
Flatten the Curve of Armchair Epidemiology - scott_s
https://medium.com/@noahhaber/flatten-the-curve-of-armchair-epidemiology-9aa8cf92d652
======
darepublic
> the recent outbreak indicates that severe DKE-19 primarily affects men ages
> 24–36 working in tech, for reasons unknown to scientists who are
> unaccountably also men.

Aren't flippant comments of this type also a virulent form of DKE?

~~~
MattGaiser
It is just a lazy way to rally supporters. Notice that it is the highlighted
part they want you to tweet out.

~~~
notacoward
That dismissal seems a bit lazy and flippant too. Plus attribution of motive.
Like it or not, there _is_ a grain of truth to the idea that techies (young
and old) are particularly prone to this kind of thing. We're rather notorious
for it. It's a natural and predictable consequence of having often been the
smartest person in the room, able to leap ahead of others who have studied a
subject (only slightly) longer. Equating income with intelligence doesn't help
either. It takes a while to learn that "opine first, study later" doesn't work
so well when dealing with fields equal or superior to our own in technical
depth and rigor requiring _years_ of effort to gain expertise, or that it's
actively dangerous to indulge such impulses when the stakes are far higher
than all but a handful of computer bugs.

~~~
MattGaiser
I attribute motivation because when it came time to choose one sentence in
their article to tweet, that was what they chose.

I don't deny there is some truth to it, but it seems perfectly suited for
gaining traction through the Twitter mobs...

------
ralfn
It's completely logical that engineers have professional deformation.

Society is in crisis and there are metrics. How can you realistically expect
engineers not to start debugging and interpreting the metrics?

Yes. Engineers aren't medical domain experts, but the experts and/or political
leadership failed. The models are not being shared. The process isn't
transparent, nor optimized for quick interventions based on incomplete
information. And claims and policies differ around the world. There is no
consensus among the experts.

The truth is, engineers might have more experience in how to operate during a
crisis like this. How to quickly asses risks in a world of incomplete and
sometimes contradictory information.

I wish the real experts were more convincing in their abilities. But in the
west this is the first time they deal with this kind of situation while it not
being a hypothetical. They is their first real experience with it as well.

~~~
notacoward
> engineers might have more experience in how to operate during a crisis like
> this.

That's probably more true for real engineers - the kind requiring
certification and insurance - than software engineers. Yet they hesitate to
speak over the epidemiologists. Why? Because they know how to be diligent and
not say things they can't back up.

> But in the west this is the first time they deal with this kind of situation
> while it not being a hypothetical.

I've personally had to work with epidemiologists in the context of a (minor)
measles outbreak. TB is an ongoing issue in many western countries. Norovirus.
Legionnaire's. Western epidemiologists have also been involved in outbreaks
elsewhere - e.g. SARS or Ebola. Their experience isn't any less because those
outbreaks weren't on their home turf. Sure, they don't have direct experience
with a pandemic, but _neither do the amateurs_. How can you think that people
who have spent years studying the theory _and_ getting real in-the-field
experience are less qualified than some random software developer or VC when
_neither_ have faced this before?

The fact that mistakes have been made doesn't mean there's nothing to their
field and anyone can do it. Are there no software bugs? Can just anyone come
in and fix those?

~~~
ralfn
>when neither have faced this before

>The fact that mistakes have been made doesn't mean there's nothing to their
field and anyone can do it

I was not making that claim. The premise is: should every non epidemiologist
keep their mouth shout and trust the domain experts blindly? Don't they have
the right in a democracy to question the science and therefor the legitimicy
of the suggested policies?

That's a hard sell, when the field of experts themselves have little
consensus.

I might be seeing this from a different bubble, over here in the Netherlands.
We have had our equivalent of the CDC show and explain their models and policy
choices in detail. We got powerpoints and everything.

Now before you make assumptions:
[https://www.weforum.org/agenda/2019/11/countries-
preparednes...](https://www.weforum.org/agenda/2019/11/countries-preparedness-
pandemics/) .. the Netherlands ranked 3rd in preparedness.

Yet, it turned into a shitshow. It was already going wrong in Italy, when they
claimed their models suggested we have it fully under control if only we wash
our hands, and they then let one of the biggest social events happen (think
mardi gras). Even though we already had our first cases.

Italy's expert initially said they had it under control. They suggested
cultural differences in health and healthcare made it such a bigger problem
for China than for them. Then it went to shit in Italy. Then the dutch experts
said, after a few cases, they had it completely under control and the reason
it went wrong in Italy was cultural differences in health and healthcare.
Yeah, racist ignorance.

And all their preparations didn't include supply chain management or the human
factor. For example: the relationship (worldwide) between the lockdowns and
the available of PPE -vs- the availability of PPE and the spread of the virus.
Because none of those things are part of the known models. So they just
exclude it entirely.

So, given all of that, yeah, I would like to see their homework in more
detail, and we should all put in the intellectual effort, to sort this stuff
out.

>Are there no software bugs?

More than any other engineering discipline. So software engineers do actually
have more experience debugging collapsing systems build on the wrong
assumptions.

>Can just anyone come in and fix those?

Yes, mostly. Its called a pull request.

------
chkaloon
BS, flim-flam, conspiracies, and Cliff Clavens will always flourish at times
like this. I remember before the Internet, mimeographed and Xerox'd flyers
were the primary transmission method.

The difference now is the Internet, and the fact that lack of leadership has
created a huge vacuum that this stuff happily fills.

------
SketchySeaBeast
Reminds me of the Olympics, when for a brief few weeks every 4 years, a ton of
people suddenly become experts in the skeleton bobsled.

