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The course combines in-class instruction with small-group exercises. Case studies will be introduced on the first day and will be completed during the week in small groups with a faculty supervisor.


  • Turning mechanistic ideas into models: deterministic, stochastic, and agent-based models
  • Examples and applications in infectious disease epidemiology, surveys/surveillance, health policy, and healthcare operations
  • Introduction to computational tools for modeling in public health


  • Chronic disease/physiological models in individuals
  • Infectious disease models in individuals and populations
  • Computational tools for modeling with ordinary differential equations


  • Cost-effectiveness analysis
  • Uncertainty analysis
  • Optimal decision making in public health


  • Fitting and interpreting models
  • Statistical inference and model calibration
  • Inferences about causality
  • Predictions about the future dynamics of a system
  • Sensitivity analysis


Presentation of case studies. Topics may include:

  • Identifying priority groups for COVID-19 vaccination
  • Risk of cancer progression and cost-effectiveness of treatment
  • Cost-effectiveness of HIV treatment strategies
  • Evaluation of typhoid conjugate vaccine delivery strategies
  • ICU prioritization during the COVID-19 pandemic
  • Optimal SARS-CoV-2 testing strategies for school opening