trentmkelly/LessWrong-43k huggingface dataset:
Coronavirus: California case growth
by VipulNaik — 2020-03-29T16:14:19.459Z

In this post, I try to understand the case growth rate for coronavirus
cases in California, and try to address questions such as:

How long will the case count continue to grow?

At what level will the case count stabilize?

To what extent will we be able to infer from the data whether level
2 restrictions were sufficient, or level 3 restrictions were
necessary, to stop or significantly slow down case growth? The
"level 2" and "level 3" jargon are from my previous
post.

Skip to answers and lessons for my (incomplete
and tentative) answers.

NOTE: My original post was based on data till 2020-03-27 (row 17 in
the
spreadsheet). On
2020-04-01 (April 1, 2020) I made edits to this post of two kinds:

Language improvements, based on feedback in the comments, without
any substantive changes to the model.

Addenda at the end of some sections giving updates based on data
seen since I originally published the post. I used data till
2020-03-31 (row 21).

A simple model from true currently-or-eventually-symptomatic cases to confirmed cases to deaths or recoveries

The model

For simplicity, I will use the shorthand "true
currently-or-eventually-symptomatic cases" only for cases where a
person is already infected and will eventually become symptomatic
(so this will include both currently symptomatic cases and cases that
are presymptomatic, i.e., will become symptomatic later). I expect
that most asymptomatic cases (i.e., cases that never become
symptomatic) won't get diagnosed, and therefore won't count in the
number of confirmed cases either, so this seems a reasonable
approximation for the model I will present below. However, if
incorrect, this could cause estimates to be off by a factor of two or
more, depending on the fraction of cases that are asymptomatic.

The simplistic model identifies the following flow:

Get infected

Start showing symptoms

Get a test

Get test results

Recover or die

Technically, 5 can happen before 3 or 4; the logical dependencies are
1 -> 2 -> 5 and 1 -> 2 -> 3 -> 4. It's also possible (and probably
more likely) that 5 happens after 3 but before 4.

To keep this post focused, I will not discuss 5 here, though it's
obviously very important.

Time lags in the model (1 -> 2 -> 3 -> 4)

The total time lag from 1 to 4 shows up as the lag between any trend
change in the number of true currently-or-eventually-symptomatic cases, and the
co

...
Chris Masterjohn on Coronavirus, Part 1
by philh — 2020-03-29T11:00:00.819Z

Update (April 28th 2020): Part 2 published. I've also made at least one minor update to the original text that hasn't been reflected here, because keeping them in sync is a minor hassle.

Update (April 5th 2020): Chris released version two of his recommendations on April 2nd. There are some fairly major revisions. In particular, Chris now thinks Coronavirus infects the throat, not just the lungs. He was already making recommendations just in case this turned out to be true, so he hasn't specifically added anything, but it's still an important update. The other thing of note is that he no longer recommends using copper spray or taking echinacea until you get sick, except (for the copper) before and after potential exposure. I've updated this post accordingly. You can still read version 1.

Chris Masterjohn is a nutritionist who has some advice on supplements to take to help protect against Covid-19, and some to avoid. The raw advice is available for free, but the full report with explanation and references costs $10. I bought a copy. (The public advice has not been updated to reflect version 2.)

Should we trust him? On the one hand, "nutritionist" is not a profession I necessarily hold much respect for (it's not a protected term). Nor do I tend to think highly of people emphasizing that they have a PhD. Also, his website looks super sketchy to my eyes. Also also, something that costs money gets fewer eyeballs than something free, and so mistakes are less likely to be discovered.

(Only the last one of those should be considered a problem with the report; the rest are just priors.)

On the other hand, Chris previously turned me on to zinc so he has some credibility with me. Also, although I'm out of my depth in actually evaluating the science, I do think I have a decent bullshit detector, and the report is superficially very convincing to me.1 I get the sense that he actually really knows what he's doing, is weighing the evidence appropriately and making claims that are justified by it. He admits to uncertainty, and some of his recommendations are "this probably won't help, but just in case"; but he does speak precisely enough to be falsifiable. This doesn't mean he's right, of course. But I think he's worth some people paying some attention to.