~~~
dariusj18
Gotta lean in more, what a rookie!

------
NelsonMinar
A particularly urgent need to identify and quarantine VC investors who think
that because they financed a virally marketed app once, they are now experts
in Covid-19.

------
quaffapint
My daughter is studying to be an epidemiologist. I used to have to explain to
people what that was.

------
augustt
I don't think it's too surprising people are not interested in listening to
authorities given

1) the bungled response from the WHO, CDC, and of course Trump administration.

2) the lack of clear messaging about the steps forward. The curve was
flattened, now what?

------
thu2111
It's pretty apparent the writers don't understand epidemiology themselves.

What is this field, really?

It's not medicine. Its most famous practitioners often don't have any medical
training or background, e.g. Prof Ferguson who did his PhD in theoretical
physics. The assumptions it uses are of the level of medical complexity that a
small child can understand - essentially that people who spend time close to
each other infect each other. The models don't take as input the molecular
biology or DNA of a virus that's being simulated.

Arguably it's not science. A basic characteristic of science is that it tells
us something new about the world, something that can be proven true.
Experiments are fundamental to science for that reason. Epidemiology is about
the development of models. Models aren't science because they cannot ever tell
us something truly new or unexpected: a model is ultimately just a rendering
of its author's assumptions. When someone puts some formulas into R and copies
the results into a paper, the model is acting as a kind of fancy PowerPoint.
It's useful to illustrate the conclusions that follow from the assumptions,
but it no more lets us discover new things than PowerPoint does.

(n.b. Under this definition, a lot of what gets published as computer science
also isn't science but rather engineering. "Data science" on the other hand is
a tautology. That's a fine conclusion and one I'll happily defend.)

It often gets described as mathematics. It's not mathematics any more than
economics or coding a search engine is mathematics. Mathematicians publish new
theorems and proofs. The maths they develop may be of theoretical interest or
it may find applicability in other fields, but it ultimately stands alone.
Epidemiologists use maths as a part of their work but so do many other people.

As far as I can tell the field that resembles epidemiology most closely is
actually video game programming. The models epidemiologists vary in their
approach, but the most advanced ones attempt to simulate society by simulating
the interactions between people. That's why they can take into account things
like school closures or novel social policies. 3Blue1Brown has done a good
video where he implements an epidemiological model:

[https://www.youtube.com/watch?v=gxAaO2rsdIs](https://www.youtube.com/watch?v=gxAaO2rsdIs)

One of the top comments is _" I'm a professional epidemiologist (generally
focusing on modelling livestock diseases) and I'm angry at how amazingly good
your graphics are compared to anything I've come up with!"_ \- so what's being
done in that video isn't much different to what they do.

If you play Cities: Skylines then you actually end up running an
epidemiological model, because the inhabitants of your city are all simulated
individually. They can get sick and die, they can overwhelm hospitals or even
the road networks between them. For instance put a water intake pipe too near
to a sewage outlet and you'll have mass disease pretty quickly.

After watching epidemiologists flame each other in public for the last month,
disagree on basically everything, constantly make unfalsifiable claims, have
their few falsifiable predictions be indeed proven false and repeatedly
publish papers through news outlets, I've come to the conclusion that this
field structurally has no way to resolve disagreements or improve itself. The
epidemiological models being presented by places like Imperial or University
of Washington are no more reliable now than they were 20 years ago for the
foot-and-mouth epidemic in the UK. How can a field not get even slightly more
accurate over a period of 20 years? Well, because there's no incentive to.
Like academic economists they're rewarded for publishing papers, not being
correct.

