Simulation-Based Inference for Epidemiological Dynamics
Meeting Times:
- Monday, July 29, 8:30 AM – 5:00 PM
- Tuesday July 30, 8:30 AM – 5:00 PM
- Wednesday July 31, 8:30 AM – 12:00 PM
Classroom: Randall Rollins Building (RR 200)
Module Summary:
This module introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. The course will 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 will cover sequential Monte Carlo, iterated filtering, and model criticism techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference.
Prerequisites:
Students are expected to have a working knowledge of the R computing environment. Programming will be in R. Students new to R should complete an extensive tutorial before the module. This module assumes knowledge of probability and inference covered in an introductory statistical course.
Module Content:
- Introduction: What is “Simulation-based Inference for Epidemiological Dynamics”? POMPs and pomp
- Simulation of stochastic dynamic models
- Likelihood for POMPs: theory and practice
- Iterated filtering and estimation: theory and practice
- Case study: Measles, recurrent epidemics, covariates.
- Case study: Polio.
- Case study: Ebola.
Instructors
Spencer Fox
Assistant Professor, Department of Epidemiology and Biostatistics, University of Georgia
Spencer J. Fox is an Assistant Professor at the University of Georgia in Athens. He is jointly appointed in the Department of Epidemiology and Biostatistics and the Institute of Bioinformatics. He was formerly supervised by Dr. Lauren Ancel Meyers in the Department of Integrative Biology at the University of Texas at Austin and was the Associate Director of the UT COVID-19 Modeling Consortium.
Qianying Lin, PhD
Postdoctoral Researcher, Theoretical Biology and Biophysics, Los Alamos National Lab
Dr. Lin is a postdoctoral researcher in Theoretical Biology and Biophysics (T-6) at Los Alamos National Laboratory (LANL). She applies statistical methods and builds mathematical models to explore the growing patterns, disease characteristics, and transmission mechanisms of infectious diseases, including Human Influenza, HIV, Middle East Respiratory Syndrome (MERS), Ebola, Tuberculosis, COVID-19, etc. These studies help answer urgent and key questions on known or novel infectious diseases from the public and propose controlling and prevention strategies.
Required Software:
- R
Recommended Reading:
