DSAIDE - Dynamical Systems Approach to Infectious Disease Epidemiology
A collection of Shiny/R Apps to explore and simulate the population dynamics of infectious diseases.
Written and maintained by Andreas Handel, with contributions from others.
For this module, we will explore a stochastic SIR-type model with 2 different pathogen strains, wild-type and a drug resistant mutant in the presence of drug treatment. Read about the model in the “Model” tab. Then do the tasks described in the “What to do” tab.
This model tracks susceptibles, wild-type infected untreated, wild-type infected treated, drug resistant infected and recovered hosts. The following compartments are included:
The included processes/mechanisms are the following:
The flow diagram for the model implemented in this app is:
Flow diagram for this model.
Note that this model is not an ordinary differential equation model. It is instead its stochastic equivalent. We can specify the model by writing down every possible transition/event/reaction that can occur and their propensities (the propensity multiplied with the time step gives the probability that a given event/transition occurs). For our model these are the following:
Event type | Transitions | Propensity |
---|---|---|
S turn into Iu | S => S-1, Iu => Iu+1 | (1-f) * (bu * (1-cu) * Iu + bt * (1-ct) * It) * S |
S turn into It | S => S-1, It => It+1 | f * (bu * (1-cu) * Iu + bt * (1-ct) * It) * S |
S turn into Ir | S => S-1, Ir => Ir+1 | (bu * cu * Iu + bt * ct * It + br * Ir) * S |
Recovery of Iu | Iu => Iu-1, R => R+1 | gu * Iu |
Recovery of It | It => It-1, R => R+1 | gt * It |
Recovery of Ir | Ir => Ir-1, R => R+1 | gr * Ir |
All parameters described below and in the model are assumed to be in units of (inverse) days
simulate_evolution.R
. You can call this function directly, without going through the shiny app. Check the help file for the function for more information.?DSAIDE
at the R console.Handel, Andreas, Ira M Longini Jr, and Rustom Antia. 2009. “Antiviral Resistance and the Control of Pandemic Influenza: The Roles of Stochasticity, Evolution and Model Details.” J Theor Biol 256 (1). Department of Biology, Emory University, Atlanta, GA 30322, USA. andreas.handel@gmail.com: 117–25. doi:10.1016/j.jtbi.2008.09.021.