Stanford Launches Data-Driven Model Evaluating COVID-19 Interventions

Researchers at Stanford University have launched an interactive, data-driven model that evaluates possible outcomes of non-pharmaceutical interventions for COVID-19, including social distancing and quarantine. The team aims to help users understand the benefits of delaying the peak of the epidemic and staying below a fixed healthcare capacity. Rather than trying to map the exact dynamics of a certain location, the model shows possible trajectories under different hypothetical scenarios. The modeling framework allows for different types, intensities, and durations of interventions to be implemented, and shows how these different interventions impact the number of COVID-19 cases and fatalities through time.