The "men working in tech" this comment so snottily dresses down differ from
epidemiologists in some crucial ways. Many of them will work with data in a
context where being wrong will ultimately inflict financial damage on
themselves and their coworkers, or even crater their startup. Their ability to
work with data correctly matters to them in ways it doesn't really matter to
academics, whose jobs are perfectly safe even in this time of mass
unemployment and furloughs. Given the total failure of academics to correctly
predict real epidemics, I hope we see way more such men bring outside views to
what is otherwise a small and stagnant field.

~~~
notacoward
> A basic characteristic of science is that it tells us something new about
> the world, something that can be proven true. Experiments are fundamental to
> science for that reason.

A lot of science relies on observation without experiment. That's still enough
to support a cycle of hypothesis, test, etc. For example, where are the
_experiments_ in astrophysics? Seen anyone build a quasar recently?
Epidemiology is no different. From the very first days of the "pump handle"
cholera situation, epidemiologists have acted based on empirical observation.
Then they have built models based on empirical findings, and those models are
constantly checked against new findings. How is that not science? It seems
considerably _more_ scientific than most of what we in tech do.

~~~
thu2111
Astrophysics does have pretty considerable on-going problems as a result of
its inability to do real experiments. Even regular physics is heading the same
way. Dark matter is basically a giant admission that our models of the
universe don't match reality and we have no idea why or how to fix them.
String theory does trigger big debates about whether it's actually science or
not.

Astrophysics is saved as a science largely by the universe's close
relationship with more testable on-earth physics - the motion of planets is
Newtonian motion which can be experimented on locally, the physics inside a
star is something that can be replicated in a Tokamak, and so on. Sometimes
observations are all we have to go on, like with the light bending observation
that cemented Einstein as right, but that's rare. Mostly physics yields to
experiments. When it doesn't you do see decades without progress.

 _those models are constantly checked against new findings_

I think I'll have to dispute that. My research into epidemiology and its
history suggests that this part never seems to happen except by outsiders.
Models produce totally failed predictions, in fact often reality falls outside
even the uncertainty bounds that are already very generous.

Scientists would at this point stop and say, hmm, there seems to be a problem
with germ theory. Our predictions are wrong so we need a refined theory.
They'd then design experiments to figure out what that problem is.

Epidemiologists never do this, perhaps because they often don't have a medical
background. They can't simply switch contexts and start doing lab experiments.
So they simply declare themselves to be highly successful despite all the
evidence they weren't, and wait for the next outbreak. Then they jump up and
present often the same or very similar models to policymakers as "science",
and inform them if they don't immediately implement the epidemiologists
recommendations (which are always extreme) millions will die.

COVID is a case in point. Imperial presented a model that was in the words of
its author based on "thousands of lines of undocumented C written over 13
years ago to model flu pandemics":

[https://twitter.com/neil_ferguson/status/1241835454707699713...](https://twitter.com/neil_ferguson/status/1241835454707699713?lang=en)

It's rather ironic that we've seen endless attacks on people who "claim it's
just like the flu" when one of the world's leading epidemiologist literally
used a flu model to predict COVID.

But this statement implies epidemiological theories and models didn't really
change in 13 years. And when you look at these models in depth, that isn't a
surprise. They're astonishingly simple. There hasn't been any breakthrough in
germ theory from these people, and their models always predict massive
epidemics that don't actually happen. To explain this they'd need to do
medicine, or at least honestly admit that current theory is inadequate to
explain observed reality. They never do this, which is why in my eyes they
aren't scientists.

~~~
notacoward
> My research into epidemiology and its history

I'm sorry, but you get an absolutely huge [citation needed] for that. You're
making a _lot_ of highly prejudicial claims, with just about zero data to back
them up.

> thousands of lines of undocumented C written over 13 years ago to model flu
> pandemics

Is the fact that it's undocumented even relevant? That seems like severe
bikeshedding, in the sense of judging what you know instead of what matters.
Are there qualitative differences between how flu and coronaviruses spread? I
don't mean do the _diseases_ have different symptoms or prognosis, though I
see you're conflating the two. Does the _spread of the virus_ differ other
than in the sense of tweaking variables like R0? It's not immediately clear
why a model originally developed for flu would not be readily adaptable to
COVID-19, just like a CFD model can be adapted to different problems. It
doesn't require detailed medical or virological knowledge either - just
empirical observation, of which epidemiologists are quite capable.

BTW, let me ask you: do you consider economics a science? Because it seems
like your opposition to epidemiologists' conclusions seems to be based in
economic projections. How ironic.

~~~
thu2111
I can give many citations, but they're also an easily Googled matter of public
record. I'll provide some anyway.

