Zian

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Coronavirus: Justified Practical Advice Thread
Answer by ZianOct 31, 20211

Get fully vaccinated, preferably using the Moderna vaccine.

https://www.microcovid.org/paper/14-research-sources#moderna--pfizer says "[the] CDC released a study showing 90% reduction in all cases (symptomatic + asymptomatic) 14+ days after participants received Moderna or Pfizer’s vaccine".

It makes sense for anyone competent enough to give consent for the vaccination unless you are known to have an allergic reaction to a component of all available vaccines.

My experience at and around MIRI and CFAR (inspired by Zoe Curzi's writeup of experiences at Leverage)

Unfortunately, by participating in this community (LW/etc.), we've disqualified ourselves from asking Scott to be our doctor (should I call him "Dr. Alexander" when talking about him-as-a-medical-professional while using his alias when he's not in a clinical environment?).

I concur with your comment about having trouble finding a good doctor for people like us. p(find a good doctor) is already low and difficult given the small n (also known as the doctor shortage). If you combine p(doctor works well with people like us), the result may rapidly approach epsilon.

It seems that the best advice is to make n bigger by seeking care in a place with a large per capita population of the doctors you need. For example, by combining https://nccd.cdc.gov/CKD/detail.aspx?Qnum=Q600 with the US Census ACS 2013 population estimates (https://data.census.gov/cedsci/table?t=Counts,%20Estimates,%20and%20Projections%3APopulation%20Total&g=0100000US%240400000&y=2013&tid=ACSDT1Y2013.B01003&hidePreview=true&tp=true), we see that the following states had >=0.9 primary care doctors per 1,000 people:

  • District of Columbia (1.4)
  • Vermont (1.1)
  • Massachusetts (1.0)
  • Maryland (0.9)
  • Minnesota (0.9)
  • Rhode Island (0.9)
  • New York (0.9)
  • Connecticut (0.9)
Open & Welcome Thread October 2021

(If this were a standalone post, the tag would likely be "life optimization.")

In the past few months, I've been updating my dental habits to match the evidence that's accumulated that shows that caries can generally be handled without a restoration, also known as "drill and fill." There appears to be increasing support (possibly institutional and cultural, not necessarily scientific) for using fluoride in all its manifestations, doing boring stuff like flossing and brushing teeth with toothpaste really well, and paying more attention to patients' teeth and lives.

Today, it all paid off when the dentist & hygienist said that all was well despite my past experiences with the likelihood of the dentist finding something wrong.

I'm certainly not qualified to give anyone specific advice on this topic. If you trust the American Dental Association, see https://ebd.ada.org/en/evidence/guidelines. If you'd rather trust King's College (London, UK) and some other dental schools around the world or if you'd prefer to get closer to the primary sources, the website for the International Caries Classification and Management System (https://www.iccms-web.com/) is good.

That Alien Message

History says that when United Fruit wants you dead, you will die. (see Latin America)

Covid 8/26: Full Vaccine Approval

Do you mean that if I give you a perpetual $20 coupon to buy bottles of water and then increase the price by $20 you have lost something?

Alarm bell for the next pandemic, V.2

You've inspired me to think about what goes into the mental model of someone who does this professionally such as an epidemiologist or https://firstwatch.net/category/health-intelligence/outbreaks/, which a coworker maintains (conflict of interest alert).

I suspect that the problem isn't a lack of data as much as it is a problem of remaining constantly vigilant (allusion intended) and being willing to pay the price of false positives. For example, if H7N9 scored highly initially, would you be OK with your family thinking that you are a fool for selling your retirement funds? This is getting into the topic of "Shut up and multiply."

What 2026 looks like

2022 The United States continues to build roads and struggle to increase the quality of public transit except for rare exceptions. New York continues to struggle to implement congestion pricing.

Regional bus route redesigns continue to make major concessions to coverage compared to frequency.

2023 The United States continues to pay excessively (compared to other nations) large amounts of money for public transit projects. People continue to vote against building new dense housing in desirable locations.

People continue voting for roads. Electric and hydrogen busses continue to build a reputation for breaking down and running out of power, especially in hilly or cold regions.

The MTA makes noises about how this would be a really good time for some congestion charges to appear as income. Revenue, if any, begins to dribble in from those unlucky enough to be unable to get a "temporary" exemption.

