybe I've been lucky and I'm just an idiot. Having said that, this is also my problem, I have right now a hotspot in the vicinity and I'm worried. I just want to have a quantitative idea of what's going on. Now it's your job as experts to explain what's going on to the politicians and make sure they hear and they can do the right thing. I wouldn't be worried if the messages had matched the numbers when this started, they didn't. At some point we need someone to tell us "we're at this point and tomorrow we'll be at that point". I shouldn't feel compelled to find out by myself what all this is about. I feel awful by posting all this, I'm perfectly aware that non-experts in a field spout nonsense, but this is a very nonsensical situation and I'm tired of hearing we've been doing fine.

I mean, any smooth curve (without singularities) is well approximated by simple polynomials locally, which is simply its power series expansion. Doesn’t mean simple polynomials would necessarily hold much predicative power, however well they fit the current data. Higher order terms, if any, will come to dominate.

A logistic curve is indistinguishable from an exponential when you're "early". The earlier you are, the closer they are.

Yes, I found out that later, but while we were in the low hundreds fitting to an exponential didn't seem to work very well in comparison to a polynomial (it was overestimating). It wasn't a quadratic either. There it jumped and I remembered I should try population growth models. I started with Gompertz, which is pretty much indistinguishable from logistic at this stage. Then I found out that logistic was more stable and parameters seemed to converge for a few days, so I learned about SIR/SIS. And then I figured I had some idea about what to expect in the following few days.

I stopped trusting any of my rudimentary models by Sunday+incubation, because conditions changed then. Which data, why I trusted them and the set of assumptions taken to consider them representative for the shape of the time series/distribution, that's something I don't think I should share here at this point. In any case I'm used to extract some information from imperfect data, you approximate and estimate. I mean there was by then 12, then 16, then 30 positives in a nursery home next to my door, and no lockdown measures, now there's 100, an improvised hospital there and I'm not allowed go out except for buying groceries. Two weeks ago I needed some picture which I wasn't getting anywhere, the alternative was freaking out after comparing what the authorities were saying with what I knew about the situation. Now they are making sense and the measures in place too, so I expect it getting much better soon.

I don't want to go into epidemiology and I want to respect and trust blindly the epidemiologists, but in any field you expect the professionals having predictive skills working before your eyes, that's their job. Hearing that we should wait for the post-mortem to know where we are now, I mean, come on... And now I'll just shut up.

Nursing home, obviously.

Worth linking to this page, which is regularly updated with the sort of analyses you refer to: http://nrg.cs.ucl.ac.uk/mjh/covid19/

Scary that the US (especially some areas) and Switzerland are starting to track above Italy:

http://nrg.cs.ucl.ac.uk/mjh/covid19/20mar2020/covid-us-norm....

But how much of the US’s growth is due to us finally having some (albeit not enough) testing?

This is key. We really can't compare countries confirmed case numbers directly because they are operating very different testing programmes. Just look at Germany. More confirmed cases than the USA, but a quarter of the deaths. Germany also has 80% as many cases as Spain, but about 5% as many deaths. All that, without a severe lockdown.

Clearly something completely different is going on there than in many other countries, and it seems they have an extremely effective and well organised testing system. They're testing about 160,000 people a week. In comparison, the UK to date has only tested around 50,000 people in total so no wonder our case numbers are far lower, even though we have double the number of deaths.

So Germany is an outlier, but it just goes to show even for other countries you can't just compare the curves and draw any definitive conclusions without also looking at the testing regime. This is where the experts with their detailed population models and simulation tools come in.

I think what is going on is a combination of relatively good testing capacity from the beginning and mostly young people infected, which due to the testing were prevented from spreading it to older demographics. See https://experience.arcgis.com/experience/478220a4c454480e823...

Also there is the purely anecdotal hypothesis of a cultural difference between for example Italy, Spain and Germany. In the southern countries it seems like older people have quite an active social life compared to Germany.

There are also far more old people living with young people in Italy and Spain than in Germany.

We could possibly clean up the numbers, assuming the death rate is predictable across regions.

Take numbers from a region where testing is frequent, say the death rate is 2%. Then scale the numbers in a region with lack of testing to match the 2% death rate.

Not perfect, as there are external factors like hospital capacity/quality and existing population health.

