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.
Every year, this set of tools has become more actionable for protecting human and wildlife health. We can use these tools to spot "Disease X" before the first human case, to sample wildlife reservoirs for new viruses more efficiently, and even to predict climate change impacts.
So why have decades of efforts to "predict and prevent the next pandemic" fallen flat? Two answers: (1) We keep missing our chances to actually apply these tools for actionable prediction. (2) Prevention actually falls on health systems, not scientists.
But we're at the tipping point right now where we can start to use these tools to do a lot of good. That's why @viralemergence exists in the first place - we want to build and share new tools, and the datasets we need to power them, to close this gap and save lives.
If that sounds good, you can see more of what we do at viralemergence.org, and we'll be posting about different aspects of our study and the work we do on the host-virus network all day 🎉

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More from @wormmaps

24 Nov
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.
Read 8 tweets
24 Nov
The definitive @viralemergence review is out today. Here's a thread-of-threads if you're looking to catch up:
A quick explainer of the study itself:
A guide to the Host-Virus Model Database:
Read 5 tweets
24 Nov
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.
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.
Read 30 tweets
24 Nov
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?)
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)
Read 8 tweets
20 May
The next coronavirus is here. 🦠🚨

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 has *always* been true, even - maybe especially - for coronaviruses:
(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.)
Read 13 tweets
17 Mar
Something missing in a lot of viral ecology / "stop pandemics at the source" work right now:

If you're not including flu in your schema for pandemic risk, you're not actually talking about pandemics. You're talking about general disease emergence, not pandemic preparedness.
Before COVID-19, the answer to the question "what's the next pandemic most likely to be" was influenza. After COVID-19? It's actually still influenza believe it or not
So much of how we respond when a new virus emerges in a new pathway is to try to hyperfocus on sealing that entryway. But it's a bit like only locking the specific window a burglar came into your house through, and not checking the front door.
Read 4 tweets

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