New results from control theory could be used to intervene in future epidemics. getty

When an outbreak of an infectious disease begins to spread, policymakers face an unenviable challenge. They must decide when to intervene and how forcefully, knowing that their choices carry both public health and economic consequences, typically in tension. Act too soon, and they may impose unnecessary costs on society. Act too late, and the outbreak can spiral out of control. Moreover, they must make these decisions using data that are often delayed, incomplete, and riddled with uncertainty.

A new algorithm , invented by computational epidemiologists Sandor Beregi and Kris Parag and published last week in the journal PLoS Computational Biology , offers an interesting new framework for tackling t

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