However, it should give a closer approximation of total cases where politicians fail to provide adequate testing.

Well unfortunately US has much higher rate of obesity, diabetes and other factors that will increase mortality so would be pretty hard.

China has a much higher rate of smokers, poor air quality, and existing respiratory disease.

Do you have any statistics pointing to obesity as an increased risk factor for a respritory disease?

High Blood pressure was a big factor from what I read. There is very strong correlation between obesity and hypertension.

Each country has it's own date for when "testing came online", and it seems like starting from then might make things more comparable, despite differences in testing capacity and strategy. Another approach might be to on start looking at the data after the number of cases is > 100.

Stop repeating German propaganda :(

https://www.youtube.com/watch?v=0M4kbPDHGR0&feature=youtu.be... turn on google auto translate

TLDR: Any comorbidities present at the time of death are reported as the main culprit. In other news https://www.thelancet.com/action/showPdf?pii=S0140-6736(20)3... Table 2: Treatments and outcomes. Sepsis 100%, Respiratory failure 98%. There you go, nobody dies from the virus alone, problem solved, 20x less deaths than comparable Spain.

Then why are basically all Corona deaths in Germany reported as having other illnesses too? If this claim were true, none of these deaths of sick old people would have been reported as being due to Corona. Do you have any evidence this is more than speculation (at least the english translation also makes it sound like it is worded speculation, not a claim of fact), and that someone who was known to be infected and died was not reported?

I keep asking people about this because I'd for sure would like to know if it were happening, but the only two sources I've seen sounded like speculation by Italian officials that at least as absolute as it is reported in other languages does not match the actual reporting here as far as I can tell.

It is the health care system like number of ICUs, training of doctors and stuff like not being able to get antibiotics simply over the counter like in Italy. If my memory serves me right e.g. in Italy 29% have a resistance to antibiotics, in Germany only 0.4%.

People don't become resistant to antibiotics, bacteria do and resistant bacteria in Italy would just spread to Germany. Anyway Covid-19 is a virus, not a bacterium.

While its true SARS-CoV-2 is a virus, opportunistic bacterial pneumonia is a secondary complication of Covid19 which increases its lethality. So you need decent antibiotics too.

..and it's nasty, my wife only just recovered from it a week ago after a total of 8 weeks in bed.

Related, there's also the distinct possibility that many had Covid-19 well before it was in public consciousness.

Look for ILI (Influenza-like Illness) stats which were appreciably higher in January. The obvious inference is that many had Covid-19 before it was recognised as such.

This might be a good thing.

I think this is underrated. This thing must have been spreading since November here.

I know a person who back in January acquired something like pneumonia and a bad fever, went to the hospital for awhile and tests were negative for flu, etc. doctors just didn’t know what it was. The person has COPD however.

But anecdotally I’ve noticed quite a few more colleagues over the last few months out sick “with the flu or something”. No idea.

They’re still only testing people with symptoms. We need a randomizes test and a test for antibodies to have any idea how much it has already spread.

If it’s as contagious as they say and spreads in asymptotic people then why wouldn’t there be a significant population that has acquired and recovered from this? Many probably with very mild symptoms.

I could just be in denial right now as well.

If COVID19 was spreading in the US since November then the US hospital system should have been overwhelmed by now with intensive care patients. The first infection in China was traced back to November and they were building hospitals by early February.

The only places I've seen the first US infection being dated that early are conspiracies on Chinese social media that claim the origin of COVID19 to be the US instead of Wuhan. The only way that theory works is to date the first infection in the US and claim that Americans bought it into China during the Wuhan Military games. That theory is easy enough to debunk with common sense because if the US had COVID first the hospitals would be completed overwhelmed before China's was, but it's not the first time wishful thinking's buried common sense in Chinese history.

"spreading in the US since November", and "there's a new, highly infectious respiratory disease since November" are two very different things.

I don't know anyone that credibly believes it was in the US in November. We did, however, that something was amiss that early, and if the US had a pandemic response team who's job was to see something like this coming, we could have been getting testing ready since then.

”If COVID19 was spreading in the US since November then the US hospital system should have been overwhelmed by now with intensive care patients.”

Or the fatality and hospitalization rates have been overestimated due to severity bias.

We know this to be true, actually. We just don’t have good estimates of what the true rates are.

It wouldn’t be shocking at all to find out the virus is already widespread, and only slightly more dangerous than influenza.

Even in Italy, if there is widespread infection, the ~4k dead would reflect a low mortality rate.

It would be shocking to epidemiologists. All their confidence intervals are well above influenza. They are fully aware of all caveats amateur people talk about here and don't think it is like normal flu.

In italy in hard hit city, they were unable to cremate people fast enough and army had to move bodies. Influenza does not cause that.

Everything I said above was directly taken from non-amateur epidemiologists. I have invented nothing.

The error bars on CFR estimates run from fractions of a percent to a few percent. In countries that have tested large numbers of people who are not severely ill, the case fatality rates have been at the low end of that range.

This isn’t “shocking to epidemiologists”, it’s shocking only to people who dismiss basic facts and try to use irrelevant distractions (like cremation rates) to incite fear.

Epidemiologists say that influenza is easier to control. Irrelevant distraction relevant to topic says that only school here and there has to be closed to contain it. Epidemiologists say on that that one infected person with influenza infects less people then one infected with corona.

Epidemiologists do count with overwhelmed hospitals and services, excess deaths due that that, excess deaths on unrelated heath problems. It not just something they talk about, they have it in their models too. This does not happen with influenza, because it is easier to control. Irrelevant distraction there is Italy or Spain lately.

Epidemiologists do talk also about costs after epidemic ends, in treatment of people who will need care for long.

What amateurs do is to ignore all the above, pick up case fatality ration in well run non-overwhelmed health care system and pretend that is situation same as influenza to feel good.

” Epidemiologists say that influenza is easier to control.”

Not that I have heard of. You’re going to have to cite some evidence here.

(They’re both respiratory viruses with similar R0, so your evidence needs to be pretty extraordinary to be convincing.)

21 days to death, doubles every 2.5 days, exponential growth. Do the maths.

You’d think mild cases wouldn’t have the same viral load as the severe cases. Even if you’re technology contagious it may be less for the mild cases.

Especially asymtomatic. You’d not be coughing or sneezing more than usual which must have some effect.

Just a thought. No one seems to have mentioned this idea.

What is this idea exactly? That the virus rolled through Seattle and other areas with just a bunch of relatively mild cases and without a critical uptick in hospital load? I am not an epidemiologist but that does not seem realistic.

If you haven't heard about the Seattle Flu Study - this is a great read. Coronavirus was in Seattle before officials confirmed it. The federal government also prevented the researchers from doing more testing, delaying valuable time.

https://www.nytimes.com/2020/03/10/us/coronavirus-testing-de...

> If it’s as contagious as they say and spreads in asymptotic people then why wouldn’t there be a significant population that has acquired and recovered from this?

Because all the effects are limited by the time it takes for them to happen. We know the speed of growth, it's much faster than the expected recovery times. From all known COVID-19 cases the number of recovered are still dominated by the recovered in China.

   China: reported cases: 81,008
          dead:       3,255 
          recovered: 71,740

   Whole world: reported cases: 282,395
                dead:      11,822
                recovered: 93,189

Virus causing COVID-19 is "novel" meaning not existing among humans at all until a few months ago.

Seems like everyone I talk to either had something like that or knows people who did. It could be a huge effect.

> a person who back in January acquired something like pneumonia and a bad fever, went to the hospital for awhile and tests were negative for flu

That’s exactly what happened to my dad in February.

Yes it was the case for many people in February. They didn't have covid. Your dad is very high risk and should stay home. The doctors in UK don't need a test to diagnose covid in a patient with pneumonia. They see it in a second on an xray because it is so distinct. The Chinese doctors said the same. So do the Italians.

You are in denial.

If that were true, we should see a much higher proportion of tested people showing positive. Also we shouldn't see the proportion of people tested being positive shooting up, but we do. Those ILI stats were probably just Flu.

I believe that most of the current testing only shows active infections?

Yes, and it has shown initially small numbers of active infections, growing rapidly, as a proportion of those tested. If the virus was already widely distributed, we would expect it to be much closer to equilibrium.

Yeah I’m not sure what people are expecting to happen here. This virus has been circulating in the US for months and testing was limited. There’s surely many multiples of infected people than currently reported, and that’s true for any country that isn’t doing blanket testing of entire population.

