# 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: RCTâ€™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)?
- 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