Using Mathematical Models to Understand the Effects of Antimicrobial De-escalation in ICUs
Antimicrobial de-escalation is a highly recommended and widely practiced drug use strategy. It is believed to play important roles in reducing the development and transmission of antibiotic resistance, as well as reducing mortality and inappropriate empiric therapies. However, such benefits were not uniformly observed in clinical studies, making it hard to conclude the benefits and trade-offs of de-escalation. In this talk, I will present results from mathematical models on the effects of de-escalation in intensive care units, and show how we link the mathematical results with observations from clinical studies and vice versa. Our study shows that mathematical models could assist the future design of clinical studies in clarifying expectations and outlining data collection and analytical methods.
Bio: Dr. Huo is an Assistant Professor in the Department of Mathematics at University of Miami. She received her Ph.D. in Mathematics from Vanderbilt University under the supervision of Dr. Glenn Webb. She received her postdoctoral training in Toronto, Canada, where she was a member of the Laboratory for Industrial and Applied Mathematics (LIAM) directed by Dr. Jianhong Wu. Dr. Huo works in mathematical biology, with a focus on modeling, analyzing and simulating problems arising from three major biomedical topics: antimicrobial resistance, vector-borne diseases, and emerging infectious diseases such as COVID-19. She is also interested in the analysis, application, and numerical simulation of first order hyperbolic PDE models for physiologically structured population dynamics. Her work is partially supported by the National Science Foundation.