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Comment by ThePhysicist | Hacker News Reader

It's disappointing that the WHO and national agencies seem not able to adequately inform the public about their assessment of the situations, the models they're using and the conclusions they're drawing.

Right now, the most shared article on Corona is written by a growth hacker from SF who cobbled together an Excel sheet and recommends it to people to decide whether they should close their companies or stay at home.

I'm not saying this is bad, I just think such recommendations and articles should come from people that have more experience with modeling such epidemics.

Fitting a dataset with an exponential function is easy, it just doesn't tell us much beyond the current growth rate. It's also a bit ridiculous to judge how bad the situation is in a given country without accounting for differences like the number of tests that are done and the overall population of these countries.

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It is surprising to me to. If this was Star Trek world, you'd have computational models built by experts shared and discussed the very first day, forming the very foundation of the discussion and policy around the virus.

I know of a certain company whose entire purpose is building and selling a product that lets you plug in equations of complex, dynamic systems and play with the models to understand and predict things. Like serious, industrial-grade stuff that's used to design chemical plants. They could easily get a virologist on call and model the spread more sensibly, or even individual countries in detail, and release that - which would give a basis for people to discuss what to do and when in a much better way that's happening now. They'd probably also save themselves a year's worth of marketing expenses. But I don't see them, or any of their competitors doing anything like this.

I'm thoroughly disappointed. Is the ArcGIS dashboard from John Hopkins University really the best our civilization has to offer in terms of informing people and evidence-based discussions? Because while I want to believe that our politicians have more sophisticated tools available, I'm pretty sure they're just looking at the same dashboards and same news stories as we do.

That kind of tool will give you accuracy without precision. There are so many unknown assumptions involved that a model like that is going to give at most qualitatively correct results.

People who model the effects of climate change, like Nate Silver and the 538 crew, run thousands of simulations and present a summary. Even then you can get hung up on the "unknown unknowns".

For instance 1970s estimates of nuclear reactor accidents assumed the main risk was that the pressure vessel was going to burst. That's an easy risk to control, so you hardly ever hear of a pressure vessel bursting at a chemical plant, but all the time people blow up or suck in storage tanks that can't handle 1 psi of under or overpressure.

Now really a legacy nuclear power plant melts down 100% of the time if the power goes out and they can't start the emergency generators. That happens a lot more than bursting pressure vessels!

I'm not saying someone should throw a model with 100 parameters and say it's the truth. But you could incrementally build up a model for questions like "should we close schools and when", refining it step by step and getting a range of outcomes, a probability distribution. Sure, the precision is still limited, but it's much, much better than how the discussion looks today: "oh, our government decided to close all schools for the next two weeks, I guess that buys us a couple of days, so we're maybe no longer 3 weeks behind Italy, but 4". Because that's the level of the discussion on the smarter end.

My point is: we have the tooling necessary to talk about this problem better, more accurately, more precisely, in real-time, and to cover more angles simultaneously - which would lead to better decisions. But we're not using it. At least not publicly.

(This is a part of a general issue of mine, that we absolutely suck at having any kind of data-based discussion; best what we do is exchange badly made charts and context-free data points. We should be exchanging and discussing whole dynamic models. But I suppose this wider issue can wait until we're done with the coronavirus mess.)

We’re in a real situation where choices (or the lack thereof) will have major consequences. If the best estimate we can make is a rough one, it’s still sensible to use it as a guide for our actions.

Industrial grade is the key word here. They have there customers, so they don't need "cheap" PR. They are specialized in chemical plants, usually professional organizations know there limits (conditions apply). Plus, the WHO and others have these models and tools as well. Plus the domain knowledge needed.

Why wo don't have anything from these bodies is a different story, so.

> Plus, the WHO and others have these models and tools as well.

Assuming they have[0], my core issue is: why won't they share them? Society is not 100% made of illiterate rubes who can only add (but not multiply) and read a line chart (and get confused by a log scale). There's enough people who could make use of more sophisticated information, including politicians and political advisors.

The discussion about e.g. whether a country should stop mass gatherings now or later would be infinitely better if it wasn't just "I think so" and "we have to flatten the curve!!11", but instead if one could say "look, the WHO model shows it won't help much", or "an alternative model proposed by virologist X suggests that it has most benefits if done today; here, look at this chart", especially if people could play with the models themselves and propose corrections.

