It’s Time for a National Pandemic Prediction Agency

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The big idea that might save the world from the next catastrophic pandemic isn’t totally buried in the Biden administration’s Covid-19 strategy, but it isn’t exactly above the fold, either. After a flick in the Executive Summary, you’ll have to scroll quite a ways down—to page 115 of the 200-page plan—to find it: “To improve the United States’ preparedness, the Administration will work to secure funding and Congressional support to establish an integrated, National Center for Epidemic Forecasting and Outbreak Analytics to modernize global early warning and trigger systems to prevent, detect, and respond to biological threats.”

That’s it—federal PreCrime for pandemics. Precognitive epidemiology. Make up whatever sci-fi words for it you want; the fact is, one thing the Covid-19 pandemic proved is that pandemics can happen, and certainly will again. Building a place to develop the sophisticated models and simulations that can give a hint of when and where an outbreak will hit, and give guidance on how to stop it … well, that sounds like a pretty good idea.

That notion has been kicking around in wonk circles since the years after the anthrax attacks of 2001, and it comes back up with every big disease outbreak. Two longtime advocates, epidemiologist Caitlin Rivers of the Johns Hopkins Center for Health Security and Dylan George, a vice president at the intelligence agency-affiliated venture capital firm In-Q-Tel, laid it out most recently and in more detail in an article in Foreign Affairs. Think of it, they say, like a National Weather Service, but for predicting and studying pandemics and disease outbreaks rather than hurricanes and tornadoes. It’d combine data gathering capabilities with a centralized approach to the kinds of epidemiological and statistical models that featured so heavily in the first months of the Covid-19 pandemic.

The US climate and weather infrastructure combines data from buoys on the ocean, readings from barometers and thermometers everywhere, and satellite images, using predictive engines to generate analyses and simulations on everything from how climate change is making hurricanes worse to where cargo ships should go to whether you should carry an umbrella. So, similarly, an outbreak analysis center might combine genomic surveillance and public health data with, say, notes on mosquito and bat populations, to point to where the next outbreaks might break out. “We have public health emergencies all the time, even more than people realize,” Rivers tells me. Before Covid-19, there was Zika, Ebola, H1N1, H5N1, SARS, anthrax—not to mention seasonal influenza, or longstanding global threats like tuberculosis. “These crises just feel like they’re continuous, and every time, there’s a need for this analytics capability. But it’s usually just modelers working in academia who volunteer,” she continues.

That’s no way to run a country in an emergency—especially when resources to deal with public health crises come from the federal government but the policies and on-the-ground deployments happen at the state and local levels. “When you are trying to incorporate people with a range of different skills or perspectives in the middle of a crisis, who may not have experience working at the speed of an outbreak or sitting with decisionmakers, it’s hard to cobble that together,” Rivers says.

To be clear, she’s been saying that. About a decade ago, she and George were on a task force set up at the Office of Science and Technology Policy to study pandemic predictive capabilities. It looked like one of the problems with the government’s handling of the H1N1 pandemic had been a push-pull in the advice that epidemiologists were giving to responders—dueling models. George says that, at the time, the Centers for Disease Control and Prevention, the heart of the US federal public health infrastructure, didn’t really have the capability to evaluate which models were the right ones at the right moment. And there wasn’t enough of a standing capability to build the best models from scratch. “When a hurricane comes barreling onto the East Coast, we don’t randomly ask modelers at academic institutions in the US, ‘Hey, could you drop what you’re doing and model where this hurricane is going to hit?’ There’s been this progressive investment in people, models, systems, and data to improve forecasting skill,” he says. “We are in the early stages of infectious disease and pandemic forecasting. I’m confident we can get much better at it if we do a similar investment.”

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