trentmkelly/LessWrong-43k huggingface dataset:
Model estimating the number of infected persons in the bay area
by Eli Tyre — 2020-03-09T05:31:44.002Z

[Edit: I already found one error in my spreadsheet, and adjusted the incubation rate, which decreased my results by an order of magnitude. My up to date spreadsheet is here, but heed it at your own risk.]

[Epistemic status: Quite uncertain. It seems plausible that I made a major math error and this model is flat-out wrong, or that some of the inputs I used were very off. Best to think of this as a draft.]

[Thank you to Elizabeth Garrett, Luke Raskopf , jimrandomh, and PeterH.]

In my coronavirus planning, the crux between different actions is often "how many people are infected (as opposed to symptomatic) on a given day?" (For instance, when 0.5% of the Bay area population is infected, I'm going to stop going to the gym.)

This post walks through the model that I'm using to estimate current infection rates. I'd be grateful for anyone suggesting improvements, nitpicking the inputs, and especially correcting errors.

I'm computing my estimates in this messy spreadsheet, which is automatically importing data from John Hopkins CSSE's github repo. (Thanks PeterH!)

Basic argument

(This model is a variation of one that Elizabeth Garrett shared with me. Please give credit where credit is due.)

My goal is to estimate the number of people who are infected (who are carriers of the disease) rather than the number of people who are currently suffering symptoms. Here I'm going to walk through a series of steps, starting from the number of confirmed cases in a location, and derive and estimate of the number of infected persons in that population.

Use diagnosis rate and number of confirmed cases, to get the total number of symptomatic cases

To estimate the number of people currently infected, I start with the number of new cases that that were diagnosed in the past doubling period.

But not all the people who developed symptoms are confirmed as having the disease. Presumably some fraction (less than one) of all people who develop symptoms are successfully diagnosed. But if you know what that fraction (the diagnosis rate or confirmation rate) is, you can get the total number of cases by multiplying the confirmed number of cases by one over the diagnosis rate.

total cases that became symptomatic in the past doubling period = cases confirmed in the most recent doubling period * 1 / confirmation rate

Use doubling time and recent daily cases, to get the...
It's Time To Stop Dancing
by jefftk — 2020-03-09T03:30:02.494Z

At this point it is likely that the coronavirus will spread widely,
and the

majority
of people will get it. Still, it matters enormously whether we all
get it at once, or over a longer period. This is a disease where a
lot of people need hospitalization for respiratory problems, and if it
spreads too quickly our medical facilities won't be able to keep up.
By slowing the spread we lower the peak number of people who are sick
at one time, making much better use of our medical capacity:

CDC

Looking back at the 1918 flu
pandemic, efforts to reduce social contact were relatively
successful at blunting the peak. Public
health interventions and epidemic intensity during the 1918 influenza
pandemic (Hatchett et. al. 2007) compares the responses of
different cities, and gives the difference between the experiences of
Philadelphia and St. Louis as an especially clear illustration:

The coronavirus is spreading through multiple communities around the
US, including Seattle, San Francisco, and New York. Because of a
combination of how far behind we are on testing, how long the
incubation period is, and how these cases can seem relatively
unremarkable at first, it is likely to also be spreading in many other
communities. This means we shouldn't wait until it's clear that it's
spreading in our community to start taking steps to reduce the
potential transmission rate.

Social dance puts people in close contact with a lot of other people.
You're touching hands, and breathing close to each other's faces.
Good hand washing can help, but it's probably not enough. The tango
community has seen the coronavirus spreading
at their events and I don't think tango is any more prone to this
than others.

The Country Dance and Song Society is recommending that dances
cancel if their local schools are cancelling, but I don't think this is
enough. Because school cancellation often
means that grandparents will be watching the children instead,
older people are especially vulnerable to this disease, and children
are not, even places with extensive community spread are often not
cancelling school.

When there's snow or ice, where it would be dangerous for people to
travel to a dance, we cancel. The coronavirus is a different kind of
danger, a less visible one, but continuing to hold dances would not be
responsible. Contra dances in Seattle, the Bay Area, Por...
How can we extrapolate the true prevalence of a disease, given available information?
by Elizabeth — 2020-03-09T00:16:45.611Z

Note: a similar question got more attention here so maybe check that out.

The motivation here is COVID-19, but I think there are useful general models in the area.

I've made a lot of risk assessment models over the last week, most of which depend on knowing the true infection rate of a population. That's difficult to pin down at the best of times, but especially in the case of COVID-19. In the country I'm most familiar with, the US, there simply aren't enough tests performed to provide good prevalence information. This post is for models extrapolating the true prevalence of a disease from information you have on hand.