[0] - I'm not convinced; I have the feeling most work in this space is still done with Excel or basic Python and R toolkits.

You are basically asking the same question I am. Why are they not sharing this kind of things? I think it is because the majority of the population is illiterate in that regard. This whole "exponetial growth modelling" going shows this pretty clearly. Not showing just opens a door they are not aware of I think.

also, I don't care about the tools they use. As lonfg as the results are correct.

“Flatten the curve” is a reference to a chart suggesting it has the most benefits if done today. I can imagine objections about the parameters of the chart or the authoritativeness of the source, but I haven’t seen anyone argue against it on those terms; the vast majority of counter arguments I’ve seen are casual dismissals that it could become so bad when things are mostly OK today.

> Society is not 100% made of illiterate rubes

> There's enough people who could make use of more sophisticated information, including politicians and political advisors.

Far be it from me to suggest politicians and political advisors are "illiterate rubes", however I can tell you with confidence that in my experience politicians don't like numbers and models and won't listen to you if you try to tell them about them. Fundamentally this means that even when they're not actually illiterate, they are functionally illiterate (unless of course said model serves their political interest exactly).

The trouble is that pretty much all of the data is garbage quality, and if you feed garbage into a computer you of course just get garbage out. The figures from Wuhan are particularly bad, probably just a function of their testing ramp-up until well after the peak, and the ones from Italy might not be much better. (The other problem is that China has taken the position that their numbers are an exact description of reality and their approach is a model for the rest of the world to follow, basically for domestic political reasons, and the WHO seems to be eagerly regurgitating this to the many people who'll listen.)

You can model the quality of your data too, with proper tools. No articles in public circulation do that, though.

Is there an open data portal for emergency room visit stats? That would probably paint a better picture of the real case count, by showing us whether there's a spike in visits for acute respiratory illness.

I wouldn't necessarily call the data "garbage" but the numbers are definitely _soft_.

But soft numbers are a common issue with many areas of public health. In particular, analysis of the current pandemic suffers from both a "numerator" and "denominator" problem (http://conflict.lshtm.ac.uk/page_83.htm)

I think many of us are accustomed to getting high-quality stats when studying a process. But that's often not possible with a fast-moving public health issue.

I think you have to acknowledge the limitations of the data, but it can still be helpful.

The thing I _don't_ like about the JHU dashboard is that it doesn't really let you slice the data in interesting ways. For example, I'd like to be able to click on a country or state/province and see temporal data. This doesn't seem like it would be that difficult, but maybe I'm naive.

I've tried doing some stuff with the Italian data but got a similar roadblock. For example, I'd be interested in knowing, among the ICU cases, if these cases were new (people arrived in the ICU with a diagnosis) or a worsening of the already existing patients (perhaps in sub-intensive care earlier).

It would help forming an idea on how the current situation (in addition to the new cases) is going.

I have heard through word-of-mouth (third hand) that there are models made by experts not being shared publicly (but presumably with governments etc).

This is definitely the case. I don't know if the lack of sharing is purely reflexive or because they want to avoid societal breakdown.

The third option is "because they don't want to be blamed for model error". Governments aren't necessarily competent, but you can try to get them to understand 5%/95% confidence intervals, at least in hindsight. If you publicly release a prediction, and then the real outcome is the 10% confidence line, you're probably going to be yelled at for being wrong regardless of the error bars.

Of course, if the center of the prediction is horrifying, "people don't understand confidence intervals" then becomes a case of avoiding societal breakdown.

>It is surprising to me to. If this was Star Trek world, you'd have computational models built by experts shared and discussed the very first day

The problem with Star Trek (esp. TNG) is that it doesn't reflect reality at all. It gives you the idea that the people in charge really know what they're doing and are competent. In our reality, this simply isn't the case.

I love watching TNG reruns to relax. It lets me fantasize about living in a well-run society run by mostly competent people. That's not the society I live in now, but this is why this form of entertainment is called "escapist".

Exactly this! I kind of understand the WHO, the broad public, including media, doesn't care about models and such things. Providing to much details can have the opposite effect, instead of calming things down it could spark more fear.

On the other hand, online stuff like said growth hacker can draw a lot of attention. This will end up in the mainstream. That diseases aren't exponential, who cares when exponential curves fit so nicely early on.