My intention here is mostly to summarize his reasoning, in more detail than is on the linked page but less detail than ...
How to study statistical/computer modelling of the current pandemic and its outcomes?
by  — 2020-03-29T08:40:17.244Z

The purpose of this question is NOT to help with the ongoing real research.

Rather, I want to use the excitement of living in a pandemic to learn new stuff, mostly statistics and computer modelling. I noticed that I'm wasting a lot of energy reading clickbait articles and I want to redirect that energy towards something more productive in the long term. (Side-note: generally my self-improvement meta-strategy is redirecting emotional impulses with minimal willpower, rather than imposing self-discipline which in my experience depletes more willpower and is less enjoyable.)

I'm interested not only in health outcomes but also/mostly economic ones.

Resources I have identified so far:

http://jvalue.co.uk/papers/J-value-assessment-of-combating-Covid-19-Thomas-23.3.2020.pdf (the draft from U of Bristol that recently made the news since it provides an estimate of a cutoff cost above which harm to the economy will cause more deaths than Covid-19). I'm also going to follow up on select bibliography, although without much excitement because the most promising citations seem to be to earlier papers by the same team.
https://gabgoh.github.io/COVID/index.html - interactive visualisation of the commonly used epidemic model with tweakable parameters and a brief writeup explaining the model
https://www.stlouisfed.org/~/media/files/pdfs/community-development/research-reports/pandemic_flu_report.pdf "Economic Effects of the 1918 Influenza Pandemic" This article was recommended to me but I haven't even skimmed through it yet.

For the sake of other readers who might also be interested, any related resources and advice will be very welcome.

My own background is in C/C++ programming with some Python experience and nearly all of statistics knowledge forgotten. I would prefer my study to focus more on maths, statistics, etc. since it should yield greater proportional gain in knowledge for time invested (since my starting point in computer-related fields is much higher and so my learning curve will be flatter), and also because I'm working as a full-time programmer so I prefer to do other things in my free time.
[Update: New URL] Today's Online Meetup: We're Using Mozilla Hubs
by Ben Pace — 2020-03-29T04:00:18.228Z

That's right! Tomorrow/Today (Sunday Mar 29th) we're having an online LessWrong meetup in Mozilla Hubs. You're welcome to join
and chat with some of the interesting people who read LessWrong, SSC, Overcoming Bias, and so on. I am personally quite excited to talk with a bunch of you. Make sure you read the rules for joining the hub.

At 12 noon PDT we'll be having the Hanson/Mowshowitz "Expose The Youth" Debate, then after that at around 2pm we'll having a post-debate meetup. This an experiment, I'll do it more if it's fun.

Basic controls:

WASD to move
G to toggle flying
Click and Drag to look around
You can hear and be heard by people in proportion to how close you are to them

The rules:

Only 25 people can be in a room. Move around and check out the other rooms!
Login as soon as you arrive and use either your real name or a standard alias like your LessWrong username. I'd like some basic accountability please, and today I will remove people who don't follow this rule.
Use headphones. Mozilla Hubs does not have any basic feedback reduction tech, all sound will get unenjoyably recursive quite quickly unless everyone uses headphones.
Being weird is positively fine and I don't think you have a limited budget to spend, you can just be super weird. But if you're too loud and if people are having a bad time because of your presence, a moderator will remove you from the rooms.

I've made a few basic rooms. If you've not visited before or need some help, enter the tutorial room. Here's the first:

Please leave the tutorial room once you've solved your problems, to make space for others.

And here are the hangout rooms.

