Aaron A. King, Ph.D.

Nelson G. Hairston Collegiate Professor of Ecology, Evolutionary Biology,
Complex Systems, and Mathematics, University of Michigan
External Professor, Santa Fe Institute
Fellow of the American Association for the Advancement of Science

Simulation-based Inference for Epidemiological Dynamics.

A short course taught with Prof. Edward Ionides as part of the Summer Institute in Statistics and Modeling in Infectious Diseases at the University of Washington in Seattle.

This short course introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. We explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course covers sequential Monte Carlo and synthetic likelihood techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference. Knowledge of the material in the module on Probability and Statistical Inferenceis assumed. Students new to R should complete a tutorial before the module.

Course objectives

The course materials can be found here.

© 2024 Aaron A. King
3038 Biological Sciences Building
1105 North University Avenue
Ann Arbor MI 48109-1085 USA