Introduction to MCMC
Online Lab Schedule:
- Monday, July 7, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
- Tuesday July 8, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
Classroom: Virtual
Module Summary:
Bayesian statistical models are frequently applied in scientific research for their flexibility in handling complex systems and sparse data and convenient implementation. This module aims to introduce the basic Bayesian methodolgy for estimating key epidemiological parameters governing transmission dynamics and control of infectious diseases, with an emphasis on hands-on experience in buidling and interpreting models for practical problems with real data.
The course includes a gentle introduction to Bayesian statistics and inferential algorithms based on Markov chain Monte Carlo such as importance sampling, Gibbs sampling and Metropolis-Hastings, in the context of modeling transmisison of infectious diseases ta both the population level and the individual level. Model diagnostics and model selection will be discussed. The module will be supplemented with special topics in missing data and forecasting. The sessions will alternate between lectures and labs.
Prerequisites:
This module assumes undergraduate level of probability and inference covered in an introductory statistical course. Students will learn how to use R to analzye and forecast infectious disease transmission dynamics. Students are expected to have basic knowledge of the R computing environment, e.g., load, manipulate and visualize data. Students new to R or R Studio should complete a tutorial before the module (see example below).
https://www.youtube.com/watch?v=_V8eKsto3Ug&ab_channel=freeCodeCamp.org
https://www.youtube.com/watch?v=yZ0bV2Afkjc&ab_channel=EquitableEquations
https://www.youtube.com/watch?v=ANMuuq502rE&ab_channel=GlobalHealthwithGregMartin
Module Content:
- Brief review of ordinary differential equation models and chain-binomial models for simulating and analyzing infectious disease transmission and control.
- Introduction to Bayesian statistics and MCMC theory
- Sampling algorithms: importance sampling, Gibss sampling, and Metropolis Hastings algorithm.
- Building hierarchical models in Rstan/Rjags (to be determined)
- Model selection and diagnostics
- How to handle missing data
- Forecasting epidemic trajectories
Instructors
Yang Yang, PhD
Professor, Department of Statistics, University of Georgia
Dr. Yang is a professor in the Department of Statistics at University of Georgia. He obtained his PhD in biostatistics from Emory in 2004. His research interest mainly focuses on statistical models for transmission of infectious diseases, evaluation of intervention effectiveness, and optimization of intervention strategies. He is particularly interested in high-dimensional missing data in outbreak analysis and statistical adjustments for a variety of surveillance biases. Recently, he extended his scope of research to agent-based modeling, coupling transmission with ecological modeling for zoonotic pathogens, and methods coupling transmission process with phylodynamics. Dr. Yang has been actively engaged in international collaborations with researchers from Asia, South America, Europe and Africa on emerging diseases including avian influenza, Ebola, Zika, MERS-CoV and SARS-CoV-2.
Mandev Gill, PhD
Assistant Professor, Department of Statistics, University of Georgia
Mandev Gill is an Assistant Professor in the Department of Statistics at the University of Georgia. Before arriving at UGA, he completed his Ph.D. at the University of California, Los Angeles and then worked as a postdoctoral researcher at KU Leuven in Belgium. His research focuses on Bayesian statistical and computational methods for studying infectious disease dynamics.
Required Software:
Recommended Reading:
Primary research and tutorial articles will be provided for additional reading.
