This is a collection of programs, scripts and descriptions to produce results in various publications. You can get the publications from the publication section of this webpage. Some of the code is relatively well documented, some isn’t. Feel free to use and modify the programs any way you want. If you need help understanding certain programs, I will try to help. But my time is limited, and for a lot of the old programs, I might not even remember anymore what exactly I did. That’s especially true for anything Matlab or Fortran since I haven’t used either in years.
TB Cough duration analysis - A zip file containing various supplementary R scripts and codebooks for a superspreading/supercoughing study in TB patients. The manuscript describing this work is currently under review.
TB-MAC UGA Model - A file containing the R code and description of the model that was used by us to produce the results described in a multi-model project on TB intervention analysis. The results are described in Houben et al. (2016) Lancet Global Health.
TB Persistence - A collection of R scripts to run the simulations described in our 2014 PLoS One publication “Modeling the potential impact of host population survival on the evolution of M. tuberculosis latency.”
Gapjunction ABM - An agent-based simulation, written for the freely available Netlogo platform. The code simulates gap junction mediated antigen transport during the local spread of virus and clearance by CTL. The simulation is described and used in Handel et al. 2009 JRSI.
Bacteria evolution simulation - A Matlab program that simulates the evolution of a bacterial population in repeated exponential growth and serial dilution cycles. A version of this code was used in Handel and Rozen 2009 BMC Evo Bio. Note that this uses the lightspeed collection of Matlab functions.
Compensatory Mutations - (sparsely documented) Matlab and Fortran programs to simulate the evolution and spread of drug resistance through compensatory mutations. Details about the mathematical model can be found in Handel et al (2006) PLoS Comp Bio.
I use the materials in this section for several of the classes and workshops I teach. You are of course free to use them independently from any of my courses.
DSAIDE - Dynamical Systems Approaches to Infectious Disease Epidemiology. This is an R package containing multiple Infectious Disease Epidemiology models that the user/student can run through a graphical user interface.
DSAIRM - Dynamical Systems Approach to Immune Response Modeling. This R package consists of a set of simulations (refered to here as apps) that teach within-host infection dynamics and immune response modeling from a dynamical system perspective. By manipulating the models through a graphical (Shiny) user interface and working through the provided instructions, you can learn about some important concepts of within-host and immmune response modeling.
YaRI = Yet another R Introduction - I always wanted to give something a name like that! This is a short introduction to R that I compiled and used for various courses I teach that are based on R. I wrote the YaRI tutorial several years ago. Since then, a lot of good R tutorials and teaching resources have become available. At this point, I don’t use YaRI much in my teaching anymore. The 2 I currently use and recommend are Datacamp and the Swirl R Package. Datacamp is more polished, feature rich and overall nicer, but requires you to pay a monthly fee to get full access (some content is free). Swirl is completely free. For further links to R (and other) resources, see the compilation of github lists mentioned below.
Lists with further resources, pertaining to learning R, Data Analysis, and other topics. - I maintain a compilation of links and bits of information related to my research and teaching on Github. Mainly as a quick look-up and reminder for myself, though others might find it useful too.
This section is mainly intended for potential group members so they know what they might be getting themselves into. If others profit from some of the information, even better.
Our current programming environment of choice is the free and Open Source software R. We use it for almost all of our projects. While not required, using R through R Studio makes working in R more user/beginner friendly. R Studio is also free. Almost all of our current writing is also done in the R ecosystem using Rmarkdown/bookdown/blogdown, etc.
In the past, we have used Netlogo, a free agent based simulation platform, for both teaching and research. It is fairly easy to program, has lots of examples and is quite flexible. The main disadvantage is that it’s not that fast.
Sometimes we need to do a bit of analytics. While Mathematica and Maple are the two main programs for analytical calculations, they are expensive. We have found that for our purpose, the freely available Maxima suffices.
If you need to regularly synchronzie files between different machines, Dropbox, is very useful. Dropbox even works nicely with large files, such as encrypted VeraCrypt containers. For more structured work and syncing between computers and users, Github is our preferred mode of work.
If you can get people to send you their data, great! But often, that fails, even if the data is already published. A great tool to extract data from figures is the free program Engauge Digitizer. The program Data Thief provides similar functionality.