Co-lead (Near-Term Detection) at the Nucleic Acid Observatory in Boston. Speaking for myself unless I say otherwise.
jefftk
Karma: 21,236
It’s open; no door.
Sorry for assuming you were also in the US!
since the scale of damages in the upper tail exceeds almost everyone’s accessible wealth
Car insurance is [edit: in the US] bounded: a standard policy will cover you up to some cap (ex: $50k). I think maybe your comment is a better argument for umbrella insurance, though that is also not infinite.
While it’s nice to know the mechanism, I think all we really need in this case is the empirically determined performance curve.
Other, more targeted risks, such as bioweapons, pandemics and viral outbreaks would be better served by these shelters
I think they could maybe be appropriate for some bioweapons, but for most pathogen scenarios you don’t need anywhere near the fourteen logs this seems to be designed for. So I do think it’s important to be clear about the target threat: I expect designing for fourteen logs if you actually only need three or something makes it way more expensive.
Filtering liquids is pretty different from air, because a HEPA filter captures very small particles by diffusion. This means the worst performance is typically at ~0.3um (too small for ideal diffusion capture, too large for ideal interception and impaction) and is better on both bigger and smaller particles. The reported 99.97% efficiency (2.5 logs) is at this 0.3um nadir, though.
It’s not really an edge thing, it’s a top vs inside thing. So I wouldn’t expect more side surface area to help?
This is good! But note that many things we call ‘insurance’ are not only about reducing the risk of excessive drawdowns by moving risk around:
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There can be a collective bargaining component. For example, health insurance generally includes a network of providers who have agreed to lower rates. Even if your bankroll were as large as the insurance company’s, this could still make taking insurance worth it for access to their negotiated rates.
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An insurance company is often better suited to learn about how to avoid risks than individuals. My homeowner’s insurance company requires various things to reduce their risk: maybe I don’t know whether to check for Federal Pacific breaker panels, but my insurance company does. Title insurance companies maintain databases. Specialty insurers develop expertise in rare risks.
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Insurance can surface cases where people don’t agree on how high the risk is, and force them to explicitly account for it on balance sheets.
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Insurance can be a scapegoat, allowing people to set limits on otherwise very high expenses. Society (though less LW, which I think is eroding a net-positive arrangement) generally agree that if a parent buys health insurance for their child then if the insurance company says no to some treatment we should perhaps blame the insurance company for being uncaring but not blame the parent for not paying out of pocket. This lets the insurance company put downward pressure on costs without individuals needing to make this kind of painful decision.
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Relatedly, agreeing in advance how to handle a wide range of scenarios is difficult, and you can offload this to insurance. Maybe two people would find it challenging to agree in the moment under which circumstances it’s worth spending money on a shared pet’s health, but can agree to split the payment for pet health insurance. You can use insurance requirements instead of questioning someone else’s judgement, or as a way to turn down a risky proposition.
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Short story about this from a few years ago: Your DietBet Destroyed the World. Mirror bacteria developed to produce L-Glucose, everything is fine until there’s an accident.
Here is a now-public example of how a biological infection could kill us all: Biological Risk from the Mirror World.
I don’t think this makes much sense. In a regulated industry, you want to build up a positive reputation and working relationship with the regulators, where they know what to expect from you, are familiar with your work and approach, have a sense of where you’re going, and generally like and trust you. Engaging with them early and then repeatedly over a long period seems like a way better strategy than waiting until you have something extremely ambitious to try to get them to approve.
Funny! I almost deleted the cross-post because it seemed too short to be interesting here.
Put particles in the air and measure how quickly they’re depleted. ex: Evaluating a Corsi-Rosenthal Filter Cube
Sounds like I should try repeating this with someone with a higher voice!
I think that’s right! Not a reason to take up vaping, though.
There’s probably a way to do this with physics, but I do a lot with trial and error ;)
I do think expanding the ceiling fan air purifier would work well. You could make a frame that takes furnace filters, and purify a lot of air very efficiently and relatively cheaply.
If I were doing this again I would extend the filters down below the plane of the fan, now that I know more about how the Bernoulli principle applies.
I assume this is for one location, so have you done any modeling or estimations of what the global prevalence would be at that point? If you get lucky, it could be very low. But it also could be a lot higher if you get unlucky.
We haven’t done modeling on this, but I did write some a few months ago (Sample Prevalence vs Global Prevalence) laying out the question. It would be great if someone did want to work on this!
Have you done any cost-effectiveness analyses?
An end-to-end cost-effectiveness analysis is quite hard because it depends critically on how likely you think someone is to try to create a stealth pandemic. We’ve done modeling on “how much would it cost to detect a stealth pandemic before X% of people are infected” but we’re not unusually well placed to answer “how likely is a stealth pandemic” or “how useful is it for us to raise the alarm”.
What’s the core reason why the NAObservatory currently doesn’t provide that data?
Good question!
For wastewater the reason is that the municipal treatment plants which provide samples for us have very little to gain and a lot to lose from publicity, so they generally want things like pre-review before publishing data. This means that getting to where the’d be ok with us making the data (or derived data, like variant tracking) public on an ongoing basis is a bit tricky. I do think we can make progress here, but it also hasn’t been a priority.
For nasal swabs the reason is that we are currently doing very little sampling and sequencing: (a) we’re redoing our IRB approval after spinning out from MIT and it’s going slowly, (b) we don’t yet have a protocol that is giving good results, and (c) we aren’t yet sampling anywhere near the number of people you’d need to know what diseases are going around.
when in the future would you expect that kind of data to be easily accessible from the NAObservatory website?
The nasal swab sampling data we do have is linked from https://data.securebio.org/sampling-metadata/ as raw reads. The raw wastewater data may or may not be available to researchers depending on how what you want to do interacts with what our partners need: https://naobservatory.org/data
Right! I agree there are advantages to getting people onto your site beyond the opportunity to show them ads or convince them to buy a subscription. The post, though, is about the consequences of being in the fortunate position of not needing to do this.