Even if that (EDIT: ‘that’ being an idea that virus has been circulating for months) were true (which almost certainly it is not), so what? The only logical explanation for this is that percentage of severe cases relative to totally susceptible and infected is orders of magnitude lower, and now the total number of infected is so large that number of severe cases is starting to get noticeable. But that is not the picture we are getting from South Korea that had much more comprehensive testinG.

> Even if that were true (which almost certainly it is not)

What are you referring to? You don’t think the virus is already much more widespread in the US than currently reported numbers? I’m just making the observation that perhaps the large increases in US cases are more indicative of confirmation of already existing cases rather than necessarily being _new_ cases.

I am not arguing that virus is more widespread than the reported numbers, that much is obvious. I was replying to the idea that virus has been circulating “for months” undetected. With a doubling time of 5 days or so, with “for months” circulation we would be in a totally different territory right now.

> I was replying to the idea that virus has been circulating “for months” undetected.

I don’t think that’s crazy at all. We had our first case sometime in January, and only until very very recently have we had any significant travel restrictions. It’s very contagious and for a significant number of people there are no real symptoms, or if there are, nothing life threatening. I don’t think it’s crazy at all that it’s been working it’s way around the US population for months.

Maybe to put it another way, if we could instantly test al 300+M Americans right this instant, would you be surprised if say 20M or so tested positive or had had it?

> would you be surprised

Yes, I would be very surprised. Given what we know about severity distribution that means 2M hospitalizations and ~200K-1M ICU cases that we somehow missed.

That's a very good point, and the lack of tests had a secondary effect. Nobody here took it very seriously at first, because the numbers were very low.

Good question, I don't know. If it helps, a quick search got this https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/test...

See table at bottom.

Yes - absolute criminal negligence in the US and UK. Possibly Australia too.

The problem isn't just that a lot of people will die because the health system is swamped. That's hardly good news, but when you have a high peak you have a lot of people ill at the same time. And that means you run the risk of losing a significant percentage of critical workers in essential services including food, utilities, logistics, and IT.

Some will die, some will "just" be very ill and unable to work for 2-4 weeks. If you're lucky the numbers of seriously ill will be low enough to keep things running. Beyond that you're on a scale from frightening inconvenience and breakdown in some locations to the economic equivalent of critical organ failure.

Pushing any country through this is a huge and utterly irresponsible risk.

Original poster. I'm from the UK. Looking at the curves only and leaving out politics completely, the UK curve is by far the slowest growing of the 3, for whatever reason. It's the US that has the most to fear at this point, of the 3 countries.

UK policy is "testing for coronavirus is not needed if you are staying at home" for symptomatic suspected cases, so it's likely our stats are a bigger underestimate of actual infections than many other countries

UK number of deaths is two weeks behind Italy's. I'm quite sure that that growth will be consistent in the next two weeks in the UK -- namely, up to now, the growth characteristic remained the same across the countries with different policies, and it can be observed, from China and Italy, how long it takes for any policy to decrease the growth. Then check and compare the Italian measures with the UK for your estimate when the UK curve will flatten in the weeks that follow. When the flattening will start will indeed make a huge difference in the direct impact of the virus (the number of ill and dead) between the countries, due to the growth characteristics being what they are. Also see my other posts.

The number of "cases" reflects at the moment the capabilities of the whole medical system in each country (it's dependent on who is selected to be tested at all) and the tests used. The number of deaths hides less. Also, even if the specific numbers can't be compared 1 to 1 across the countries, the characteristics of the curves can.

The growth is fast but it is comparable, and the measures take time to affect it.

It’s taking a week for them to get test results at the NHS. So currently today’s results are from last weeks tests.

Of course that’s assuming the labs doing the testing are doing it first in first out.

The UK stopped testing anyone unless they were admitted to hospital for suspected coronavirus.

>Pushing any country through this is a huge and utterly irresponsible risk.

It's a risk, but the amount of hardship all the people who lose their jobs and business will endure if an indefinite lockdown is implemented is an absolute certainty. Poverty has lasting, lifelong negative health impacts.