Other agencies try to pass bond measures to dig out of the financial hole caused by (supposedly) the coronavirus. The vast majority are rejected by voters.

2024 My crystal ball fails at this point. I'm not sure how the chatbots will affect people's preferences around land use or personal property.

What are some triggers that prompt you to do a Fermi estimate, or to pull up a spreadsheet and make a simple/rough quantitative model?
Answer by ZianJul 25, 20213

I last made a spreadsheet because I received a medical bill and wanted to calculate the correct amount and estimate what the insurance company should pay.

Open and Welcome Thread – June 2021

Potentially trivial math question:

Imagine that you have a computer with the following properties:

  • flat
  • black
  • hot
  • speck of red on one side

And your prior at this point for p(slow given it's a computer) = 0.5.

If an article about computers said p(slow given flat, black, and speck of red) = 0.25, then would you use the new # as your prior or combine the two pieces of information to calculate p(slow given flat, black, hot, and speck of red)?

I'm inclined to say that I should use .25 as the new prior and forget about 0.5 but I may also be making a silly logical error here.

Power Outage Chances

You could look for something that can provide a more relevant reference set for your situation. For example, you point out that power outages have different probability distributions based on your location in time and space. 

You could also look at others' revealed preferences.

My direct answer your question of estimating an outage for the average person in the US over the next 10 years is that the question is not very useful.

I'll flesh this comment out with a specific example to show what I meant with the first two paragraphs as I have time to do so. By publishing the comment early, I intend to provide some quick value and prevent computer bugs from eating the post.

Goal:

p(>3d outage for a residential location in the next 10 years in downtown San Diego, CA)

Thought Process:

Sources of power outage information:

Potential Primary Sources:

  • Angry articles and social media posts about extended power outages in the last 10 years
  • Other studies of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
  • Raw counts of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
  • Reports of telecom outages
  • Reports of datacenter or server outages

Likely Locations of the Information:

  • Local newspapers (searchable via databases)
  • Reddit.com (searchable via Reddit and Google)
  • Patch.com (Google and site-search)
  • Local online-only newspapers (searchable via Google and site-search)
  • San Diego Gas & Electric
  • California Public Utilities Commission
  • California Air Resource Board
  • Just ask the data source directly

Revealed Preferences:

  • Type of power redundancy in use by self-interested entities that want to always have power
    • City of San Diego
    • County of San Diego
    • Local public safety agencies
    • Local datacenters
    • Telecommunications companies
    • Hospitals
    • Transit agencies
    • Jails
  • Sources of information about the revealed preferences:
    • Satellite imagery
    • RFPs, purchase orders, and financial statements
    • Just ask them

From the CPUC:

SDG&E had 68.64 SAIDI minutes in 2019 (data is available going back to 1997) excluding MED. There were 122.96 SAIDI minutes including MED.

Assuming that ">3 day outage" = a MED, then we can do a little algebra.

Given:

SAIDI = Total minutes every customer was without power due to sustained outages / total number of customers

SAIDI without MED = Total minutes every customer was without power due to sustained outages - total MED minutes / total number of customers

68.64 = (Min - Med) / C

122.96 = Min/C

MED minutes = 1358 * # of customers / 25

Since we want to know the p for a given customer, then we further calculate:

MED minute / customer in a year = 1358/25 * # of customers / # of customers

= 54.32 minutes

Therefore:

p = 1.033E-4 that any particular minute will be without power during a major power outage in San Diego, CA

Over the span of 10 years, you'd expect to sit through just over 9 hours of lonely extended darkness.

From a sewage treatment plant RFP:

The "[fuel] tank shall hold 3 days of diesel fuel storage at 70% generators load" for the "two Owner Supplied Trailer Mounted 2MW Caterpillar XQ2000 backup generators".

If we could find the power consumption of the Penasquitos Sewer Pump Station, then we could calculate (3 * amount of fuel consumed at 70% load of a 2MW Caterpillar XQ2000 generator * 2) / (daily power consumption) to find the # of expected consecutive blackout days.

Let's pretend that # is "3" and simplistically assume that it corresponds to 3 days of doom per year. We'll also multiply by 2 as an extra safety factor because the City can probably get fuel easier than normal people.

p = 3 days / 365 days  * 2

= 0.016

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