This paper from engineers at Cambridge University claims that a standard aroma diffuser and plastic bag is close to the performance of commercial equipment. That said, I'm not sure how much the total cost and prep time would compare to the nebulizer approach that jimrandomh suggests.
Qualitative fit testing is a popular method of ensuring the fit of sealing face masks such as N95 and FFP3 masks. Increased demand due to the coronavirus disease 2019 (COVID-19) pandemic has led to shortages in testing equipment and has forced many institutions to abandon fit testing. Three key materials are required for qualitative fit testing: the test solution, nebulizer, and testing hood. Accessible alternatives to the testing solution have been studied. This exploratory qualitative study evaluates alternatives to the nebulizer and hoods for performing qualitative fit testing.
Four devices were trialed to replace the test kit nebulizer. Two enclosures were tested for their ability to replace the test hood. Three researchers evaluated promising replacements under multiple mask fit conditions to assess functionality and accuracy.
The aroma diffuser and smaller enclosures allowed participants to perform qualitative fit tests quickly and with high accuracy.
Aroma diffusers show significant promise in their ability to allow individuals to quickly, easily, and inexpensively perform qualitative fit testing. Our findings indicate that aroma diffusers and homemade testing hoods may allow for qualitative fit testing when conventional apparatus is unavailable. Additional research is needed to evaluate the safety and reliability of these devices.
Clintons? Obamas? There are many examples from academia. Nobel Laureates Banerjee and Duflo, or these two economists:
During the pregnancy, they employed a doula, or birth companion; after the birth, they hired a nanny named Ellen, who had a BA and was finishing her master's degree in education policy, and whom they paid $US50,000 (about $65,000) a year. "We didn't just want a warm body," Wolfers says, over his second beer. "Some people just want someone who'll keep their kids safe, but we wanted more than that."
I haven't re-read the paper, although IIRC there are critiques online of this paper and the author's other statistical analyses. How strong do you think the evidence is for the counterfactual "If person has chooses to have kids, their chance of major achievement will drop substantially" (for a range of different people)? Ideally there'd be natural experiments (due to infertility or someone who didn't want kids raising their sibling's children etc).
These graphs aren't that different and (I'd guess) it wouldn't be hard to p-hack to get the intended result. Rate of being unmarried will vary over time and with country and this will correlate with age of achievements (e.g. if people in biology peak later than math/physics, if there's more biologists in UK and math/physics people in Germany and Italy). And there's the causal / counterfactual inference..
I'd like to see discussion of data rather than mostly a priori argument ("I have a sense" ... "I suspect desire"). For aggregate data, there's SSC survey and there are studies of "ambitious" groups (e.g. the Harvard Men study, Benbow on precious math talent). There are also anecdata of the exceptionally ambitious. E.g. Musk had first child age ~30 and has many kids, Hassabis had first child aged ~29. It seems Jaan Tallinn had kids starting in his 20s before founding Skype (Wikipedia). Bezos has 4 kids (started age 37). Gates has 3 kids (started age ~40). Turing award-winners David Patterson and Judea Pearl had kids in their 20s before their biggest contributions. Yoshua Bengio in his 30s. etc
I don't know of instances. But I'm also interested to know if people have good sources on this.
My understanding is that people entering the UK by air (e.g. from the US) now enter via ePassport gates and so don't need to talk to a border/immigration official. This might make it easier to enter than before. At the same time, I would be wary (based on what little I know) of entering without a clear explanation and evidence you are not working in the UK (e.g. epic holiday in UK, clear family reasons).
I believe you can spend 6 months in the UK visa free and there's no rule against more than 6 months out of the year. My understanding is that visitors will be vaccinated and treated for Covid by the NHS -- you may need to pay some modest fee.
China was somewhat unified and had a big chunk of the world's population and was more likely to record population levels -- though I'd guess there are huge error bars around the Three Kingdoms War and An Lushan Rebellion. If you control for political unity and population, were Chinese death rates in armed conflict higher than other regions?
