From the localized emergence of COVID-19 followed by its spread to a global pandemic, policy makers have been faced with numerous critical decisions regarding COVID-19 prediction, prevention, and response. This talk describes model-based analyses that address questions in the following areas: Can cell phone mobility data be useful in predicting COVID-19 outbreaks? To what extent can face masks prevent the spread of COVID-19? How effective are various measures for limiting COVID-19 spread in incarcerated populations? How should limited COVID-19 vaccines be allocated among different population groups? Can smartwatches provide information on COVID-19 vaccine side effects not captured by patient self-reports? We conclude with thoughts on challenges and opportunities for applying analytics to improve COVID-19 decision making now and in the future.
Margaret L. Brandeau is Coleman F. Fung Professor of Engineering and Professor of Health Policy (by Courtesy) at Stanford University. Her research focuses on the development of applied mathematical and economic models to support health policy decisions. Her recent work has examined HIV and drug abuse prevention and treatment programs, programs to control the opioid epidemic, and COVID-19 response strategies. She is an INFORMS Fellow. From INFORMS, she has received the Philip McCord Morse Lectureship Award, the President’s Award, the Pierskalla Prize (twice), and the Award for the Advancement of Women in Operations Research and the Management Sciences.