As part of the LessWrong Coronavirus Link Database, Ben, Elizabeth and I are publishing update posts with all the new links we are adding each day that we ranked a 3 or above in our importance rankings. Here are all the top links that we added yesterday (March 24th), by topic.
You can find the full database here: https://www.lesswrong.com/coronavirus-link-database
Dashboard with estimates and predictions of true prevalence
A dashboard that gives you estimates for current prevalence by country, as well as predictions for the future based on varying amounts of mitigation.
What will the economic effects of quarantine be?
LW attempts to predict what the effects of a short or long quarantine will be
Flexport CEO explains why scaling PPE is hard
Outlines the difficulties in scaling, including QA and legal issues
Reddit thread of people who lost their jobs due to coronavirus or quarantine
Spread & Prevention
They're useful but the gains may be overwhelmed by any risk compensation, and they need to be saved for medics
(EV) He left out a swath of studies on mask use in mass gathering
A summary of the best suggestions from the justified practical advice thread
Work & Donate
A list of questions we want answered to inform future decisions, and assembly of answers as they're created
Does anyone know why the dashboard says infections will peak at 3% if no mitigation is done?
That’s active infections. That number corresponds to something like 70% of the population having been infected at some point.
It still looks weird to me. For example, in Switzerland with no mitigation it estimates 1% of people infected now and 3% at the peak on Apr 14, which is 2.5 weeks from now. Since each infection lasts a couple weeks or more, and there have been few deaths and recoveries so far, that means <5% of the population will have been infected by that point. And then it says active infections will start falling. Why?
I think the model uses a much shorter time for active infections than 2.5 weeks. Not sure what it is, but I think it's closer to 5 days or something like that, which seems to actually fit the behavior of the disease best, on a broad scale.
Agree that it looks weird. I've asked the authors of the project to add a cumulative graph, which makes these assumptions a lot clearer.
We used parameters based on a paper modelling Wuhan, that found that ~2 day infectious period predicted spread the best.
Adding cumulative statistics is in the pipeline; I or one of the devs might get around to it today.
Wait, so your graph shows the number of people having their 2-day "infectious period" at any given time, which could be much lower than the number of people infected at a given time? That doesn't seem to be explained on the page.
Anyway, I think the really important number is how many people are having their "required hospitalization period" at any given time (which is longer than 2 days). Maybe you could show that too, since you're already showing the "care capacity" line?