What could, and should, be done by large tech companies is preventing this. Google could make sure that every Corona related search ends up showing the WHO on top, followed by local sources like the CDC, Robert Koch, John Hopkins and so on. They could support these parties in getting them up in the search results. Facebook could do the same, as does Twitter. In Germany, you have to explicitly include Robert Koch or RKI in your search for the link to show. Even by doing so, earlier today it still wasn't the tip link. The rest is reporting crap written every time a new case was found somewhere.

And yet, nothing like that happens.

But the early stages of disease spread _are_ exponential. And I find that people really fail to understand the meaning of that, and how scary numbers like "new confirmed cases doubling every two days, 500 new cases in France yesterday" truly are.

Yes, but extrapolating the early exponential phase is, plain and simple, mathematically wrong. No idea why data scientists aren't going the first mile and look up the relevant functions before spitting data out. Total fail on their part, IMHO.

It's not wrong: it has produced extremely good predictive results day after day for total cases outside of China for months now. Using a simpler approximate function in place of a more complicated exact function is far from wrong, it is bread and butter for getting practical results in all areas of science.

It’s right until it’s totally wrong, e.g. for any country which has already peaked, of which there are several.

Countries that implemented extreme measures to slow the spread of the virus. It will be different for countries which haven't. The WHO and other health officials around the world are saying the same thing. This will spread to a large number of people. It's too late for containment, you can only hope to flatten the curve. That's why it's now a pandemic.

> This will spread to a large number of people. It's too late for containment, you can only hope to flatten the curve.

Follow the logic through.

South Korea is detecting ~100 cases per day on top of a total of 7,700 and 66 deaths. Total population is 50 million people.

How long do you shut down the economy over a disease where you are detecting 100 cases per day with a CFR of 0.8%?

Seems like this is a perfect example of the curve being too flat.

Unless you simultaneously ramp up your healthcare resources (what China did), even as "low" as 100 cases/day still has very real potential to go exponential (R0=2.5), overwhelm your healthcare, and kill 10% of our population if not more (especially if your population is aged).

I hear that, what I'm saying is, follow that logic through... If it's crucial to maintain strict protocols even during a rate of only 100 new cases/day, that means these protocols stay in place roughly... indefinitely.

Counting individual cases and deaths in a country of 300 million is nonsensical. You could do the same thing with the flu and you'd hit 20,000 deaths a year and you'd destroy the economy in the process.

IMO, the solution is best preparing to treat the number of people who do become severely ill from it, and getting the rest of the country fully back to work as quickly as possible.

Although I disagree strongly on the "kill 10% of your population" bit, which is just fear-mongering, it's kind of besides the point.

>Although I disagree strongly on the "kill 10% of your population" bit, which is just fear-mongering, it's kind of besides the point.

If every case went untreated, 10% is a reasonable guess about what the death rate would be. Unchecked growth would lead to almost every case going untreated. That's the penalty for allowing the number of active cases to rise above five times the number of open beds/ventilators.

Mathematically wrong is not a relevant category here. There is no "ground truth" that is approximated by an exponential growth. Every disease model is counterfactual "spread of the disease if no countermeasures were taken", etc...

Modelling the beginning of an epidemic as exponential growth is useful, sensible and empirically validated. As, countermeasures are taken during the beginning of the epidemic, for discussions of the effect of countermeasures, exponential is the correct model.

In fact, hopefully the countermeasures taken will be sufficient that the intrinsic disease dynamic will never get past the exponential stage...

I don't find any differentiation between early stage and later stage nor references to exponential functions:

https://en.wikipedia.org/wiki/Mathematical_modelling_of_infe...

If you find any source, idealy scientific papers on the subject, that shows that contagious diseases can be modelled with simple exponential functions, please share them. I am not an expert on that matter, so I would be really interessted to see some reliable sources confirming the use of exponential functions.

Ok, look at the differential equations on this page about the Kermack-McKendrick model: https://mathworld.wolfram.com/Kermack-McKendrickModel.html

Notice that the rate of change in the infected population (dI/dt) is proportional to the current number of infected (I) and the current number of susceptible (S).

In the early stages, when S is large and I is still small, this acts just like an exponential function in I.

For example, think about what happens when you double I the first several times. S stays relatively unchanged, and so dI/dt roughly doubles each time you double I.