Taking Joy in the Merely Real
Many Worlds Interpretation
In Favour of Niceness, Community, and Civilization

Extra rooms in case you want them for whatever reason:

Don't Touch the Floor

Slack and the Sabbath

Beyond the Reach of God

For anyone who has trouble with the Mozilla Hubs setup, we also have a backup Discord server that you can join here.
"No evidence" as a Valley of Bad Rationality
by Adam Zerner — 2020-03-28T23:45:44.927Z

Quick summary of Doctor, There are Two Kinds of “No Evidence”:

Author has a relative with cancer. Relative is doing well after chemo and is going to a doctor to see if it's worth getting more chemo to kill the little extra bits of cancer that might be lingering.
Doctor says that there is no evidence that getting more chemo does any good in these situations.
Author says that this violates common sense.
Doctor says that common sense doesn't matter, evidence does.
Author asks whether "no evidence" means 1) a lot of studies showing that it doesn't do any good, or 2) not enough studies to conclusively say that it does good.
Doctor didn't understand the difference.

Let me be clear about the mistake the doctor is making: he's focused on conclusive evidence. To him, if the evidence isn't conclusive, it doesn't count.

I think this doctor is stuck in a Valley of Bad Rationality. Here's what I mean:

The average Joe doesn't know anything about t-tests and p-values, but the average Joe does know to update his beliefs incrementally. Lamar Jackson just had another 4 touchdown game? It's not conclusive, but it starts to point more in the direction of him winning the MVP.
The average Joe doesn't know anything about formal statistical methods. He updates his beliefs in a hand-wavy, wishy-washy way.
The doctor went to school to learn about these formal statistical methods. He learned that theorizing is error prone and that we need to base our beliefs on hard data. And he learned that if our p-value isn't less than 0.05, we can't reject the null hypothesis.
You can argue that so far, the doctors education didn't move him forward. That it instead caused him to take a step backwards. Think about it: he's telling a patient with cancer to not even consider more chemo because there is "no evidence" that it will do "any" good. I think Average Joe could do better than that.
But if the doctor continued his education and learned more about statistics, he'd learn that his intro class didn't paint a complete picture. He'd learn that you don't always have access to "conclusive" evidence, and that in these situations, sometimes you just have to work with what you have. He'd also learn that he was privileging the null hypothesis in a situation where it&

...
Rob Bensinger's COVID-19 overview
by habryka — 2020-03-28T21:47:31.684Z
Link: https://docs.google.com/document/d/10MFFoUMYHqGB3cLCxuqhsM2kahtOQVybPhLG-YNZdaI/

Robby posted this to Facebook on March 15th, and just updated section 2 and 3 with new information, making this I think currently one of the best guides to how to respond to this whole situation.

(Added Jun. 4: List of changes to this document.)

(Added Nov. 9: This document evolved from some recommendations I put together for friends, colleagues, and family on Feb. 27, when much less was known about COVID-19. At the time, it made sense to prepare for the worst and emphasize quick, dramatic action.

Over time, this document evolved into more conditional advice: rather than "lock down immediately," I assumed that different individuals would have different risk tolerances, and I try to give advice for how to mitigate risk without building in the assumption that everyone should be maximally cautious. Still, it's worth emphasizing these points explicitly:

Pay attention to what's going on around you (e.g., COVID-19 rates in your area) and pick the risk level that makes sense for you, given your circumstances, your preferences, and the preferences of people you might expose.
Being over-cautious has real costs, just as being under-cautious does.

For more COVID-19 information and recommendations, try microcovid.org or Zvi Mowshowitz's updates.)

If you live in the US, I recommend that you self-quarantine immediately (to the extent that's possible for you) to avoid exposure to COVID-19, the new coronavirus disease. I'll explain why below, then give tips on how to reduce exposure and what to do if you get sick.

Quarantine isn't all-or-nothing, and every little bit helps. Even if you expect to catch COVID-19, you're likely to get sicker if you're exposed to more viral load early on.

(Paul Bohm says "pretty much any viral/bacterial dose study shows that result". Divia Eden: "As I understand it, the virus replicating is an exponential process, and antibody production is an exponential process too. So an early difference in load should make it easier for the latter to outpace the former.")

So even if you can't (e.g.) work from home right now, I would still recommend taking large measures to reduce your exposure.

