Teaching: PHP1855


Infectious Disease Modeling (and Applications)

Official blurb: This course will introduce infectious disease modeling and its applications. Students will learn to build computational models of infectious disease, fit models to data, leverage models to develop predictions, and target public health interventions. We will explore how models were used during the COVID19 pandemic, and are being used to project the health effects of climate change. We will introduce core mathematical underpinnings including dynamical systems and statistical approaches for model fitting.

Back story: Infectious disease modeling is a wonderful and strange methodological tool/field that intersects many different academic disciplines. You'll find modelers lurking in epidemiology departments, ecology departments, mathematics departments, biology departments, the CDC, the NIH, in non-profits and the tech industry.  Modeling work spans studies in fundamental science and applied mathematics, to high-impact policy-focused public health work. To appropriately capture the discipline, this course will have three prongs: 

1) Real-world studies: we'll explore how ID models have been used to make key public health decisions e.g. in the COVID19 pandemic, or to project pathways under climate change. 

2) Mathematical underpinnings: we will use calculus to explain key model properties including R0, transmission, and herd immunity. We'll also discuss were ID models sit in the broader study of nonlinear dynamics systems including bifurcations, higher-order periodicites and chaos. 

3) Computational approach: we will learn how to build and fit ID models in R, including developing models for specific pathogens such as influenza, COVID19 and dengue.