MMED 2018 Lectures and Lab Summaries

Lecture Slides

Plenary Sessions from Week 1

• Public Health, Epidemiology, and Models (Eduard Grebe) - Slides
• Introduction to Thinking About Data I (Brian Williams) - Slides
  • Video of the 2017 lecture given by John Hargrove - Video, Slides
• Introduction to dynamic modeling of infectious diseases (Juliet Pulliam) - Slides
  • Video of the 2017 lecture given by Steve Bellan - Video, Slides
• (Hidden) assumptions of simple ODE models (Juliet Pulliam) - Slides
• Consequences of heterogeneity, and modeling options (Jonathan Dushoff) - Slides - Handouts
• Introduction to models and data: HIV in Harare (John Hargrove) - Slides
• Introduction to statistical philosophy (Jonathan Dushoff) - Slides, Handouts
  • Video of the 2017 lecture given by Jonathan Dushoff - Video, Slides
• Introduction to Likelihood (Eva Ujeneza) - Slides
  • Video of the 2017 lecture given by Steve Bellan - Video, Slides
• Likelihood fitting and dynamic models, Part 1: Dynamic Model Fitting and Inference Robustness (Juliet Pulliam) - Slides

Track A Sessions from Week 1

• Introduction to infectious disease data (Faikah Bruce) - Slides
• Introduction to Thinking About Data II (John Hargrove) - Slides
• Study Design and Analysis in Epidemiology: Where does modeling fit? (Faikah Bruce) - Slides
• Study Design and Analysis in Epidemiology II: RCT’s (Carl Pearson) - Slides

Track B Sessions from Week 1

• Foundations of dynamic modeling (Jonathan Dushoff) - Slides - Handouts
  • Video of the 2017 session led by Jonathan Dushoff - Video, Slides
• Basic stochastic simulation models (Rebecca Borchering) - Slides
• Creating a model world to address a research question (Juliet Pulliam) - Slides, Assignment and examples
  • Video of the 2017 session led by Juliet Pulliam - Video

Plenary Sessions from Week 2

• Doing Science (Brian Williams) - Slides
• Likelihood fitting and dynamic models II (Carl Pearson) - Slides - Handouts
• Introduction to Monte Carlo Markov Chains (MCMC) (Carl Pearson) - Slides (180mb read-only powerpoint slide set with embedded movies)
• Introduction to Data Wrangling (Jonathan Dushoff) - Slides
• Model Assessment (Jonathan Dushoff) - Slides, Handouts
• Stochastic Modeling IIA (Jonathan Dushoff) - Slides, Handouts

AIMS Public Lectures

• Math and Rabies Control (Jonathan Dushoff) - Slides

Lab Summary Slides

Tutorials

• Tutorial 4 Summary: Visualizing Infectious Disease Data in R (Reshma Kassanjee) - Slides
• Tutorial 5 Summary: Data Wrangling (Thumbi Mwangi) - Slides to be added

Labs

• Lab 1 Summary: ODE models in R (Zinhle Mthombothi) - Slides
• Lab 2 Summary: Consequences of heterogeneity (Roger Ying) - Slides
• Lab 3 Summary: Study Design in Epidemiology (Eva Ujeneza) - Slides
• Lab 4 Summary: Study Design for Clinical Trials (Faikah Bruce) - Slides
• Lab 5 Summary: Introduction to Likelihood Lab (Carl Pearson) - Slides to be added
• Lab 6 Summary: MLE fitting of a dynamic model to prevalence data (Juliet Pulliam) - Slides
• Lab 7 Summary: MCMC fitting I (Eduard Grebe) - Slides

Exercises

• Exercise 1 Summary: Basic stochastic simulation models (Rebecca Borchering) - Slides
• HIV in Harare (Additional Info): Distributed Delay Models of Survival (Boxcar Models) - Slides

Code from MMED 2018 Participatory Coding Sessions

• Introduction to model implementation (Carl Pearson) - Live coding example
• Participatory coding of a dynamical model (Carl Pearson) - Code from session
  • Video of the 2017 session led by Steve Bellan - Video
• Participatory Coding (Sampling Var & Study Design) 2018 - Is aidamycin superior to cotrim for treating malaria (under construction)?
  • Single trial
  • Many trials
• Sampling Var & Study Design additional example 2018 - Does vaccinating schoolchildren protect elders from clinical influenza (cluster RCT)?
  • Single trial
  • Many trials
  • A range of protection assumptions
  • Worrying about village-level variance
  • Participatory coding of a stochastic model (Chain binomial SEIV) (Carl Pearson) - Code from session