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.
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.
(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.)
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.
Half a million dead. It speaks to the complete and total failure of the American healthcare system. If you come out of this pandemic with the audacity to see pandemics as part of your expertise, and that's not at the front of your list of failures, you're doing your job wrong
There is no talking about pandemics without talking about the moral bankruptcy and structural rotten fucking wood that is a for-profit, privatized, fragmented, discriminatory healthcare system. There is literally nothing but this to talk about anymore
I've spent my entire life working on climate, I've ground myself into a pulp for the last 3 years working on climate + health, and if I had to choose between a global conversation about climate-and-pandemics and a conversation about U.S. healthcare, I'd pick the latter
A few years back, McNeil wrote a book called Zika: the Emerging Epidemic, which I read and reviewed for Quarterly Review of Biology in April 2018.
I was absolutely shocked with how he talked about race, and about women - both in the abstract, and in his specific interactions.
A couple spicy tweets got some conversation going in my Twitter circles, but that was it. Since then, whenever folks talk about his various odd takes, I've always thrown this example out - and folks are often surprised to see it. (I don't think the book was widely read 🙃)
Our ensemble doesn't predict Rhinolophus shameli but our two best models - Trait-1 and Network-1 - both do, bringing their hit rate up to 22/24 and 15/15 respectively. (Network-1 still undefeated!) Updates will follow shortly on viralemergence.org/betacov
As the authors point out, SARS-like viruses are still fairly understudied deeper in southeast Asia, but our model predicts that should be a hotspot of undiscovered bat βCoVs....
So post-workshop, the World Meteorological Organization's Task Team on COVID-19 and Climatic, Meteorological, and Environmental Factors has published some guidance on how to do the science. It's nice, but missing the words "talk to an epidemiologist"
It's tough. I appreciate what they've done here, and they very clearly nod to our piece on how climate-but-not-epidemiology experts got things wrong. But I also still think, 10 months in, the magic words are "talk to an epidemiologist about your understanding of the system."
This is particularly salient given that they actively encourage scientists to do public facing communication that "...includes informing media outlets or policy makers of dissenting views and encouraging the presence of multiple voices in coverage of their work."
Take SARS-CoV as a counterfactual, where tracing back to wildlife trade was efficient and transparent. Civets are linked to SARS-CoV before the outbreak ends, and horseshoe bats are implicated as the reservoirs of SARS-like viruses by 2005. Access and cooperation at work! But...
The actual reservoir species isn't fully tracked down and published until 2017. That has less to do with early outbreak transparency, and more to do with the arduous nature of tracing viral origins in the wild:
This week I wrapped up COVID-19 related policy work. Just to quickly pin it, here's a reference thread of my writing about various pandemic topics.
Epidemic forecasts are important, but often fail to translate to on-the-ground decision making. We list a handful of high-priority questions, from basic epidemiology to healthcare data science, that policymakers have been asking us to help them answer.
The wildlife trade is implicated in a tiny fraction of emerging disease outbreaks worldwide (and has no concrete link to SARS-CoV-2). Centering wildlife trade regulation as "pandemic preparedness" undermines the work of global health experts.
The 2014 outbreak in West Africa, the largest to date, was traced back to human-bat contact without any link to wildlife trade. Many scientists find the evidence for this incomplete, but it's probably impossible to know now. (2/4)
Some (not all) outbreaks since 1976 were linked to human consumption of wildlife, especially wild primates or bats as a primary food source. This is not the big, international wildlife trade with a criminal underworld side that conservationists mean (3/4)
New COVID-19 comment: Species distribution models are a great tool, but wrong for a respiratory virus. Here's an explainer of where ecologists went wrong, and why we have to stop right now, before people get killed.
Let's say that after COVID-19, you wanted to discover every single animal virus that can make a person sick, from Aichi to Zika.
How many are there?
How long would it take?
A little thread for a quiet night.
When you walk into a party, you meet the most strangers early in the night; the longer you’re there, the fewer new people you’re likely to meet, and the more repeats you hit.
Ecologists usually measure diversity the same way, using what they call “rarefaction curves.”
Every year, about 2-4 new animal viruses that can infect humans – zoonotic viruses – are discovered. Even though we’re being hit by more virus outbreaks every year, we started hit a plateau in zoonotic virus discovery over the 20th century.
Can I talk to the public as a modeler for a second?
I've been training for something like this for 10 years. I've been doing outreach, posting, publishing and now I'm asking you:
Don't listen to modelers right now. Don't try to understand the full range of good and bad science.
Normally, there's a bell curve of good and bad science - some really brilliant work, some really confused work - but it all sort of approximates the truth like a shotgun blast.
Right now, every single person with my training is doing this work. I can't even keep up with it.
What that means is that every now and then, there's going to be a paper that says something like "Africa won't be hit by COVID because it's too hot" or "10 million Americans were infected weeks ago and the disease isn't severe."
I want to add a few points of nuance that I wish had made it into this, starting with the fact that today, the program was extended for 6 months / $2.26 million budget, focused on 1. COVID-19 diagnostics, and 2. Tracing wildlife origins of SARS-CoV-2