Does that make sense?

Yeah, exactly. In the beginning exponential, later on not anymore. Without changing the equation. So, no, not exponential. Because, you know, very early on an exponential graph can approximated by something non-exponential. Would be utter BS, sure, so nobody does it.

Exponential growth. It's the growth phase that matters.

People keep mentioning the exponential because both of these curves, at an important point in time, grow very fast. Much faster than anything you're likely to encounter in daily life, so the public really doesn't have the requisite intuition.

But I can see you're not interested in understanding the point. You asked for equations, I gave you equations. Good day sir.

We are, I think, not disagreeing at all. May sound crazy, but bear with me a second here. I know that the initial phase of any epidemic is exponential. Key are these three small words, initial growth phase. Because they make all the difference, as every epidemic will ultimately peak.

These three words are simply to often drowned out in discussions about COVID-19. That's we you see extrapolations of this growth against, for example, the population of Italy. And this is dangerous. And I don't think we disagree here, I never disputed the exponential nature of the early phase.

We just shouldn't make the mistake in assuming everybody catches these three little words, especially not online.

The most critical question everybody is trying to answer right now is, when the peak will be. And whether this peak will to high for our medical infrastructure. With all the measures being taken, the conclusion seems to be the point will be too high. Hence the counter measures. So we should all do what we can to flatten the curve as much as possible. I never said anything else, and honestly, I don't see where are disagreeing here.

In your link, look at this:

https://en.wikipedia.org/wiki/Mathematical_modelling_of_infe...

S = N - I - R, and lets used normalized ratios, so i = I/N, r = R/N, etc... so the dynamics of i are given by:

di/dt = beta (1 - i - r) i - gamma i

di/dt = (beta - gamma) i - beta (i^2 + ri)

At the beginning of the disease, the ratio of infected and recovered is very small, say 1e-5 for 100 cases in a population of 10 million. So initially the second term is 1e-10 whereas the first is 1e-5. The initial dynamics of a new disease are given by:

di/dt ~ (beta - gamma) i

exponential growth. It will start deviating from exponential growth once i becomes large enough. If 10% of the population have had the disease it will deviate from exponential growth by 10%. Once it gets to half the population being infected or recovered you start seeing a real deviation.

In reality this might never happen, because we are taking a lot of measures to get beta down. So really you have something like

di/dt ~ (beta(t) - gamma) i

where beta(t) will capture all the countermeasures people take. If the countermeasures are effective and manage to push beta(t) below gamma before a large chunk of the population is infected, we might never see non-exponential behaviour from the disease. It will have an exponentially growing phase, and then an exponentially shrinking phase.

---

Here is just one paper I found with two seconds of google that looks at this, very recent, a bit basic:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962332/

But here is what it says in the abstract:

"The initial exponential growth rate of an epidemic is an important measure of the severeness of the epidemic, and is also closely related to the basic reproduction number."

"Classical compartmental transmission models assume exponential growth during the early phase of a well-mixed population"

"The initial exponential growth rate"

Key word, initial. Later on, not so much. Which is the whole point, pandemics aren't exponential over the complete run time. So, why not using these formulas at least in the models instead of using exponential equation limited by world population?

Edit:

"It will have an exponentially growing phase, and then an exponentially shrinking phase"

Confirming the point above. Math is a precise science, it's a while since I did stuff like that, but getting equations right or not used to be a big deal back than. And now we talking more serious things than just a grade in an exam.

Okay you're just trolling now. We are very much in the initial exponential phase of Covid-19. As I derived above, once you get to 10% you might start seeing deviations from exponential in the SIR model. The bump that you saw in China is not due to this deviation from exponential growth behaviour. We luckily never got close to 10% infection. It's due to getting to negative exponential growth. The beta(t) part of my answer above.

You're free to use whatever sensible model you want in your analysis, but you are criticising people for using exponential models. You have no arguments for that. Then you throw out Wikipedia, falsely claiming that exponential models are nowhere to be found there. Then you demand peer reviewed studies that use an exponential model, which I provide. But somehow that is not enough either.

In fact there are numerous assumptions in the SIR/exponential growth model. These might not hold. We might get slightly sub-exponential growth in some diseases. There is vast literature on that. But the base line in all the vast literature on this is that the most natural, obvious and common behaviour is exponential.

See my comment above. I don't think we really disagree.