The three historical figures I can think of who built giant institutions lasting thousands of years
Why draw the cutoff at thousands of years? And I'd guess recent institution building is much more relevant to EAs than ancient.
China does capitalism well without conflating capitalism with democracy
There were already the examples of Taiwan, South Korea, Hong Kong and Singapore. (One could also consider European and South American states that were right-wing dictatorships).
Nor does China entangle religion with politics to the same extent you find in the Christian and Islamic worlds.
Christian worlds? Secularism has been important in France since the French Revolution. What about India or Japan? What about Hellenistic culture or Rome?
Robust models of a region usually depend on knowing the region's history.
The question is how much "memory" or "persistence" the time series has. Mostly history is screened off by the present and recent past. You wouldn't predict North vs South Korea by looking at Korean history for any time period up to 1930s.
Outdoor is not viable in that much of the US during winter. Companies and individuals aren't using the microcovid methodology or the sources about risk. It's hard to trace infection to spending 20 mins in a cafe (vs from friends or family).
There was a huge number of cases before September around the world. Why didn't we see the new more transmissive variants earlier? (One source could be cross-over from some animals, another is the rare cases of extremely long-lasting Covid infection. Curious if people are doing Bayesian calculations for this.)
What can countries/states do? Impose hard lockdowns, focus test/trace/isolate resources on the new strain, stop travel, get people wearing N95s, create extra hospitals, vaccinate (using less effective vaccines as well as Pfizer/Moderna), run challenge trials to see how vaccines protect against new strain and against transmission, and ... hope for the best. One source of uncertainty is how much news of a complete collapse of hospitals in some region will impact behavior in regions that haven't collapsed yet. (I fear a "boy who cried wolf" scenario, where people think, "We never needed those temporary hospitals last time").
What can individuals do? If the new strain is not more severe, then the risk for young and healthy people remains low. Presumably staying at home and receiving deliveries still has very low risk of infection. People who might need hospital care for non-Covid reasons should make plans. (If health care collapses, how much bigger is the risk from Covid for young people? You'll probably get priority but standard of care will drop substantially.)
EDIT: Added some important points about vaccination I left out.
It should be possible to make rough estimates of chance the UK strain has reached country X by looking at the spread within the UK (where there's some coverage) and extrapolating based on volume of travel within UK and between UK and country X. If the UK data is too sparse now, it should be possible to do this in a week or two.
More information on Factored Cognition: the term was introduced by Ought and Ought has done a series of explainers and experiments on it. Ought also wrote a brief introduction to IDA, with a view to ML experiments.
More information on Factored Cognition: the term was introduced by Ought and Ought has done a series of explainers and experiments on it. Ought also wrote a brief introduction to IDA, with a view to ML experiments.
It's not a news source, but I find the Google and Apple Mobility data for Europe to be a useful measure of "how people are actually behaving on the ground". If people are going to retail/recreation locations (rather than ordering online), they are probably not taking the pandemic that seriously. Much of Europe eased up more than US before it had a rapid growth of cases (starting in August/Sep), and behavior hasn't changed much since this rapid growth.
in San Francisco, the so-called deaths of despair are both up 60% year over year and dwarf Covid-19 deaths four to one
These are mostly deaths due to fentanyl. When fentanyl displaces heroin in a region, it usually causes this kind of spike in deaths. (I don't know if there's an uptick in fentanyl in SF over the last few years, but such an uptick has happened in various places in the US). SF already had serious drug/homelessness problems. Why think this has anything to do with the specifics of SF's Covid response?
It also seems odd to criticize SF. Their Covid track record looks superb compared to major US or cities of Western Europe (save for Germany). Lots of businesses will be forced to close, but that's also true in places that have had more permissive rules.
