Introduction to Mathematical Modeling in Public Health

Undergraduate / Graduate

Modeling is a simplified representation of reality that aims to capture essential features of a real-life object or process. Mathematical modeling in public health encompasses a wide array of methodologies offering a powerful toolkit to approach questions that would otherwise be extremely difficult or impossible to answer. This course will introduce students to the conceptual framework of mechanistic modeling and cover the most widely used mathematical modeling approaches in public health, including Markov models, queuing models, models of contagion, and a brief overview of network models and systems dynamic models. The course will explore differences, trade-offs and applications of discrete and continuous time models, deterministic and stochastic models, and compartmental and agent-based models, and will cover the basics of computational techniques used to calibrate models to data. The course will combine lectures and interactive computer / discussion sessions aimed to provide students with an opportunity to practice quantitative techniques covered in lectures and apply these methods to real-world problems.