The problem is, exponential for how long? For instance, all seems to indicate measures in Italy are taken too late: and the exponential growth is still happening there (almost 3 weeks after the problem started)

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline

    it = pd.read_json('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-andamento-nazionale.json', 
    convert_dates=True).set_index('data')
    plt.plot(it[['deceduti', 'totale_casi']])
    plt.xticks(it.index.values, rotation=90);

It's to early to say. The people who are popping up with symptoms now might have been infected up to two weeks ago.

If you look at the Wuhan data, you see that the lockdown worked, but the number of confirmed cases at the time continued to rise dramatically until 12 days after the lockdown went into effect. We are only three days in on the Italy lockdown.

https://jamanetwork.com/journals/jama/fullarticle/2762130

Figure 1.

> That diseases aren't exponential, who cares when exponential curves fit so nicely early on.

Shouting this point does more bad than good. Outbreaks are exponential in their initial stage, to an extremely good level of approximation. Sure, they're ultimately s-shaped, but we're far from the bend point. That the growth starts to deviate from the exponent a tiny bit when half of your country is sick isn't a useful observation right now.

And ignoring the long term s-shape is just wrong. It is completely different function. Facts matter.

You don't have to wait until half the country is sick, though. If you restrict movement, you see a sharp reduction in transmission when you start getting neighborhoods hitting 50%. Some will get there faster than others.

Yup. But to make that point, you have to show the trend that the disease will follow if you won't restrict movement - and shouting that "it's an S curve, not exponential" isn't particularly helpful here.

To be exact, it's a "logistic curve", which initially is exponential. From this 3Blue1Brown excellent video: https://www.youtube.com/watch?v=Kas0tIxDvrg

So how do you explain South Korea? Their cases have decisively peaked. So one of three things happened;

1) Herd immunity has kicked in, meaning a majority of people have already been infected and are recovering/recovered

2) Containment measures were effective against a highly communicative virus which can live in the air for 3 hours and on surfaces for up to 12 hours.

3) They just stopped testing and/or are lying about the results and are hiding the resulting health care burden.

IMO the only one here that doesn’t require competency on the part of the government is #1, making it by far the most likely scenario.

If #1 is true to any extent, then it means IFR is actually quite low, more like 2009’s H1N1.

The other fundamental question I keep going back and forth on is the final total population infection rate. For H1N1 and other flus it’s usually on the order of 10-30% of the population. (There are arguments why this one might be higher, although I think some people confuse exposure numbers with infection numbers...)

Containment supposedly will not meaningfully reduce this ultimate number but would just slow down how long it takes for everyone to ultimately be exposed.

So while there are very few new cases in China (and they are closing their temporary hospitals) what happens when people go back to work?

When comparing countries (e.g. Italy against South Korea), you have to consider many factors.

Cultural factors: how quickly do people change their habits, to do e.g. social distancing. Do people in public wear surgical masks. Population density, temperature, weather,...

Lying governments: I don't think South Korea or Italy are lying, but Iran is for sure, and China up to some point (at least early on). Italy stopped testing at some point, but this is known. South Korea increased testing over time.

Herd immunity: maybe a part of the population was already immune before (too early to say), but it couldn't have "kicked in" during this year as there was only one wave yet.

There is no question whatsoever that this corona virus IFR is a lot higher than the 2009 H1N1 flu, but lower than the 1918 H1N1 flu. When comparing countries / regions, you should consider the population age pyramid (Italy has more old people than South Korea) and how overwhelmed the hospitals are (I think quite bad in Italy and Wuhan).

Can you provide the source for “Italy stopped testing at some point”, please?

A co-worker from Italy told me. But you can find this for example here as well: https://www.aljazeera.com/news/2020/03/italy-south-korea-dif... "Italy started out testing widely, then narrowed the focus so that now, authorities do not have to process hundreds of thousands of tests." (I don't normally read Aljazeera... just found this using Google.) It matches what my co-worker told me. See also https://www.worldometers.info/coronavirus/covid-19-testing/ (I have no clue how reliable that source is)

I live in Italy and I'm not aware of any changes in the procedure for testing. If I read the worldometer link you provided what I see is that both on March 2nd and on March 9th Italy was second only to Korea in number of tests performed and that we moved from 386 tests per million people to 1005 in the same period. What am I reading wrong?