This is an exploration thread, so don't worry about it not being rigorous or defensible enough. I'll be posting my own as an example in the answers section.
At what point does disease spread stop being well-modeled by an exponential function?
by Eli Tyre — 2020-03-08T23:53:48.342Z

Early in the spread of a new disease, its growth rate is exponential. But obviously, no disease can maintain that rate forever, it will soon run out of new people to infect. It has to level off at some point.

At what point (at what percent of the population infected?), does exponential spread become unrealistic?

Secondarily, how should you model the spread after that point?

(My very naive idea is to just model the up to 50% as exponential, and then model the next 50% as the inverse of the first half. (i.e. If weeks 1, 2, 3, and 4 had 6.25%, 12.5%, 25%, 50% infection rates, then project that weeks 5, 6 and 7, will have 75%, 87.5%, and 93.75% infection rates.) How good an approximation would that be?)

tags: coronavirsus, COVID-19
Quadratic models and (un)falsified data
by Bucky — 2020-03-08T23:34:58.128Z

On 5th Feb a commenter on Reddit posted that the coronavirus cases in China were following a suspiciously accurate quadratic curve, implying that China was making up their data and weren’t even bothering to hide it particularly well.

This set off my bullshit sensors fairly strongly so I wanted to check it out.

Having looked at the data myself, there may be other reasons why the Chinese data are not accurate but I think the quadratic pattern provides no evidence in favour of falsification.

I doubt people are making many decisions based on that post but possibly looking at where the statistics went wrong may be beneficial.

R^2

It turns out that the claim of a “near perfect” model was based on a very high R2 value (0.9995).

R2 is often chosen to summarise how precisely a regression fits the data – it tells you how much of the variance in the data is explained by the equation. Surely if only 1 part in 2,000 isn’t explained by the model that indicates that the data is fabricated?

The first thing to note is that "variance" is a technical term which isn't the same thing as the natural understanding of "variation". If you don't appreciate this then R2=0.9995 will seem more impressive than it is. In particular, variance isn't the deviation from the mean, but the

squared

deviation. In the more natural understanding, the quadratic model explains 44 parts in 45 of the deviation from the mean, not 1,999 in 2,000. This is still pretty good but seems like less evidence for fabrication.

An alternative explanation of the R2 value is that it compares two models:

1. The model that you are trying to fit to the data

2. The model where the value of y is expected to be the same for all values of x (the mean of the y values)

A high R2 value is telling us that model 1 is a much better fit that model 2.

However in our case we already know model 2 is going to be a terrible, terrible, terrible fit to our data. The y value used in the regression is total cases so far. So model 2 represents some number of cases having already been identified at the beginning of the time period in question and no more cases occurring during the time period.

So saying that model 1 accounts for 44 parts in 45 of the deviation between the data and model 2 doesn’t really tell me much – model 2 is a lost cause.

New cases per day

The proble...
March Coronavirus Open Thread
by Elizabeth — 2020-03-08T22:45:07.348Z

This thread was created on 3/8/2020, or approximately one million years ago in virus time. It’s getting pretty bloated now, and a lot of things that were high value at the time have been eclipsed by events, making karma not a very useful sorting tool. So I’m declaring this thread finished, and asking everyone to move over to the April Coronavirus Open Thread.

Interested in what happened in this thread? Here’s the timeless or not-yet-eclipsed highlights:

Scott Alexander

comes up with

Hammer and Dance 6 days before Tomas Pueyo
Spiracular on why SARS-Cov-2 is

unlikely

to be lab-created.

Two

documents

collating estimates of basic epidemiological parameters, in response to

this thread

Discussion

on whether the tuberculosis vaccine provides protection against COVID-19.

Suggestive evidence

that COVID-19 removes sense of taste and smell.

Could

copper tape be net harmful?
Open & Welcome Thread - March 2020
by habryka — 2020-03-08T22:06:05.649Z

If it’s worth saying, but not worth its own post, here's a place to put it. (You can also make a shortform post)

And, if you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are welcome.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ.

The Open Thread sequence is here.
Toby Ord’s ‘The Precipice’ is published!
by matthew.vandermerwe — 2020-03-08T21:20:20.653Z

[x-posted from EA Forum]

The Precipice: Existential Risk and the Future of Humanity is out today. I’ve been working on the book with Toby for the past 18 months, and I’m excited for everyone to read it. I think it has the potential to make a profound difference to the way the world thinks about existential risk.

