Some graphs from the UK:
I can’t see the graph. I’d also love to know the variability across people and demographics.
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.)
Other sources of evidence (albeit weaker): the nature of the mutations (some of which have been studied prior to emergence of the new strain), the related evidence from South Africa.
I stand by my claim. We know the effects 10 months out. If some studies have convinced you otherwise, it would be useful to cite the evidence (maybe in a separate post).
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.
David MacKay: Sustainable Energy – without the hot airDavid MacKay: Information Theory text bookSteven Pinker: How the Mind Works, The Stuff of Thought (Cognitive science, linguistics, philosophy of language)
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.)
Is there a scientific paper on the testing of flight staff? The airline industry is in dire straits and so it’s not surprising they can produce a positive statistic on the (alleged) safety of flying.
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.