> The rest is reporting crap written every time a new case was found somewhere.

This is the problem. Not that our institutions are failing to deal with the problem, but that our media has vastly overblown the whole situation in order to scare everyone witless so they'll keep clicking on news updates and watch those ads.

With the secondary effect, of course, that all searches for information end up at a media outlet trying to scare people, and not the appropriate primary source of suitable information.

And the tertiary effect that an institution presenting non-sensationalist data based on research is dismissed as an "inadequate response" if it doesn't match the hyperbolic media frenzy.

We should be shutting down newspapers, not schools.

+1! And just to be sure, I would add social media.

But in all seriousness, I am absolutely disappointed with the reactions of Google, Facebook, Twitter and YouTube on this. Everybody made a well deserved fuzz about the 2016 US election. So these companies are absolutely aware of this. Now that the stakes are much higher. Still no response, no action.

What are you suggesting exactly? Do you want them to delete "alarmist" posts? That sounds like a recipe for a total collapse in trust in public institutions.

No, just giving the top search spots to updates from WHO, followed by the national, regional and local sources. Play with the search algorythm to push the more alarmist ones further down.

Help the above mentioned institutions to write their "content" in a social media optimized way. That would be agreat opportunity for these tech giants to gain trust they lost since 2016. Not doing anything right now moght very well have the opposite effect.

The WHO waited an extra month before declaring a pandemic, and US national sources are generally considered by experts to be understating the severity of the issue. What you want is the top slots to go to the experts, who are not necessarily ranked correspondingly to the hierarchy of political bodies. For example the top virologists are probably in labs and universities, not the Cabinet.

That's why I didn't inlcude the cabinets, did I? Just in Germany, the relvant data comes from the WHO (global picture), Robert Koch Institut (national), state health ministries (regional) and community health authorities (locally). All are relevant, the international numbers show the large picture, inlcuding the declining cases in China. The national ones provide more detailed insights into the country in question, while the regional and local ones are the most actionable for you. Which schools are closed, which policies are in place and whay. Stuff like that.

I would prefer to not get these from some journalist writig his tenth article today.

Personally, I am also very cautious with virologists taking the cable news and talk show turs right now. Where are they taking the time from to do so? If they are spokes people from institutions, it is different. But if their function is "professor at university X", my gut feeling tells me that they might like the public attention a tad too much.

>But if their function is "professor at university X", my gut feeling tells me that they might like the public attention a tad too much.

This is a very interesting perspective, which is very different from my own. Being in the US, my attitude is that officials are usually chosen based on criteria other than their skills in the domain they are administrating, and I would expect the best knowledge on any subject to be found among low-level people who deal with it professionally. I am also suspicious that officials might prioritize maintaining public order over disseminating true information, which caused many unnecessary deaths during the Spanish Flu.

>I would prefer to not get these from some journalist writing his tenth article today.

That's true enough. But I would rather have information from front-line experts than administrative officials.

Second that, I would love to hear from the frontline guys. But I don't. It is either elected officials (fair enough, that's part of their job) using input from said experts.

You don't hear from the frontline people / orgs, I assume because the are simply too busy right now. Which brings me back to the professor acting basically as a free agent. That should change.

The professor is the front-line guy. I'm talking about epidemiologists, not physicists. If he wasn't acting as a "free agent," then you would be back to hearing from elected officials, who are mired in compromises and not entirely trustworthy.

Which is true as well. Just realized I am more worried about the communications part of the crisis than about COVID-19 itself.

Communications are beast.

WHO has been shouting “take action now” the whole time and the official stance was “it could be stopped if governments act now.” Governments didn’t listen, so here we are. I fail to see what good declaring a month earlier would achieve, as governments clearly don’t give much of a crap about WHO advice unless the situation is seemingly spiraling out of control. One likely outcome of declaring one month earlier is being taken even less seriously; they can’t just re-emerge a month later and declare it’s really a pandemic this time.

if you think the WHO are not the best people to be dealing with this problem, that's a different problem than thinking that the WHO are not the best organisation to be dealing with this problem.

The WHO are literally the organisation we specify to deal with this problem. If they're staffed with the wrong people, that's a different problem.

I think it would have been terribly irresponsible to forcibly give the WHO top billing back when they were arguing nobody should restrict travel, or to suppress news of the preparations starting to take place in SV when official US authorities were universally saying it’s not a big deal.