Big improvements (for me -- YMMV): 1. Boston has two of the world's best few universities very close together. (It's hard to live close to Stanford without studying there, and it's a huge trek from Stanford to Berkeley). 2. There's an obvious Schelling point in Boston for where to live (Camberville), while interesting people/companies/organizations in the Bay are in SF, Oakland, Berkeley, and South Bay/Peninsula. 3. Boston is closer to NYC (and the other big East Coast cities) and Europe.
I'd guess Camberville is significantly cheaper in terms of overall COL than SF but it has similar big city amenities (concerts, opera, museums, huge diversity of events) that Berkeley lacks.
I've lived in Boston, NYC, SF Bay, and Oxford. For me, a big advantage of Boston was that most people I knew were clustered in a small area (Cambridge/Somerville or a short cycle away from them). This is radically different from the SF Bay, where people are spread across Berkeley (where UC Berkeley, MIRI, CFAR are), Oakland, SF (where Open Phil and many tech jobs are) and the Peninsula and South Bay (home of Stanford and many other tech jobs) and transport between these areas is mostly slow (esp without a car).
London, NYC, and Berlin have the same issue of people living far apart, but it's mitigated by better transport options than the SF Bay. Oxford has the same advantage as Boston. (NB: I was studying in Cambridge and so had more friends in that area. But at the time, many rationalists who weren't studying at Harvard/MIT also lived near Cam/Somerville.)
I presume the blinding is imperfect because some of the vaccines cause mild reactions that the placebo wouldn't. I doubt it's a big problem. The people doing the trial are selected for being more conscientious than the average person. (For one of the two trials, the rate of Covid seropositivity was only ~1% for people starting the trial, which is lower than the general US population). They will not want to risk their household members getting Covid, and they will have been warned that that the vaccines are unlikely to work perfectly.
Re: Why not do 300k instead of 30k for vaccine trials? Clearly bigger trials would be better -- especially as the current trials aren't that representative of the general population or the most at-risk groups. But presumably the logistical cost of 10x more patients is significant. You have to be testing all these people for COVID and following up on any possible adverse reactions. I think lack the lack of challenge trials is the biggest problem. (Note that AFAICT, UK trial is likely to happen but not 100% confirmed and it only starts in January.)
The Metaculus community forecast has chance of >95% dead (7.5%) close to chance of >10% dead (9.7%) for AI. Based on this and my own intuition about how AI risks "scale", I extrapolated to 6% for 100% dead. For biological and nuclear war, there's a much bigger drop off from >10% to >95% from the community. It's hard to say what to infer from this about the 100% case. There are good arguments that 100% is unlikely from both, but some of those arguments would also cut against >95%. I didn't do a careful examination and so take all these numbers with a grain of salt.
Good points. Unfortunately it seems even harder to infer "destruction of potential" from the Metaculus forecasts. It seems plausible that AI could cause destruction of potential without any deaths at all, and so this wouldn't be covered by the Metaculus series.
I've made a distribution based on the Metaculus community distributions:
(I used this Colab notebook for generating the plots from Elicit distributions over specific risks. My Elicit snapshot is here).
In 2019, Metaculus posted the results of a forecasting series on catastrophic risk (>95% of humans die) by 2100. The overall risk was 9.2% for the community forecast (with 7.3% for AI risk). To convert this to a forecast for existential risk (100% dead), I assumed 6% risk from AI, 1% from nuclear war, and 0.4% from biological risk. To get timelines, I used Metaculus forecasts for when the AI catastrophe occurs and for when great power war happens (as a rough proxy for nuclear war). I put my own uninformative distribution on biological risk.
This shouldn't be taken as the "Metaculus" forecast, as I've made various extrapolations. Moreover, Metaculus has a separate question about x-risk, where the current forecast is 2% by 2100. This seems to me hard to reconcile with the 7% chance of AI killing >95% of people by 2100, and so I've used the latter as my source.
Technical note: I normalized the timeline pdfs based on the Metaculus binary probabilities in this table, and then treated them as independent sources of x-risk using the Colab. This inflates the overall x-risk slightly. However, this could be fixed by re-scaling the cdfs.
