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Turning Assumptions into Knowledge with Mathematical Models

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