April 2, 2021
The COVID-19 pandemic has highlighted the devastating potential of rapidly spreading infections and the need for mathematical analysis in all aspects of the response. The complex interplay between the virus and human physiology and behavior has confounded attempts to predict and control disease spread. In this talk I will discuss how biological and social factors are integrated with dynamical systems theory to build mathematical models of infectious diseases. We will cover how models have been used during the emerging COVID-19 epidemic to interpret data in real-time, to design effective public health interventions, and to predict future dynamics under different scenarios. I will describe our work to understand how the structure of human contact networks determines patterns of disease spread and control, such as the efficacy of social distancing interventions, the impact of changes in household structure, and the shape of epidemics in cities.
Alison Hill is Assistant Professor of Biomedical Engineering at the Johns Hopkins University and a core faculty member at the Institute for Computational Medicine and in the Infectious Disease Dynamics Group. Her research team develops mathematical models and computational tools to help understand, predict, and treat infectious diseases, with a particular focus on HIV/AIDS, COVID-19, drug resistant infections, bed bug infestations, and anti-viral immune responses. Alison received her BS in Physics from Queen's University (Canada), her PhD in Biophysics and Medical Physics through the Harvard-MIT Division of Health Sciences & Technology, and her MPH from Harvard School of Public Health.