Clinic on Meaningful Modeling of Epidemiological Data

The eighth annual MMED clinic ~ May 29 - June 9, 2017 ~ Muizenberg, Cape Town, South Africa

  1. MMED 2017
  2. Preparation

Preparation for the Clinic

1. Poster preparation

Prepare a poster presentation to share your research.

  • Poster preparation guidelines are available here.
  • Be sure to submit your poster by the May 19 deadline, if you would like us to print it for you.
  • See this page for poster session assignments.

2. Software installation

If you plan to bring a laptop to use during the Clinic, please install the following programs prior to arrival:

  • Excel (or a compatible spreadsheet program)
  • Git - version control software
    • Note that recent versions of MacOS come with Git installed, so you may not need to install this program.
  • Git Bash (Recommended for Windows users only) - command line access to Git on Windows
  • R - a statistical programming language (download links for Windows, Linux, and MacOS)
  • R Studio - a user interface for R that will be needed for computer exercises (download link)

Please let us know if you have trouble installing any of the above software!

Note: Even if you are not bringing a laptop to the Clinic, you will need access to a computer with both R and R Studio installed to prepare for the Clinic.

3. Introductory tutorials

Introduction to R and R Studio

When you have successfully installed both R and R Studio, please work through these tutorials:

If you are unfamiliar with or rusty on your understanding of the Binomial Distribution, you may also want to work through the introductory Binomial Distribution tutorial.

Tip: To download all of the tutorials at once into a single directory on your computer, you can clone the ICI3D R tutorials repository. You can get started quickly by opening the RTutorials.Rproj file within that directory.

Introduction to Git

In addition, please complete all 4 lessons in this free, browser-based tutorial from Codecademy. You will learn the foundations of how to work with git to improve your workflow and collaborate on projects that involve code.

  • Basic Git Workflow teaches you how to create a repository for a project and keep track of changes you make to files.
  • How to Backtrack in Git shows you how to correct mistakes in coding projects or revert to an older version of your code if you decide to change directions.
  • Git Branching introduces a workflow that allows you to have multiple working versions of your code, how to bring them back together, and what to do if your versions have conflicting changes.
  • Git Teamwork teaches you how to collaborate on a project using git.

If you are already comfortable using git, you can skip this activity, but if you’re new to git or your skills are rusty, please take the time to work through all four lessons.

For all participants

  • Heesterbeek, JAP, RM Anderson, V Andreasen, S Bansal, D De Angelis, C Dye, KTD Eames, WJ Edmunds, SDW Frost, S Funk, TD Hollingsworth, T House, V Isham, P Klepac, J Lessler, JO Lloyd-Smith, CJE Metcalf, D Mollison, L Pellis, JRC Pulliam, MG Roberts, C Viboud, and the Isaac Newton Institute IDD Collaboration. (2015) Modeling infectious disease dynamics in the complex landscape of global health. Science 347(6227): aaa4339. doi:10.1126/science.aaa4339

  • We have put together an introductory overview, which includes excerpts from the below papers.

    • Bellan, SE, JRC Pulliam, JC Scott, J Dushoff and the MMED Organizing Committee. How to make epidemiological training infectious. PLoS Biology 2012; 10: e1001295.
    • Susser, M and E Susser. Choosing a future for epidemiology: I. Eras and paradigms. Am J Public Health 1996; 86: 668–73.
    • Koopman, JS and JW Lynch. Individual causal models and population system models in epidemiology. Am J Public Health 1999; 89: 1170–4.
    • Brauer, F. Mathematical epidemiology is not an oxymoron. BMC Public Health 2009; 9: S2.

Especially for those new to dynamical modeling