Disclaimer: I put this document together in my free time to collect tips and background information I’d seen on COVID-19 from a variety of sources. I am not a doctor or an epidemiologist. In writing this, I prioritized breadth, clearness/useability, and getting info...
Coronavirus Virology: A Beginner’s Guide
by brook — 2020-03-28T20:47:52.442Z

Introduction

This is aimed at those interested in a biological understanding of the basic features of a coronavirus, but who do not have a biological/chemical background to speak of—if you haven’t done biology or chemistry since high school, this should be for you. It starts at square one and it oversimplifies many things. That said, I hope it is at least clear, and allows you to read scientific papers on the features of SARS-CoV-2 (novel coronavirus) without getting too much of a headache.

Viruses mostly just use their host’s (that’s you) metabolism to make more viruses. To understand how that occurs, we will begin with some basic (host) cell physiology—how DNA, RNA, and proteins relate to one another, and what a lipid bilayer is—before moving on to look at the anatomy of a coronavirus, and then finally a handful of specific features that make SARS-CoV-2 so dangerous.

DISCLAIMER: There should be nothing controversial or disputed here, and I have specifically avoided giving information that is contested. This is not least because I am a medical student, not a virologist or epidemiologist. The aim of this guide is just to introduce the basic science, nothing more.

Many thanks to Jaden Kimura for comments and proof-reading.

How does a cell work, anyway?

In your own cells, DNA codes for all of the proteins you produce, which determines how you build cells and, eventually, a body. Think of it as the source code. Short stretches of it are ‘compiled’ (transcribed) onto RNA, which is a very similar but less stable molecule. Only RNA can be actually ‘run’ by translating it into functional proteins.

DNA is double-stranded, but the two strands are not the same—they’re kind of chemical mirror images of one another. One is ‘positive sense’ and the other ‘negative sense’. Because of the way that they mirror one another, they bind tightly together. When new RNA is made, it is ‘copied’, through a similar binding, from the negative sense strand—it looks just like the positive sense strand. This means that your protein-making machinery (ribosomes) is all used to reading positive sense RNA.

The outside ‘wall’ of a cell is made of a lipid bilayer. The bilayer is made up of molecules with a small polar ‘head’ and a long n...
Programming: Cascading Failure chains
by Donald Hobson — 2020-03-28T19:22:50.067Z

The concept of a cascading failure chain can be demonstrated by this block of python. Don't worry if you don't know python, I'll try to explain it.

xCor=((12+7)//2,(5-2)*((8-3)//2),(((3+4)*((7+4)*2))%(8*(5-3)), (5*(3+2)//(2*(2+3)))),(2*(8-1))) yCor=(23,25,26,28,30) print(xCor,yCor) Lst={"A":0,"B":1,"C":2,"D":3,"E":4} inp=input("Pick from A to E") if inp not in Lst: print("Error") inp="A" val=Lst[inp] sze=xCor[val]*yCor[val] print(sze) import numpy as np np.random.normal(size=sze)

The error sneaks in on the first line.

xCor=(9, 6, (10, 2), 14)

This is hidden by the brackets being used for tuples and precedence.

The next part of the error occurs at

sze=xCor[val]*yCor[val]

In python, the product of an integer and a tuple is a tuple. >>> 3*4 12 >>> 3*(1,2) (1, 2, 1, 2, 1, 2)

This is far from the only case where python is slack with typing. You can multiply a Boolean by a string or add two lists or take a string modulo a tuple.

The final error comes with the numpy.random.normal function. It takes in a size. if that size is an integer, it makes a 1D array of that length, if it is a tuple, it creates a multidimensional array, with dimensions from the tuple. So if you enter "C", this piece of code produces a 52D array containing 2026=6710886400000000000000000000000000 random numbers. This causes your system to run out of memory and crash.

This is the structure of a cascading failure chain. Something goes wrong somewhere, but it isn't caught by the error correction mechanism and dealt with, instead it triggers something else to go wrong, and then another thing. Here going wrong means going outside the context that the programmer was thinking of when they wrote the code.

