Thread by @wormmaps

Today, the U.S. extended a program, defunded last year, to stop pandemics by studying wildlife viruses.

For today's very first issue of Lancet Microbe, I've written about USAID PREDICT, why it was important, and why it wasn't enough to stop COVID-19:

thelancet.com/journals/lanmi…

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

ucdavis.edu/coronavirus/ne…

Even before this announcement, PREDICT-funded labs around the world (esp. Africa and Asia) have already been pivoted to COVID diagnostics.

I'm optimistic that the capacity building aim of PREDICT will pay off in this crisis.

Tracing the wildlife origins is a more complicated one.

It's a key part of normal One Health outbreak response. But these aren't normal times, and every available dollar of aid money could make a critical difference for healthcare workers & saving lives in low-resource settings.

In the pandemic of the century, it's disconcerting to see any emergency aid money spent on wildlife research in affected countries -

Especially when polio campaigns, and the other things aid programs normally fund, are being already disrupted or paused due to COVID.

Especially when that wildlife research has been framed as "relief efforts" by PREDICT-funded orgs before.

Especially when in the press packet with this announcement, the program seems to claim - as they have in the past, repeatedly - to have discovered "novel" viruses known since the 1960s and 1970s.

Practical note:

It seems like the goal is to continue research with the 50,000+ samples already collected, which is better than funding fieldwork.

But that also delays the scheduled release of that dataset to other researchers, and maybe might reset that clock entirely?

Another important (missing) nuance: when I say models can't work with most of the wildlife virus sequence data we have, there's a big exception: this paper from Simon Babayan and team, which I think shows where the next 10 years of work might take us

science.sciencemag.org/content/362/64…

The Babayan approach goes the other direction:

Rather than predicting zoonotic risk of a wildlife virus, using machine learning to reconstruct where a virus comes from in wildlife.

But this kind of approach is going to grow and diversify.

For that to happen, the broader community of scientists - especially junior scientists around the world - need access to the decade worth of data that publicly funded USAID programs have gathered.

Last thing: modelling isn't the only way we know if a virus has zoonotic potential.

Lots of folks have made this point already but, this 2015 study (among many others) showed that SARS-like viruses in bats could probably emerge someday in humans

nature.com/articles/nm.39…

This kind of study involves making a chimera between the bat virus's spike protein and a SARS-CoV virus.

It's difficult and controversial work, with some red tape. We can't do it for all 50,000 mammal viruses

nature.com/articles/d4158…

We also know to do that work - to go looking for this specific bat virus, to build a chimera, to run this experiment - specifically *because the SARS outbreak happened.* We're still not ready for a true Disease X, something we never knew was coming (beyond the certainty it is).

Wildlife research might reduce spillover, but we'll never get it to zero.

There's no substitute for strengthening healthcare systems, here and worldwide.

That's how we prevent the next pandemic.