I agree it makes sense to split into two components. Your first component could be called "mild but long COVID". By "mild", I just mean the person didn't ever require extensive hospital care. The second component sounds like permanent damage due to acute COVID. People with acute COVID were hospitalized and often spent long periods in intensive care. My thoughts/questions for you:
Mild+long COVID (1st component)
Studies: I haven't seen any rigorous, large-scale study that tries to estimate how common this is. How to do a study? Ideally there's a natural experiment, where you can compare matched populations with high vs low COVID rates (e.g. Milan vs. Rome, SF vs. LA, Stockholm vs Oslo). Failing that, you at least sample randomly from all people who had COVID using antibody tests or population PCR testing and then find a demographically matched control group. You take objective measures of their condition, e.g. employment, sick days, fitness test, and various medical tests of health. A "quick and dirty" approach is to find workplaces where a high proportion tested positive (hospitals in first wave in London/Lombardy/Madrid/Wuhan, meat plants, sports teams) and find out what proportion of people are back at work full-time.
Demographics: From existing (flawed) studies and surveys, it seems to be more common in middle age than say 10-15 year olds and 60+ year olds. It seems more common among women (perhaps more than 2:1), which I believe fits some other post-viral or auto-immune conditions. If this holds up, it might give some update in terms of personal risk.
Duration: You give a 6-month expected duration. How did you estimate this? The reference class for this component is presumably post-viral and auto-immune conditions, which (IIRC) have a longer than 6-month expected duration. Presumably you are updating on actual evidence from Long COVID sufferers. (There's also various reports of people who experienced mild symptoms having some organ damage on examination. This might also suggest a more than 6-month duration for full recovery.)
Chronic Post-Acute COVID (2nd component)
Studies. There seem to be more studies of this component because you just need to follow up with people who were hospitalized and so there aren't the same sampling issues. The UK is doing a large study on this. The reference class for this study is (presumably) people suffering from the conditions caused by acute COVID, which include pneumonia, ARDS, cytokine storm, vascular problems, etc. I think there aren't large absolute numbers of people in their 20s without comorbidity who were hospitalized in any one location, and so getting a well-powered study on them might be non-trivial.
Demographics. Severity rates for COVID are very sensitive to age and somewhat sensitive to comorbidity. Does your 0.5% estimate take this into account? I can imagine that for someone in their 20s without comorbidity, the rate of chronic damage from acute COVID would be less than 0.5%. (For such people, I'd guess death rate is < 1/5000 and that permanent damage is less than 10x more likely than that. But I'm fairly uncertain about this.)
Future treatment: If the rates of these two kinds of post-COVID are as high as you estimate (0.5% and 3%), then there will be millions of people across Europe/US/Mexico etc. with these conditions. So there will be a huge incentive to improve treatments. Maybe some kinds of "permanent" damage are very hard to ameliorate, but if you're doing the projection out for 20-30 years from today, I'd be optimistic. (It seems that hospital treatment for COVID has already improved significantly. There'll be lots more cases in the next 6 months and so further improvements are expected).
You need to first run all the cells in "Setup Code" (e.g. by selecting "Runtime > Run before"). Then run the cell with the form input ("risk1label", "risk1url", etc), and then run the cell that plots your pdf/cdf. It sounds like you're running the last cell without having run the middle one.
We made a Colab notebook that lets you forecast total x-risk as a combination of specific risks. For example, you construct pdfs for x-risk from AGI and biotech, and the notebook will calculate a pdf for overall risk. This pdf is ready to display as an answer. (Note: this example assumes AGI and biotech are the main risks and are statistically independent.)
The notebook will also display the cdf for each specific risk and for overall risk:
As a bonus, you can use this notebook to turn your Elicit pdf for overall x-risk into a cdf. Just paste your snapshot url into first url box and run the cell below.