Exactly. This. It's a Big Deal because of the media frenzy, not because of the inherent lethality or virality of the disease. We've seen similar pandemics before, without this media frenzy.

The WHO is making science-based responses, which are then perceived as "inadequate" by a public stoked into a frenzy by the media. The media are not motivated by public good, but by profit. The more scared you are, the more times you hit refresh.

We should definitely give WHO top billing on search results, because these are the people we've entrusted to do exactly the right thing in response to this situation. Giving top billing to profit-driven media companies is utterly irresponsible.

Or if we've got this wrong, then sack the WHO and appoint a half-dozen billionaires to their role. Same same.

Based on what we've seen, I'd be pretty comfortable having the Gates Foundation take over the WHO, if the world would accept it.

this is a really good idea. Give WHO top spot on any virus-related search.

Yes and no.

People keep turning to the media because our institutional outlets provide inadequate information for the majority of the population. The institutions default to saying nothing useful because they are scared of blow-back.

I check my city, county, state, and world health sites and there is simply inadequate for my needs. I want to know what closures are in place in my community. I want to know if asymptomatic transmission is real. I want to know if there is a plan to widen the testing criteria.

That's the problem, yes. I still ignore the media, others don't. Both approaches are fine. The thing is, the media doesn't have any other sources, do they? But the have to produce content, so they do. Not very helpfull in the quality department if you ask me.

I would say that broadly speaking, media is better than the institutional outlets for providing information.

You have to sift through the garbage, but the content exits. to use the topics I listed above as an examples, I can find more details on local closures and outbreaks in the local news the local department of public health. Similarly, I have found coverage and links to medical journal publications embedded in articles. Lastly, the institutions are avoiding disclosure of next steps or plans like the plague ( no pun intended). Either they have no plan for the days to come, or they are intentionally withholding information. On the other hand, there are a number of media outlets, mostly non-traditional, that are providing projections based on other counties.

the point I was making, really, is that media sites exist to make a profit, not to inform, while government-funded sites purely exist to inform.

There's a lot of money to be made from scaring people.

That is true and I agree. I would just point out that the unfortunate reality we live in is that the government sites are doing a terrible job of informing.

This is because quality science is not correlated with good marketing, virality of media (bad word choice), or how people feel about scientific content. Most people are woefully uneducated in science and it's showing on the internet especially.

I think that this spreadsheet is communicating several things well but I share it as a "reasonable bad case back of the envelope calculation". And if we mean the same article, it actually does a reasonable job at outlining the uncertainties around testing and national differences, and focuses on what is probably the most robustly comparable data point we have: Number of confirmed deaths.

The problem with models used by experts is that they are diverse hard to parameterize and that the scientific conclusions drawn are based on many different models not on one.

It's a very fluid situation.

Exactly why replacing by something simplistic doesn't fly. I'll go on a limb and say if I tried something similar in this guy's field, I would be destroyed for publishing it. And rightly so.

The post in question mostly presents observations about the base line models and data taken from reputable and cited sources. It didn't make up simplistic models on the fly. I would say it is breathless journalism.

I also would wish for better explanations from the scientists actively working on it. My first go to is RKI.

I wish this was the most shared resource as it is the most practical, educational and positive one I’ve seen so far.

https://www.flattenthecurve.com/

Do you have a link for this article BTW? the one that's so widely shared.

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

Not sure how "widely" shared it is, but it's a good, informative piece.

In my circles, it's one of the most discussed on the Recurse Zulip, it was shared by one of the Quakers on our Ministry committee (a college prof), and it was posted by one of the game developers I admire and follow on Twitter.

It does a good job of visual storytelling, the author obviously is good at being a "growth hacker," but it points to something deeply wrong when people are basing their decision-making on a document like this rather than communications from actual public health professionals.

Thanks!.

Call for the resignation of Tedros Adhanom Ghebreyesus, WHO Director General

http://chng.it/vj6Kwnsz8R

I really don't think that changing one leader will change anything about the organization. In order to fix the situation you would have to understand their culture from the bottom up, see why they don't care about making models public (as far as I know this is the media's fault for not publicizing models they have already published, maybe the WHO is innocent), figure out what they needed to change, and then replace several leaders, being careful to only replace the ones that are part of the problem.

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