Computational Approaches to Pathogen Detection

Thanks for sharing, Jeff! Are you aware of any analyses quantifying the risk of "stealth" pandemics in terms of expected deaths or probability of a certain death toll in a given period?

Executive summary: The post discusses computational approaches for detecting novel pathogens in metagenomic sequencing data as a way to identify stealth pandemics before widespread infection.

Key points:

1. Metagenomic sequencing of biological samples like sewage can reveal nucleic acids from novel pathogens.
2. Algorithms can flag concerning sequences that are dangerous, modified from known pathogens, completely new, or growing exponentially.
3. Each approach has benefits and challenges in accuracy, requiring updates to databases and biological knowledge.
4. Secure encrypted databases like SecureDNA may reduce risks of hijacked pathogen watchlists.
5. Research is needed to improve metagenomic assembly and timeseries analysis for detecting novel and growing sequences.
6. Parallel development of multiple detection approaches is recommended, with short-term focus on known dangerous and modified sequences.

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