PSA. The report includes a Colab notebook that allows you to run Ajeya’s model with your own estimates for input variables. Some of the variables are “How many FLOP/s will a transformative AI run on?”, “How many datapoints will be required to train a transformative AI?”, and “How likely are various models for transformative AI (e.g. scale up deep learning, recapitulate learning in human lifetime, recapitulate evolution)?”. If you enter your estimates, the model will calculate your personal CDF for when transformative AI arrives.
Here is a screenshot from the Colab notebook. Your distribution (“Your forecast”) is shown alongside the distributions of Ajeya, Tom Davidson (Open Philanthropy) and Jacob Hilton (OpenAI). You can also read their explanations for their distributions under “Notes”. (I work at Ought and we worked on the Elicit features in this notebook.)
These are patients who had a positive test in April. Most infected people without symptoms or with mild symptoms did get tested in April in the US. We know about 20-40% are asymptomatic, with higher % among younger people. So actual rate based on this study would be upper bounded by 1/4 (not 1/3) and point estimate closer to 1/5. (I also agree with SDM).
To be clear, I think the 71% result needs more investigation and (on priors) is probably lower. Yes, there is reason to expect overshoot. It seems the amount of overshoot would vary based on (a) NPIs being taken at the time (e.g. are some people never leaving the house) and (b) proportion of people who have cross-immunity or innate reduced susceptibility. (In principle, you could imagine 80% of people in a town live as normal and 20% won't leave the house till the pandemic is over.) Again, I think if we did a lot of studies, we'd get a sense of both the minimum herd immunity threshold and the variability in overshoot.
This naive model is not a straw man! Such obvious nonsense models are the most common models quoted by the press, the most common models quoted by so-called ‘scientific experts’ and the most common models used to determine policy.
I think you underestimate the sophistication of the top epidemic modelers: Neil Ferguson, Adam Kucharski, Marc Lipsitch, and others. I tend to agree we need urgent empirical work on herd immunity thresholds (see my other comment) but the top epi people are aware of the considerations you raise. Communicating with the public is very challenging under the current circumstances and so it's reasonable these people would choose words carefully.
Your statement is also empirically false. One of the most influential models is the "Imperial Model", which certainly impacted UK policy and probably US and European policy too. Other countries did versions of the model. The lead researcher on the model literally became a household name in the UK. The Imperial Model is an agent-based model (not an SIR model). It has a very detailed representation of how exposure/contact differ among different age groups (work vs. school) and in regions with different population densities. It doesn't assume the only intervention is immunity, and follow up work has tested many different interventions. (AFAIK, it does assume equal susceptibility. But as it's an agent-based model you could experiment with heterogeneity in susceptibility. And I think evidence for variable susceptibility for reasons other than age remains fairly weak: https://twitter.com/OwainEvans_UK/status/1268873649202909185)
IMO what's needed here is detailed empirical analysis. There are many places round the world that have had spread that was only weakly controlled. If you get the % seropositive for a bunch of places, you could (to some extent) extrapolate to Europe/US/East Asia, where there's currently more control. Here's where I'd look:
Brazil has had a raging epidemic for quite a few months. % positive tests is currently >70%. It seems very likely that some towns have hit herd immunity. Similar story for South Africa and Mexico. (Many other countries have similarly bad epidemics, but these three have relatively good data.)
Some Indian states had >25% seropositive in studies that started in early July and there are huge number of new cases since then. Again, some towns have probability hit herd immunity.
Could also look at villages near Bergamo in Italy.
This study found 16% seropositive in a small town in Germany (e.g. with low population density). This town was locked down after an outbreak and so the 16% almost certainly underestimates the herd immunity threshold. This study was done pretty carefully (though the lead author has an axe to grind).