If you want to have better comparable values, you should account that US have had too little testing and it just starts to increase. The US also has such health care system that it doesn't treat most potential patients the same, also potentially missing the cases completely. So the numbers of the "confirmed cases" in the US aren't to be trusted at the moment.

What's more sure, in most of the countries in the world, is that if the tests are available at all, and the patients happen to be admitted to the hospitals with the symptoms, they will be tested, and the percentage of deaths will be comparable in all countries, and we should be able to assume that the hospitals will report the deaths of the patients tested positive.

In short, now that the numbers of deaths increase, these numbers reflect better the issues we are facing than just the reported cases.

We should estimate the number of "comparable" cases across the countries or areas with different capabilities from the numbers of deaths, at least for the countries and areas which have at least enough tests to test those admitted with symptoms and report the dead, and once these numbers aren't too low to be too noisy.

I've demonstrated some of my simple calculations in my other messages, so you can check these too.

You have to bear in mind when doing any analysis of this that you shouldn’t treat the US as one country when comparing to Italy. The number of distinct outbreaks happening at once makes a big difference in the steepness of the curve in the beginning. Italy might have been 5-20 distinct outbreaks while what’s happening in the US looks much more like 50-100 separate outbreaks developing at once

> The number of distinct outbreaks happening at once makes a big difference in the steepness of the curve in the beginning.

How come? "Distinct outbreaks" just means the older state was underdiagnosed, not that the characteristics of the pandemics change.

"Beginning" might be the wrong term, as it's a bit into the beginning that it starts to make a difference.

Local saturation happens fairly quickly. Having 100 infected in one place is very much different to having 1 infection happening 100 different places when you look at it after a week

When you're looking at the "country" view, the "older state" gets lost in the transmissions happening from people crossing borders. So what I mean is, people have been flying back to the states with infections spread out over the entire country while cross-border spread has been much more localized in italy

Oh my. Thanks for that explanation. Given the indifference of the US administration it suggests things may be spreading even faster over there than I realised. We'll know in a week or so.

> people have been flying back to the states with infections spread out over the entire country while cross-border spread has been much more localized in italy

I still don't see the difference? Why do you think that the cross-border movement of the disease should be different in Europe compared to the US? Why do you think that the US should be worse off, if that is your point? Do you think people more fly back to the US compared to the people moving from country to country recently in Europe in the last period (including Italy)? Are you aware of how Europe functioned up to now? Many people worked in other countries, and they typically don't even have to fly to achieve that, especially not across the ocean.

I didn’t say anything about comparing the US to Europe? I was explicitly saying to keep it in mind when comparing the numbers from the US to Italy!

One hopefully positive indicator is that yesterday new daily deaths in the US were lower than the day before (49 vs 57) [0].

Obviously it's only one day so time will tell if that means anything or a pattern occurs. As others have said, the stats for number of people affected is so closely linked to testing rates which have been increasing that this could be a more indicative metric to follow.

[0] https://www.worldometers.info/coronavirus/country/us/

I'm sorry to say it, but that's just noise in the data. The death numbers in the United States will follow an exponential curve for a long time yet. Two weeks would be the absolute minimum before the shutdowns have an effect, but I think a month or two is a more realistic hope.

Yesterday's evening jump was unexpected. We jumped yesterday, instead of 1.5x the day before, all of the way to 2x.n If 10k is the new baseline and we return back to 1.5x we're going to exceed Italy in cases by Monday and exceed China by Wednesday.

Then, I fear, St Patrick's day partiers will only be measured by next Wednesday; 5 days incubation + 5 days to have test results.

You're basing your curve off faulty data. We haven't been doing testing so we don't really know. Until a week ago no one was doing anything about it and staying home like they are now. Again that will vastly throw off your curve. It's still too early. Give it a week and re-run your model.

> if I'm right the US is going to be reeling in just a few day.

considering how the UK handles the crisis it won't be far behind the US either.

Keep in mind that all the actions we’ve taken won’t show up for 14 days or longer since the already infected take time to show up.

The numbers mean nothing. All your seeing is the availability of testing kits, which has nothing to do with the number of infections. Don’t bother graphing a thing.

For the numbers to have meaning, a stochastic sample of the population should be tested at random, not people coming forward because they think they’re sick.

To answer your last question, yes it is irresponsible.

It would be irresponsible of him to show us his math?

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