# MMED Lectures and Lab Summaries

## Lecture Slides

### Plenary Sessions from Week 1

- Public Health, Epidemiology, and Models (Jim Scott) - Slides
- Introduction to Thinking About Data I (John Hargrove) - Slides
- Video of the 2017 lecture given by John Hargrove - Video, Slides

- Introduction to dynamic modeling of infectious diseases (Zinhle Mthombothi) - 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
- Introduction to models and data: HIV in Harare (John Hargrove) - To be added
- Introduction to statistical philosophy (Jonathan Dushoff) - To be added
- Video of the 2017 lecture given by Jonathan Dushoff - Video, Slides

- Introduction to Likelihood (Cari van Schalkwyk) - 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 (Thumbi Mwangi)- Slides
- Introduction to Thinking About Data II (Jim Scott) - Slides
- Study Design and Analysis in Epidemiology: Where does modeling fit? (Jim Scott) - To be added
- Study Design and Analysis in Epidemiology II: RCTâ€™s (Alex Welte) - To be added

### Track B Sessions from Week 1

- Foundations of dynamic modeling (Jonathan Dushoff) - Slides
- 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 (Thumbi Mwangi) - Slides
- Video of the 2017 session led by Juliet Pulliam - Video

### Plenary Sessions from Week 2

(links currently go to the 2018 versions)

- 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

## Lab Summary Slides

### Tutorials

- Tutorial 4 Summary: Visualizing Infectious Disease Data in R (Juliet) - - Slides
- Tutorial 5 Summary: Data Wrangling (TBD) - To be added

### Labs

- Lab 1 Summary: ODE models in R (TBD) - To be added
- Lab 2 Summary: Consequences of heterogeneity (TBD) - To be added
- Lab 3 Summary: Study Design in Epidemiology (TBD) - To be added
- Lab 4 Summary: Study Design for Clinical Trials (TBD) - To be added
- Lab 5 Summary: Introduction to Likelihood Lab (To be added) - To be added
- Lab 6 Summary: MLE fitting of a dynamic model to prevalence data (TBD) - To be added
- Lab 7 Summary: MCMC fitting I (TBD) - To be added

### 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 2019 Participatory Coding Sessions

- Introduction to model implementation (Cari van Schalkwyk) - Live coding example
- Participatory coding of a dynamical model (Juliet Pulliam) - Code
- Video of the 2017 session led by Steve Bellan - Video

- Participatory Coding (Sampling Var & Study Design) (Jonathan Dushoff) - Code
- Participatory coding of a stochastic model (Chain binomial SEIV) (TBD) - To be added