In the changing public health environment shaped by COVID-19, federal health authorities continue to revisit traditional epidemiological approaches. Data collection and analysis systems that performed effectively for decades may no longer prove adequate to address a host of emerging issues, from the need to cope with growing amounts of information to increasing demands for faster public health response on a national scale.

But with the rapid evolution of artificial intelligence (AI) in the health sector, agencies can now automate, simplify, and transform the predictive modeling capabilities that enable large-scale disease surveillance efforts. As the success of Booz Allen’s COVID-19 Safe Return Simulator demonstrates, what AI provides federal health agencies is an unprecedented ability to extract insights from disparate data streams—and to do so with an efficiency and accuracy that protect health and save lives.