SISMID Module 8 2022

MCMC I for Infectious Diseases

View the Project on GitHub

Course Description

This module is an introduction to Markov chain Monte Carlo (MCMC) methods with some simple applications in infectious disease studies. The course includes an introduction to Bayesian statistics, Monte Carlo, MCMC, some background theory, and convergence diagnostics. Algorithms include Gibbs sampling, Metropolis-Hastings and their combinations. Familiarity with the R statistical package or other computing language would be helpful.

Logistics

Time: July 18-19, 8:00 am - 2:30 pm, July 20, 8:00 am - 11:00 am :date:

Place: https://uci.zoom.us/j/99078849451 (check Slack for passcode) :desktop_computer:

Instructors: Kari Auranen, M. Elizabeth Halloran, Vladimir Minin :man_scientist: :woman_scientist: :man_scientist:

Schedule:: mcmc_time_table.pdf :alarm_clock:

R tutorials: R for Beginners, Swirl (Learn R, in R), SISMID/SISG Introduction to R :school:

Course materials :open_book:

Course Introduction: intro_video :movie_camera:

Introduction to Bayesian inference and Gibbs sampling :telescope:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
slides_bayesintro.pdf PracticalBayes.pdf bayes_intro_lecture bayesintro2022.R
  PracticalGibbs.pdf bayes_intro_lecture_live chainGibbs_reduced.R
    chain-binomial Gibbs sampler chainGibbs.R
    chain_binomial_lecture_live  
    chain_binomial_lab_live  

Classical Monte Carlo and Markov chain theory :diamonds: :spades: :hearts: :clubs: :game_die:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
mc_mcmc2022.pdf (pages 8-14) import-sampling-lab.pdf monte_carlo_lecture_live imp_sampl_reduced.R
    imp_sampl_lecture_lab_live imp_sampl.R
    imp_sampl_lab_live1  
    imp_sampl_lab_live2  
    markov_theory_lecture_live1  
  ehrenfest_diff-lab.pdf ehrenfest_live ehrenfest_diff_reduced.R
    ehrenfest_ergodic_live ehrenfest_diff.R

Metropolis-Hastings algorithm :frog:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
mc_mcmc2022.pdf (pages 14-18) mh-lab.pdf mh_lab_live norm_mh_reduced.R
      norm_mh.R
    infect_time_gibbs_live infect_time_reduced.R
      infect_time.R

Gibbs sampling and chain binomial model :chains:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
mc_mcmc2022.pdf (pages 18-20) PracticalGibbs.pdf   chainGibbs.R

Metropolis-Hastings and Gibbs combined :octopus:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
mc_mcmc2022.pdf (pages 20-21) betabin-lab.pdf beta_bin_lab_live beta_bin_reduced.R
      beta_bin.R

Chain binomial model revisited :chains:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
chain_bin_revisited.pdf chain-bin-revisit-lab.pdf model_check_live checkmodel_reduced.R
      checkmodel.R
      chain_hierarchical_reduced.R
      chain_hierarchical.R
      check_hierarchical.R

General epidemic (SIR) model :smile: :sneezing_face: :smile:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
sir_lecture.pdf sir-lab.pdf sir_live SIRaugmentation_reduced.R
      SIRaugmentation.R
       

Monte Carlo error and MCMC diagnostics :woman_mechanic:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
mc_mcmc2022.pdf (pages 21-22) diagnostics-lab.pdf diagnostics_live diagnostics_reduced.R
      diagnostics.R

SIS model :smile: :sneezing_face: :smile: :sneezing_face: :smile: :sneezing_face: :smile:

Slides/Notes :green_book: Practicals :microscope: Videos :movie_camera: Code :computer:
sis_lecture.pdf sis-lab.pdf   simulateSIS_N.R
      MH_SIS.R
       

Useful Books: 📘

Other Resources: 🗒️