Detection of Asymptomatically Spreading Pathogens

By Jeff Kaufman 🔸 @ 2024-12-05T19:17 (+46)

This is a crosspost, probably from LessWrong. Try viewing it there.

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OscarD🔸 @ 2024-12-14T19:23 (+7)

I found this a really clear and useful explanation (though I already had a decent idea how NAO worked)!

If ever you want to reach a broader audience, I think making an animated video based on this content, maybe with the help of Rational Animations or Kurtzgesagt, would work well.

Assuming a key inefficiency of the nasal swabs method is the labour costs of people collecting them, is the process straightforward enough that you could just set up an unmanned sample collection place where in a busy building somewhere people can just swab themselves and drop the sample in a chute or box or something? Hopefully post-Covid people are fairly familiar with nasal swabbing technique.

Jeff Kaufman 🔸 @ 2024-12-16T01:37 (+4)

I think the main downside of setting up a sample collection box is that the samples would probably sit a lot longer before being processed, and RNA degrades quickly. I also suspect you wouldn't get very many samples.

(The process itself is super simple for participants: you just swab your nose.)

SummaryBot @ 2024-12-06T17:36 (+1)

Executive summary: SecureBio's Nucleic Acid Observatory is developing an early warning system to detect engineered pathogens before they cause widespread infection, using a combination of wastewater sampling, pooled nasal swabs, and advanced computational analysis methods.

Key points:

  1. "Stealth pandemics" with long pre-symptomatic periods pose a serious threat as they can spread widely before detection through traditional symptom-based surveillance.
  2. The system uses two complementary sampling approaches:
    • Wastewater sampling (cost-effective but requires deep sequencing)
    • Pooled nasal swabs (better signal-to-noise ratio but logistically challenging)
  3. Detection methods include:
    • Matching against known pathogens
    • Identifying genetic engineering signatures through junction detection
    • Tracking growth patterns
    • Novelty detection (still in research phase)
  4. System sensitivity estimates suggest $10M/year could operate a system capable of detecting novel pathogens before 1:1000 people are infected, though uncertainty remains.
  5. Some components are ready for deployment (junction detection), while others need more development (reference-based growth detection) or significant research (novelty detection).

 

 

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