On current events:

[https://www.spectator.co.uk/article/six-questions-that-
neil-...](https://www.spectator.co.uk/article/six-questions-that-neil-
ferguson-should-be-asked)

[https://judithcurry.com/2020/04/01/imperial-college-uk-
covid...](https://judithcurry.com/2020/04/01/imperial-college-uk-
covid-19-numbers-dont-seem-to-add-up/)

[https://twitter.com/AlexBerenson/status/1245748387359711234](https://twitter.com/AlexBerenson/status/1245748387359711234)

[https://medium.com/@wpegden/a-call-to-honesty-in-pandemic-
mo...](https://medium.com/@wpegden/a-call-to-honesty-in-pandemic-
modeling-5c156686a64b)

On a major prior disaster caused by listening to Imperial modellers:

[https://www.researchgate.net/publication/51683518_Destructiv...](https://www.researchgate.net/publication/51683518_Destructive_tension_Mathematics_versus_experience_-
_The_progress_and_control_of_the_2001_foot_and_mouth_disease_epidemic_in_Great_Britain)

[https://journals.plos.org/plosone/article/file?id=10.1371/jo...](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0076277&type=printable)

[http://pdfs.semanticscholar.org/5ab5/c0d4699e499e9c99a20c8d7...](http://pdfs.semanticscholar.org/5ab5/c0d4699e499e9c99a20c8d7aece68f52c5f4.pdf)

[https://www.telegraph.co.uk/news/2020/03/28/neil-ferguson-
sc...](https://www.telegraph.co.uk/news/2020/03/28/neil-ferguson-scientist-
convinced-boris-johnson-uk-coronavirus-lockdown-criticised/)

Here they are getting it all wrong on Zika:

[https://science.sciencemag.org/content/sci/353/6297/353.full...](https://science.sciencemag.org/content/sci/353/6297/353.full.pdf)

[https://www.sciencemag.org/news/2017/08/zika-has-all-
disappe...](https://www.sciencemag.org/news/2017/08/zika-has-all-disappeared-
americas-why#)

And finally, a page with many useful links under "further reading":

[https://lockdownsceptics.org/how-reliable-is-imperial-
colleg...](https://lockdownsceptics.org/how-reliable-is-imperial-colleges-
modelling/)

I hope that provides some interesting reading!

 _Is the fact that it 's undocumented even relevant?_

Absolutely. Ferguson asserted he couldn't reveal his code because nobody
except him knows how to run it. That means nothing based on it could have ever
been properly peer reviewed in 13 years. Over a month later he still hasn't
uploaded his code anywhere, despite its outputs driving extreme social
policies around the world. That's pathetic and deserves condemnation from
anyone who cares about the scientific method. Being able to peer review and
reproduce published results is critical - look at what happened in psychology
when people tried to replicate famous papers - but this is one more way
epidemiology doesn't even try to be scientific.

 _Does the spread of the virus differ other than in the sense of tweaking
variables like R0? ... It 's not immediately clear why a model originally
developed for flu would not be readily adaptable to COVID-19_

Nobody knows, do they. If anyone understood these viruses their models
wouldn't always be so wrong. We can easily assert that here because on this
forum we have nothing to lose.

At any rate, R0 and friends are the variables that define spread in these
models. There don't seem to be fundamentally different models for different
diseases. For instance SEIR models don't have any variable for different
categories of disease and they aren't labelled as for use only with e.g.
respiratory infections. Take a look for yourself:

[https://en.wikipedia.org/wiki/Compartmental_models_in_epidem...](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology)

All viruses are treated the same same except for the initial starting
variables. You can't exclude them whilst still asking if the spread of the
virus differs in these models - the answer is no, but the models are wrong so
that doesn't mean much about the real world.

 _It doesn 't require detailed medical or virological knowledge either - just
empirical observation, of which epidemiologists are quite capable._

That would require leaving their universities. The Imperial paper isn't based
on fieldwork but second hand anecdotes (e.g. "Professor Nicholas Hart,
personal communication" is cited as a source) and arbitrary guesses:

"We assume that symptomatic individuals are 50% more infectious than
asymptomatic individuals"

This assumption is justified nowhere. It's for sure not based on empirical
observation by the team.

 _BTW, let me ask you: do you consider economics a science?_

No, of course not. As you note, economics has all the same problems as
epidemiology. The public knows this and polls show economists aren't trusted.
People have clocked that economists can't predict the economy even though they
routinely claim otherwise. I hope epidemiology goes the same way quickly.

 _it seems like your opposition to epidemiologists ' conclusions seems to be
based in economic projections_

I haven't mentioned economic projections anywhere in this thread. My
opposition to their conclusions is based on the fact that they are treated as
scientific when in reality they aren't. That this has led to economic disaster
makes it a very important topic, but I can easily make the same case about
less disastrous fields like nutrition 'science' or psychology. And if you
browse my comment history (pre COVID), you'll see I have. COVID is also a
social disaster, look at all the stories of the cops going full 1984 to see
that.

