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Machine Learning
The more we sample, the more we get a picture of what's normal. This will make it easier for us to detect new and concerning conditions. We are encouraged by fields like protin design where machine learning is already being used. But i think this would require massof infra structure investment. And am a really likeasata an off swing to to our actual understanding.
nCoV is alarming governments and citizens around the world. It has killed more than 1,000 people, brought the Chinese economy to a standstill, and continues to show up in more and more places. But bad though it is, it's much closer to a warning shot than a worst case scenario. The next emerging infectious disease could easily be more contagious, more fatal, or both.
Despite improvements in the last few decades, humanity is still not nearly prepared enough to contain new diseases. We identify them too slowly. We can't do enough to reduce their spread. And we lack vaccines or drugs treatments for at least a year, if they ever arrive at all.
• Links to learn more, summary and full transcript.
This is a precarious situation, especially with advances in biotechnology increasing our ability to modify viruses and bacteria as we like.
In today's episode, Cassidy Nelson, a medical doctor and research scholar at Oxford University's Future of Humanity Institute, explains 12 things her research group think urgently need to happen if we're to keep the risk at acceptable levels. The ideas are:
Science
1. Roll out genetic sequencing tests that lets you test someone for all known and unknown pathogens in one go.
2. Fund research into faster ‘platform’ methods for going from pathogen to vaccine, perhaps using innovation prizes.
3. Fund R&D into broad-spectrum drugs, especially antivirals, similar to how we have generic antibiotics against multiple types of bacteria.
Response
4. Develop a national plan for responding to a severe pandemic, regardless of the cause. Have a backup plan for when things are so bad the normal processes have stopped working entirely.
5. Rigorously evaluate in what situations travel bans are warranted. (They're more often counterproductive.)
6. Coax countries into more rapidly sharing their medical data, so that during an outbreak the disease can be understood and countermeasures deployed as quickly as possible.
7. Set up genetic surveillance in hospitals, public transport and elsewhere, to detect new pathogens before an outbreak — or even before patients develop symptoms.
8. Run regular tabletop exercises within governments to simulate how a pandemic response would play out.
Oversight
9. Mandate disclosure of accidents in the biosafety labs which handle the most dangerous pathogens.
10. Figure out how to govern DNA synthesis businesses, to make it harder to mail order the DNA of a dangerous pathogen.
11. Require full cost-benefit analysis of 'dual-use' research projects that can generate global risks.
12. And finally, to maintain momentum, it's necessary to clearly assign responsibility for the above to particular individuals and organisations.
These advances can be pursued by politicians and public servants, as well as academics, entrepreneurs and doctors, opening the door for many listeners to pitch in to help solve this incredibly pressing problem.
In the episode Rob and Cassidy also talk about:
• How Cassidy went from clinical medicine to a PhD studying novel pathogens with pandemic potential.
• The pros, and significant cons, of travel restrictions.
• Whether the same policies work for natural and anthropogenic pandemics.
• Ways listeners can pursue a career in biosecurity.
• Where we stand with nCoV as of today.
Chapters:
Get this episode by subscribing: type 80,000 Hours into your podcasting app. Or read the linked transcript.
Producer: Keiran Harris.
Transcriptions: Zakee Ulhaq.
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