Quick thread on Omicron, virulence evolution, and health security the next time we have to do this…
I’m cautiously encouraged by the South African mortality data (give it time though) and in vitro data suggesting a bit of a tissue tropism shift with less capacity to infect lung cells! And, nervously curious what the sequelae data will look like, when we have them.
If, gun to my head, I had to gamble with any variant, this is probably the one that, as a severe asthmatic, scares me the least - but still scares me a lot, enough I’m not spending any time seeing friends during this peak.
But, maybe this is a bit of a promising development for imagining how this ends. *maybe*
If this does mark the start of a longer term shift to something 10% more like a seasonal flu than SARS classic, great! That would be a nice and lucky development. (No, we’re not there yet, evidence or evolution wise.)
But I’m worried that if this IS true, it will elicit another “the experts got it wrong!” clusterfuck from science journalists, pundits, and the public about a fundamental *consensus*: virulence evolution has predictable qualities but *definitely* doesn’t have to go this route.
And I’m worried, as we’ve seen with other key ideas, that some scientists will go with it or rise to prominence saying “this is what I said will happen!” and ride that wave into…well, this is where I get nervous.
Why am I worried? Future variants, sure, but more than that - if this creates a permanent impression that a “let er rip” approach encourages this evolutionary trajectory, it will almost certainly be the primary motivation for potential mass death when the *next* Disease X emerges
Do not put your faith in a fucking novel SARS virus, or the next MERS, or whatever! Please god do not do this.
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One last thing before I drop off for the holidays… a bit of behind the scenes on our new paper
Before there was a thing called Verena, there was me and @Gfalbery, some long chats over beers, and 12% of an idea for something called VirusNet - a team that would stop duplicating efforts and pseudoreplicating the same analyses of the host-virus network, and go far together
It’s absolutely bewildering to think this is the first thing @viralemergence worked on and that it’s finally, finally out there.
And now, a mega-thread: If you've ever wondered what connects all our work at @viralemergence, our new paper in @NatureMicrobiol ties it all together. No, really. It's all one thing. Want to step through the Verena Cinematic Universe together?
Our team uses big data, statistics, and machine learning to understand "the science of the host-virus network", a broad methodological problem that includes a number of smaller, more applied problems. nature.com/articles/s4156…
If you want to study the host-virus network, you need data. But as @roryjgibb & co. showed, existing datasets are full of taxonomic inconsistencies and conflicts. We needed a synthesis. academic.oup.com/bioscience/art…
As a companion to our new paper in @NatureMicrobiol we've opened up the Host-Virus Model Database, the @viralemergence team library of studies that try to predict the host-virus network. How does it work? 🧵
In our new paper, we define a taxonomy of six types of models that try to predict the host-virus network; in practice, they don't always look and feel like network questions (e.g., do some mammals have a higher richness of zoonotic viruses?) nature.com/articles/s4156…
We outline six big model "shapes": predicting host-virus associations; host / reservoir / vector identification; predicting zoonotic potential; predicting viral sharing; analyzing viral host range and host viral richness. Plus, some odd ones out (e.g., viral transmissibility)
With machine learning and network science, we can start to recover the source code of the global virome. We wrote an instruction manual - out now in @NatureMicrobiol - and along the way, we've tried to solve what it would mean to really "predict and prevent the next pandemic" 🧵
Practically every big question about viral ecology, evolution, and emergence - from "why do bats have so many deadly viruses?" to "can we spot a pandemic flu before the first human case?" - is a variation on a fundamental scientific challenge: predicting the host-virus network.
Over three years of research, we've compiled these kinds of studies into a unified framework, allowing us to put our finger on a new convergence science - "the science of the host-virus network" - that uses computational inference to understand viral biology across scales.
We're learning today that alphacoronavirus 1, previously not known to be zoonotic, jumped from dogs to humans > a year ago. Key lessons for where COVID-19 has been pointing us the wrong direction 🧵
1⃣ Singular focus on wildlife trade / wildlife farming as a human-animal interface is a mistake, given other natural pathways of emergence like, here, pet dogs or cats (probably).
(This doesn't mean we have to start getting rid of pets / livestock to prevent pandemics! The point of building strong healthcare systems - including One Health monitoring systems that include vets - is to stay safe by catching these kinds of events early and often.)