The basic problem was that the programming language was dynamically typed, so if you get passed a variable foo from another part of the program, it could be an Int or String or List or anything. The next problem comes from giving several different functions the same name. Multiplying integers and repeating lists are entirely different functions. If a programer relies on the fact...
Iceland's COVID-19 random sampling results: C19 similar to Influenza
by jacob_cannell — 2020-03-28T18:26:00.903Z

Update 11/09/2021: Over a year later, Iceland now reports 34 total deaths / 14757 total infections for a naive IFR of 0.23%, or a least 50% higher than the mean of this old simple estimate, and roughly 2x seasonal flu.

EDIT: updated with wider estimates to reflect potential sample bias

deCODE Genetics has tested a random-ish sample of about ~5K Icelanders for COVID-19 and about 0.9% tested positive, which naively indicates ~3K true infections in a population of ~330K.

However, if we consider that the volunteer based sample is perhaps biased (the article suggests they would control for this but doesn't describe if/how), that suggests an estimate closer to the ~1k confirmed cases - say 2K.

On the other hand, if we consider the potential of false negatives from failed tests for those who already recovered, that suggests a higher range, perhaps up to ~5k. (The PCR test is highly specific, so we can ignore false positives)

Iceland has only 2 deaths so far for a naive IFR in the range of 0.04% to 0.2% to (we can probably ignore false negatives for deaths - as they are harder to miss in Iceland). Iceland's cumulative case count is clearly in a linear growth regime (past midpoint of sigmoid). They have 6 patients in ICU (Iceland data), which has about a 30% fatality rate, and 19 in hospital with a 10% fatality rate so we can estimate the future total death count in the 2 to 8 range.

This results in a mean predicted IFR of 0.17% (6/3500)and a range of 0.04% to 0.4% (2/5k to 8/2k), similar to influenza but potentially a bit (2x) higher. The uncertainty range will eventually tighten as we know more about survival in their current hospitalizations.

This agrees with the Diamond Princess data which rules out IFR much higher than influenza. (see my analysis here, or a more detailed analysis here) In that same post I also arrived at a similar conclusion by directly estimating under-reporting (the infection/case ratio) by comparing the age structure of confirmed cases to the age structure of the population and assuming uniform or slightly age-dependent attack rates similar to other viruses. That model predicts under-reporting of ~20X or more in the US, so it's not surprising that the under-reporting in Iceland is still in the ~4X range.

The infection hospitalization rate of COVID-19 in Iceland is also in the vicinity of ~1%, similar to influenza.

This also puts bounds on how wides...
Will grocery stores thwart social distancing, and when should I eat my food stockpile? 
by Patrick Long — 2020-03-28T18:20:21.241Z

I live in Boston. From what I am hearing, grocery stores are mobbed, day after day, and don't seem to be taking social distancing seriously. Is this a serious enough problem to keep R0 above 1, and prevent successful suppression?

I have about a 4-week supply of food but I want to save it for the worst 4 weeks of the pandemic. I will take my chances with delivery/the local convenience store until then, both of which involve lower exposure risk than the grocery store by 1-2 orders of magnitude. According to some projections, the worst 4 weeks are about to start. But that seems extremely optimistic to me. My naive expectation was that without social distancing the peak would be in June. Distancing should reduce how bad the peak is, but also extend it, assuming distancing is not sufficient to lower R0 below 1.

I can extend the life of the stockpile if Amazon Fresh ever becomes operational again. This involves non-zero risk, but one delivery a month from them is much less risky than daily uber eats/convenience store runs.

In an extended-peak scenario it doesn't really matter when I eat the stockpile because I am going to have to get at least some deliveries during the peak. But in a high and short peak scenario, both my ability to avoid all deliveries and the desirability of doing so are a lot higher. In a low and short peak (i.e. successful suppression), the desirability of avoiding all deliveries is less, but I maintain the ability to do so and I might as well, if I can predict it correctly.