Itâs Time for a National Pandemic Prediction Agency
The big idea that might save the world from the next catastrophic pandemic isnât totally buried in the Biden administrationâs Covid-19 strategy, but it isnât exactly above the fold, either. After a flick in the Executive Summary, youâll have to scroll quite a ways downâto page 115 of the 200-page planâto find it: âTo improve the United Statesâ 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.â
Thatâs itâfederal 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 â¦ 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 Policy 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 Policy. Think of it, they say, like a National Weather Service, but for predicting and studying pandemics and disease outbreaks rather than hurricanes and tornadoes. Itâd 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. âWe have public health emergencies all the time, even more than people realize,â Rivers tells me. Before Covid-19, there was Zika, Ebola, H1N1, H5N1, SARS, anthraxânot to mention seasonal influenza, or longstanding global threats like tuberculosis. âThese crises just feel like theyâre continuous, and every time, thereâs a need for this analytics capability. But itâs usually just modelers working in academia who volunteer,â she continues.
Thatâs no way to run a country in an emergencyâespecially 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. âWhen 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, itâs hard to cobble that together,â Rivers says.
To be clear, sheâs 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 governmentâs handling of the H1N1 pandemic had been a push-pull in the advice that epidemiologists were giving to respondersâdueling models. George says that, at the time, the Centers for Disease Control and Prevention, the heart of the US federal public health infrastructure, didnât 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. âWhen a hurricane comes barreling onto the East Coast, we donât randomly ask modelers at academic institutions in the US, âHey, could you drop what youâre doing and model where this hurricane is going to hit?â Thereâs been this progressive investment in people, models, systems, and data to improve forecasting skill,â he says. âWe are in the early stages of infectious disease and pandemic forecasting. Iâm confident we can get much better at it if we do a similar investment.â