It will start with a flash of light brighter than any words of any human language can describe. When the bomb hits, its thermal radiation, released in just 300 hundred-millionths of a second, will heat up the air over K Street to about 18 million degrees Fahrenheit. It will be so bright that it will bleach out the photochemicals in the retinas of anyone looking at it, causing people as far away as Bethesda and Andrews Air Force Base to go instantly, if temporarily, blind. In a second, thousands of car accidents will pile up on every road and highway in a 15-mile radius around the city, making many impassable.

That’s what scientists know for sure about what would happen if Washington, DC, were hit by a nuke. But few know what the people—those who don’t die in the blast or the immediate fallout—will do. Will they riot? Flee? Panic? Chris Barrett, though, he knows.

When the computer scientist began his career at Los Alamos National Laboratory, the birthplace of the atomic bomb, the Cold War was trudging into its fifth decade. It was 1987, still four years before the collapse of the Soviet Union. Researchers had made projections of the blast radius and fallout blooms that would result from a 10-kiloton bomb landing in the nation’s capital, but they mostly calculated the immediate death toll. They weren’t used for much in the way of planning for rescue and recovery, because back then, the most likely scenario was mutually assured destruction.

But in the decades since, the world has changed. Nuclear threats come not from world powers but from rogue nation states and terrorist organizations. The US now has a $40 billion missile interception system; total annihilation is not presupposed.

The science of prediction has changed a lot, too. Now, researchers like Barrett, who directs the Biocomplexity Institute of Virginia Tech, have access to an unprecedented level of data from more than 40 different sources, including smartphones, satellites, remote sensors, and census surveys. They can use it to model synthetic populations of the whole city of DC—and make these unfortunate, imaginary people experience a hypothetical blast over and over again.

That knowledge isn’t simply theoretical: The Department of Defense is using Barrett’s simulations—projecting the behavior of survivors in the 36 hours post-disaster—to form emergency response strategies they hope will make the best of the worst possible situation.

You can think of Barrett’s system as a series of virtualized representation layers. On the bottom is a series of datasets that describe the physical landscape of DC—buildings, roads, the electrical grid, water lines, hospital systems. On top of that is dynamic data, like how traffic flows around the city, surges in electrical usage, and telecommunications bandwidth. Then there’s the synthetic human population. The makeup of these e-peeps is determined by census information, mobility surveys, tourism statistics, social media networks, and smartphone data, which is calibrated down to a single city block.

So say you’re a parent in a two-person working household with two kids under the age of 10 living on the corner of First and Adams Streets. The synthetic family that lives at that address inside the simulation may not travel to the actual office or school or daycare buildings that your family visits every day, but somewhere on your block a family of four will do something similar at similar times of day. “They’re not you, they’re not me, they’re people in aggregate,” Barrett says. “But it’s just like the block you live in; same family structures, same activity structures, everything.”