Turning Assumptions into Knowledge with Mathematical Models

Webinar | April 30th, 2020

Mathematical models are an important part of the epidemiologic toolkit, especially during rapidly evolving health crises where data availability is limited. Valid inference about population-level outcomes can be obtained from mathematical models but only under strong assumptions, many of which are often not explicitly described. Dr. Eleanor Murray, Assistant Professor of Epidemiology at BUSPH, explains the basics of mathematical modeling using individual-level or agent-based simulation models, and explains the assumptions required to learn about causal effects from these models.

Recommended Resources:

Causal Inference: What If by Miguel Hernan and Jamie Robins available for free online

An Introduction to Infectious Disease Modelling by Emilia Vynnycky and Richard White

Meet the Instructor

Eleanor Murray, ScD

is an Assistant Professor of Epidemiology at BUSPH. Her research is on causal inference methodology for improving evidence-based decision-making by patients, clinicians, and policy makers. She uses novel statistical methods to answer comparative effectiveness questions for complex and time-varying treatments using observational data and randomized trials when available, and individual-level simulation modeling when insufficient data exist in the time frame required for decision-making. Dr. Murray is an Associate Editor for Social Media at the American Journal of Epidemiology, and can be reached on Twitter at @epiellie.