How to get it

It's out in the UK on March 5 and US March 24
An audiobook, narrated by Toby himself, is out March 24
You can buy it on Amazon now, or at theprecipice.com/purchase
You can download the opening chapters for free by signing up to the newsletter at www.theprecipice.com

What you can do

Read the book
Talk about it with your friends and family, or share quotes you like on social media
If you enjoy it, consider writing a review on Amazon or Goodreads

Summary of the book

Part One: The Stakes

Toby places our time within the broad sweep of human history: showing how far humanity has come in 2,000 centuries, and where we might go if we survive long enough. He outlines the major transitions in our past—the Agricultural, Scientific, and Industrial Revolutions. Each is characterised by dramatic increases in our power over the natural world, and together they have yielded massive improvements in living standards. During the twentieth century, with the detonation of the atomic bomb, humanity entered a new era. We gained the power to destroy itself, without the wisdom to ensure that we don’t. This is the Precipice, and how we navigate this period will determine whether humanity has a long and flourishing future, or no future at all. Toby introduces the concept of existential risk—risks that threaten to destroy humanity’s longterm potential. He shows how the case for safeguarding humanity from these risks draws support from a range of moral perspectives. Yet it remains grossly neglected—humanity spends more each year on ice cream than we do on protecting our future.

Part Two: The Risks

Toby explores the science behind the risks we face. In Natural Risks, he considers threats from asteroids & comets, supervolcanic eruptions, and stellar explosions. He shows how we can use humanity’s 200,000 year history to place strict bounds on how high the natural risk could be. In Anthropogenic Risks, he looks at risks we have imposed on ourselves in the last century, from nuclear war, extreme climate change, and ...
UML final
by Rafael Harth — 2020-03-08T20:43:58.897Z

(This is the fourteenth and final post in a sequence on Machine Learning based on this book. Click here for part I.)

The first part of this post will be devoted to multiclass prediction, which is the last topic I'll cover in the usual style. (It's on the technical side; requires familiarity with a bunch of concepts covered in earlier posts.) Then follows a less detailed treatment of the remaining material and, finally, some musings on the textbook.

Multiclass Prediction

For this chapter, we're back in the realm of supervised learning.

Previously in this sequence, we've looked at binary classification (where |Y|=2) and regression (where Y=R). In multiclass prediction or multiclass categorization, we have |Y|∈N but |Y|>2. For simplicity, we take the next simplest case, i.e., |Y|=3. (Going up from there is straight-forward.) More specifically, consider the problem of recognizing dogs and cats in pictures, which we can model by setting Y={dog,cat,noanimal}. For the purposes of this chapter, we ignore the difficult question of how to represent the instance space X.

We take the following approach:

train a function to compute a score for each label that indicates how well the instance fits that label
classify each new domain point as the best-fitting label

In our case, this means we are interested in learning a function of the form f:X×Y→R. Then, if we have

f(x,cat)=2f(x,dog)=√5f(x,no_animal)=−1

this translates into something like "x kinda looks like a dog, but a little bit more like a cat, and it does seem to be some animal." Each such function determines a predictor hf:X→Y via the rule hf(x)=argmaxy∈Yf(x,y).

We will restrict ourselves to linear predictors. I.e., we assume that the feature space X is represented as something like Rd and our predictors are based on hyperplanes.

With the setting established, the question is how to learn f.

Approach 1: Reduction to Binary Classification

Suppose (unrealistically) that X=R2 and our training data looks like this:

where

We could now train a separate scoring function for each label. Starting with cats, we would like a function fcat:X→R that assigns each point a score based on how much it looks like a cat. For fcat, the label cat corresponds to the label 1, whereas dog and no_animal both correspond to the label 0. In this setting, learning fcat corresp...
The Lens, Progerias and Polycausality
by johnswentworth — 2020-03-08T17:53:30.924Z

Fun fact: the lens of a human eye consists mostly of fiber deposits which are never broken down - they do not turn over. Furthermore, new fiber layers are constantly added throughout life, so the lens thickens linearly by about 25 microns per year. Starting at around 3.5mm in infancy, it reaches 5.5mm in old age.

The main clinical result of this is the practically-universal need for glasses for close-up vision in people over 55 years old.

(Source:

Physiological Basis of Aging and Geriatrics

; the section on the eye is one of the most detailed in the book.)

Besides being a simple, self-contained gear in its own right, the growth of the lens is a clear, knock-down example of an independent

root cause

of one symptom of aging. We know exactly what’s accumulating in a nonequilibrium fashion: the fibers of the lens. It’s wildly unlikely that the growth of the lens is a root cause for other symptoms of aging - like

wrinkles

, atherosclerosis, Alzheimer’s, cancer, muscle degeneration, etc. So, we have a clear case for polycausality - at least for one symptom of aging.