Probably the only engineering fields that are doing really well are computer science and maybe, at this point, petroleum engineering. And most other areas of engineering have been bad career decisions the last 40 years … Nuclear engineering, aerospace engineering [were catastrophic fields to go into]
Where's his evidence on this? This data suggests average salaries for engineers outside software engineering were not much different from software engineering. I'd guess there's more exciting new companies in computing than in aerospace, but it doesn't mean it was a "catastrophic career move". US companies also sell a lot of products abroad and there's been huge growth in use of aircraft, cars, and other engineered products worldwide (due to catch up growth).
Why did all the rocket scientists go to work on Wall Street in the ‘90s to create new financial products?
Because the Cold War ended. There's no big mystery. If you weren't "allowed" to make rockets, how to explain SpaceX (started in 2002)? Not to say regulation doesn't limit innovation, but I'd want to see actual data on this and not just bluster.
You are understanding correctly. Here are some things to keep in mind:
The reproductive number R before lockdowns was estimated at 2-3. People are infectious for 4-7 days. The average person has contact with about 10 other people daily (paper). So there could be 20-50 unique contacts over 4-7 days. Maybe 10-30 of those are higher risk contacts (long duration, close proximity). So only 5-33% of higher risk contacts are being infected (using these very rough numbers). So I'd say that Covid is not very contagious. Note that R for measles is 12-18!
There is probably some overdispersion. Say 20% of people do 60-80% of all infecting. So many people cause zero new infections. I'd presume such people just aren't very infectious and so even if they spend a lot of time with household members they won't infect them.
The 30% is averaging over all household members, which includes children. Children are probably less susceptible (e.g. they might have 50% lower risk of infection).
Once someone develops Covid symptoms, many households will intervene to reduce exposure. If adult children get sick, they are likely to try to isolate from their older parents.
There is a small number of studies that distinguish spouse from other relationships. See Figure S5 of this paper. I don't think there's enough data to draw a strong empirical conclusion. Most of our data for estimating SAR is from China/Korea/Taiwan and I'd guess these are mostly nuclear families or extended family (not many group house / flatmates).
I've talked extensively over many posts about why I think herd immunity is a bigger deal than people think
I understood the argument as "there'll be herd immunity faster in specific locations (e.g. subway riders or people under 20 in some neighborhood)". The logic makes sense but I'd guess the effect is small, due to population mixing / small-world network effects. Young people are probably getting infected more but they are still far from HI everywhere and they are probably well mixed. I haven't seen any positive empirical evidence for your view over my take (big first wave --> people take precautions more seriously and have slower reopening + 20-30% drop in R due to fewer susceptible).
There's Google/Apple style mobility (which actually records amount of time spent in work/home/retail/public transit) and questionnaires that ask for "number of contacts per day". People have used both to model cases/deaths and they are both pretty useful. Somepapers (China) and UK. The point is that we know you can predict spread using these proxies for contact. So you can actually see if the amount of predicted contact is lower in NYC, London, Madrid and Lombardy vs. places that didn't have a big first wave (e.g. LA, Miami, Phoenix). And the predicted contact was lower in the former places. (But I haven't done a careful study).
2. Sweden did badly, but it's important to notice that it did far less badly than a naive model would expect it to do. Why did things end up getting contained when they did? Why wasn't it much worse?
Public transit use was down 55% in Sweden at peak and is still at -7%. Norway was down 65%. Swedes stopped going to the cinema and other high-risk venues were way down. Without a formal lockdown, there was a huge change of behavior in Sweden. I'd guess Swedes were aware that all the countries around them had tighter restrictions and much lower death tolls. So they acted to reduce risk. (People in the UK also reduced risk more than was required by government.) So I don't see any mystery in Sweden. The real mysteries: Vietnam, Thailand, Cambodia, Laos and Indonesia. And I'm surprised how well the SF Bay has done.
4. It's shocking because those people are having very intimate contact over extended periods of time
Agree it goes against the naive model. But if you take seriously that 20% of people do 80% of infecting (or maybe a bit less than that), then it's likely that a decent proportion are essentially not infectious. Also note that many household members are younger children, who are harder to infect.