Design, calibration, and optimization of pandemic alert systems

David Morton


During the COVID-19 pandemic, governments worldwide developed staged-alert systems to monitor data streams and trigger changes in intervention policies. However, many tracked unreliable data indicators, used heuristic policy triggers, failed to articulate measurable goals, and were implemented and communicated inconsistently. Beginning in April 2020, we worked closely with local officials in Austin, Texas to develop and maintain the COVID-19 alert system that guided public communications and policy decisions.

Over a two-year period, the system was instrumental in preventing overwhelming healthcare surges, minimizing socioeconomic disruption, and contributing to Austin’s significantly lower COVID-19 mortality rate than comparable cities across the US. In this talk, we will describe a data-driven modeling framework, and stochastic optimization model, for designing pathogen alert systems that can ensure consistent situational awareness, provide policy guideposts that reduce uncertainty and decision complexity, and enhance public trust and policy adherence. 

David Morton


Dave Morton is the Walter P. Murphy Professor of Industrial Engineering and Management Sciences (IEMS) at Northwestern University. His research interests include stochastic and large-scale optimization with applications in public health, security, and energy systems. Prior to joining Northwestern, he was on the faculty at the University of Texas at Austin, worked as a Fulbright Research Scholar at Charles University in Prague, and was a postdoctoral fellow at the Naval Postgraduate School. He is a Fellow of INFORMS.