That said, there’s a fair bit of evidence that most symptoms of aging share a common root cause, or at least a common intermediate. Qualitatively, many/most symptoms of aging in a wide variety of tissues:

Look similar at the cellular level - there’s a loss of homeostasis, with cells dying off faster than they’re replaced, high levels of misfolded protein aggregates (a.k.a. junk), and markers of chronic inflammation
Follow a similar population-level onset/progression timetable: no noticeable problems from youth through mid-twenties, gradual onset/progression throughout middle age, then rapidly accelerating breakdown around 50-60 years of age and older. Some examples:

cancer incidence

,

muscle loss

,

atherosclerosis

. Google a performance metric which declines with age, and you’ll probably see the pattern.
Are correlated - someone who has one problem early is likely to have others early, and vice versa. See the literature on physiological/biological aging clocks for details.

The growth of the lens does not follow this pattern - it’s just a straight-line linear growth starting from childhood, without any unusual role of chronic inflammation or misfolded proteins or other typical aging-associated characteristics. On the other hand, there are other contributing fac...
Help me with bayesian thinking re:coronavirus?
by hereisonehand — 2020-03-08T17:03:23.320Z

If I have a fever, what is the likelihood that I have a coronavirus infection? I'm actually less interested in the answer than in the underlying thought process, so feel free to insert variables where reliable numbers are hard to find.

I'm just having trouble thinking clearly about this. Do I start with the prevalence of coronavirus and adjust that base rate by saying if I have a fever, the probability goes up because 99% of coronavirus cases have a fever? I guess I could also start with the % of fevers that are due to respiratory conditions as my base rate. And then compare the proportion of respiratory-related fevers from coronavirus to other causes?

I'm having trouble because I realize that lots of common non-coronavirus conditions cause a fever, and that if I have a fever, the probability I have coronavirus has increased, but I can't figure out how to clearly convert the relevant information into a series of mathematical statements. This feels like a very practical exercise of bayesian thinking, so I would love to see how people who are more fluent than I am with this kind of reasoning would approach this problem.

Thanks!
Analyticity Depends On Definitions
by Chris_Leong — 2020-03-08T14:00:34.492Z

While reading Quine's Two Dogmas of Empiricism I was struck by the importance of analyticity. In philosophy, there are many arguments that are deeply challenging to evaluate. Take for example this argument from Kant for finitism:

If we assume that the world has no beginning in time, then up to every given moment an eternity has elapsed, and there has passed away in that world an infinite series of successive states of things. Now the infinity of a series consists in the fact that it can never be completed through successive synthesis. It thus follows that it is impossible for an infinite world-series to have passed away, and that a beginning of the world is therefore a necessary condition of the world's existence.

Is this a good argument? Like many abstract philosophical arguments, it's actually very difficult to tell. But if we had a method for determining which statements are analytic and which are synthetic, we'd have a better idea of whether to consider it seriously or to dismiss it out of hand.

I won't be providing a method of distinguishing analytic from synthetic today. I don't know how to do that. However, I believe I have one puzzle piece.

Quine states that is it unclear whether statements like, "Anything green is extended" are analytic even though he know perfectly well what "green" and "extended" mean.

I would actually dispute this - the definitions of either of these words aren't clear at all. Take for example green - are we talking about what color we perceive or a wavelength that is emitted? What if we discovered tomorrow that vision didn't actually utilise light, but tiny particles? Would green be defined in terms of then? What about a green hallucination? Does that count as a "thing" that is green?

And what counts as extended? If we discovered we were characters in the matrix, would we count the virtual space we move around as extended? Would the real space count as extended? Arguably, we could say that space refers to an underlying structure that plays a particular role in our experiences and that since we are experiencing the virtual and not the "real" world when we talk about "extension" we mean extension in this virtual world.

Another way to think about this is in terms of import statements. Like in order to define green we may have to import the concept of light which then requires us to import the concepts of time and space.

Of course, these...
How's the case for wearing googles for COVID-19 protection when in public transportation?
by ChristianKl — 2020-03-08T09:31:25.200Z

I'm in a high risk group and there's a higher likelihood of dying if I get infected then for the average person. I will reduce the amount of public transportation travel but being totally isolated won't be good for my health either.

I have a decent supply of surgical masks. How much percentage of the infection risk is do to having exposed eyes? Does it make sense to protect them with swimming googles?