**[In these modules, students will become familiar with basic computational methods for stochastic modeling. We will cover stochastic modeling of epidemics and biological processes using Markov Chain Monte Carlo (MCMC), Stochastic Differential Equations (SDE’s), and Agent Based Models (ABMs, aka Individual Based Models, IBMs). ** **Stochastic methods are very useful for many different things, so we’ll also discuss other applications throughout the course.** **Along the way, we will discuss many other things that are critical to your future success as a researcher. How to do literature searches and build an annotated bibliography, how to organize your work, good coding practices, how to write a good research paper, and how to give a good presentation.** **There is no required textbook for this course. However, I *highly* recommend Modeling Infectious Diseases in Humans and Animals by Keeling and Rohani. It is a great introductory- to medium-level book on modeling methods (including stochastic modeling). Another book that is good, at a medium- to advanced-level, is An Introduction to Stochastic Processes with Applications to Biology, by Linda Allen. NIMBios also has a good web page with information related to stochastic modelling. In addition, a really nice introductory exposition on the topic of stochastic modelling by Priscilla Greenwood and Luis Gordillo can be found here]**

- Good work habits, and requirements for homework
- Literature searches with Google Scholar
- Producing well written manuscripts in a timely fashion
- Giving a good presentation
- Basics of R
- Compartmental modelling in R using the Runge Kutta numerical ODE solution methods in the deSolve library
- Compartmental modelling using calculations done at small time steps using Euler’s method (compartmental modelling without calculus)
- Calculating the dynamic time step when using numerical methods to solve ODE’s, and stochastic modelling
- A somewhat more complicated compartmental model: SIR model with age structure
- Another (marginally) more complicated disease model: SIR model with periodic transmission rate
- Mean, variance, and covariance
- Probability distributions important to modeling in the life and social sciences
- Brief description of the difference between SDE’s, MCMC, and ABM’s, and their appropriate applicability in research.
- Formalism for preparation for stochastic compartmental modelling using MCMC or SDE’s
- Compartmental modelling with Markov Chain Monte Carlo: Part 1
- More on the Poisson, Exponential, and Gamma distributions
- Compartmental modelling with Markov Chain Monte Carlo: Part 2
- Stochastic compartmental modelling with Stochastic Differential Equations
- Aggregating the results of a model simulation into bins of fixed time
- Combining stochastic compartmental modelling methods: MCMC and SDE’s
- Stochasticity due to partial random sampling of population or disease data
- Simple Agent Based epidemic model in R
- Agent based modelling on simple networked populations
- Monte Carlo methods for assessing uncertainty in model estimates due to uncertainties in parameters
- AML612 class project
- Contagion in mass killings
- Linear regression and model selection