Using the package
The following sections describe the main envisioned ways the content in this R package can be used and extended. The idea is that you start at level 1, and then depending on needs and interests, you can decide to move on to the next level.
Level 1: Interactive use through the graphical user interface
The interactive exploration of the models and infectious disease concepts through the graphical user interface is the main intended use of this package. The steps to get there are simple.
Load and run the package
After install, load the package by typing the following into the R console.
You need to load the package every time you start a new R/Rstudio session.
If the package loads correctly, you should receive a short greeting. Now you can open the main menu by typing the following into the R console:
If successful, you should see a graphical menu open in your browser that looks like this:
Main menu of the DSAIDE package.
From the main menu, you can choose the different apps corresponding to different modeling topics and scenarios. Each app has the inputs you can set on the left and the outputs (figures and text) on the right. This is an example of one of the apps:
Screenshot of the input-output elements of one of the apps.
As seen in the figure above, below each app are four tabs with different sections providing information and instruction. The Overview and Further Information tabs provide a brief introduction and pointers to further exploration options, respectively.
The two most important tabs are the Model and What to do tabs. The Model tab gives a detailed description of the model underlying the app, shows the model diagram and equations, and provides any other further information needed to understand what is going on. The following figure shows an example of this for one of the apps.
Screenshot of the Model section of one of the apps.
The What to do tab contains a list of suggested tasks you might want to go through. By doing those tasks, you will learn the topic a specific app is meant to teach. The following figure shows an example for part of one of those What to do sections for one of the apps.
Screenshot of the What to do section of one of the apps.
Once you finished exploring a specific app, you can return to the main menu. Eventually, you can exit back to the R console by clicking the Exit button. You will not need to read or write any code at this level of exploration and learning. If an when you want to learn more, you can proceed to level 2.
Level 2: Directly interacting with the simulation functions
Once you are comfortable interacting with the models and have a good understanding of the concepts covered by the different apps, it is possible, without too much effort, to interact with the simulation models directly. This provides more flexibility at the cost of writing a limited amount of code.
To facilitate direct interaction and modification of the underlying simulations, each app is structured in such a way that the underlying model/simulation is a stand-alone function. For some apps, there are multiple underlying functions involved. You can call any of these functions directly, without going through the graphical interface. The Further Information tab inside each app provides the name of the corresponding underlying function(s) and how to call their help files to get information on their use.
Consider as example the first app, called “ID Dynamics Intro”. The simulator function for this model is called simulate_sir_ode.R. After loading the package (if not already loaded) with
## Loading required package: shiny## Welcome to the DSAIDE package. Type dsaidemenu() to get started.you can learn about the inputs and outputs of the model/function by looking at its documentation
The help file explains that you can run the simulation by specifying initial number of susceptibles and infected, the duration for which the simulation should be run, and the infection and recovery parameters. Each parameter has some default, listed in the help file. Running a simulation model without any specifications runs it with the defaults:
Calling the simulation function execuctes the underlying dynamical model (here a simple 3 compartment ODE model, as described in the “Model” section of the app). Most simulation functions produce and return time-series for the dynamics of each of the variables that are tracked as a list. You can then take the results returned from the simulation function and further process them. For instance you could plot the time-series for susceptible as function of time:
You can overwrite the default settings by providing your own values for specific parameters, as shown in this example:
result <- simulate_sir_ode(S = 2000, b = 0.001, g = 0.5, tfinal = 200)
plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')
Any values that you do not specify will be kept at their defaults.
Note that unless explicitly specified, the models do not have inherent time units. Instead, those depend on the time units you choose for the parameters. It is important to ensure that all quantities (parameters and time settings) have the same time units, e.g. days or months (or the inverse of those units for the rate parameters).
Not all simulator functions return time-series. For instance the function underlying the uncertainty and sensitivity app returns specific outcomes (maximum and final state of variables) for different parameter values. In every instance, the documentation for the function explains what is returned, so you know how to take the returned results and further process them.
The ability to call the simulation functions directly instead of going through the graphical interface allows additional exploration of the models. For instance if you wanted to explore the behavior of a model systematically for different values of a given parameter, this would need to be done manually if run through the graphical interface. Calling the function directly allows you to automate this by wrapping the function inside a loop over the parameter of interest, recording some quantity of interest for each run, and report the result at the end. The following is a simple example, showing a loop over different values of the recovery rate and recording the peak of the outbreak each time, with the final result peak of outbreak as function of recovery time shown in a plot:
gvec = seq(0.01,0.3,by=0.01) #values of recovery rate, g, for which to run the simulation
peak = rep(0,length(gvec)) #this will record the peak values for each g
for (n in 1:length(gvec))
{
#call the simulator function with different values of g each time
result <- simulate_sir_ode(S = 500, b = 1/2500, g = gvec[n], tfinal = 200)
peak[n] <- max(result$ts[,"I"]) #record max number of infected for each value of g
}
#plot final result
plot(gvec,peak,type='p',xlab='Rate of recovery',ylab='Max number of infected')
Thus, you can add your own custom code to the existing simulator functions and with a few lines of extra code analyze and explore many more questions and scenarios than those accessible through the graphical interface. This provides a lot more flexibility, but requires writing some R code to interface with the supplied simulator functions.
Level 3: Modifying the simulation functions
While level 2 provides the user with a fair amount of flexibility and the ability to investigate the models and ask questions that would be hard or impossible going through the graphical interface, there is still one major constraint. Namely this approach only allows use and analysis of pre-written, existing simulation models. This constraint can be overcome by directly modifiying and customizing the underlying simulation functions. As was true for moving from level 1 to 2, this move from level 2 to what I call level 3 provides further (almost unlimited) flexibility at the cost of having to write increasingly more R code.
To make modifying the existing functions easy, copies of all simulator functions are provided in a folder called simulatorfunctions which can be downloaded as a zip file from the main menu. Each function in that folder starts with simulate_. The Further Information section in each app tells you which simulator function goes with which app.
The code for each simulator function is (hopefully) well documented. Some basic to intermediate level of R coding experience is likely required to successfully modify the functions. In addition to modifying the simulator function of interest, you will likely also have to write some additional code to interact with their modified function (as described in Level 2).
The following provides a simple example of this process of modifying a simulator function and exploring its results. Assume that we want to modify the simple SIR model encoded in simulate_sir_ode.R. (To make things easy, the name of a .R file containing the code and the name of the function itself are the same.) After finding the file, making a copy and renaming it (called mysimulator.R here), we can make modifications.
Say we want to include waning immunity with recovered returning to the susceptible class at rate w.
We will need to modify the following lines of code:
old:
new:
old:
new:
old (ignoring comments present in the code):
new:
Note that if we wanted to share this function with others, we would need to also update the function documentation at the start of the file. For personal use of the function, this is a good idea but not required.
We could now for instance explore how different rates of waning immunity impact the maximum peak size over all outbreaks. This requires a slight modification of the code shown above in Level 2 as follows:
source('mysimulator.R') #to initialize the new function - it needs to be in same directory as this code
wvec = seq(0,1,by=0.02) #values of immunity loss rate, w, for which to run the simulation
peak = rep(0,length(wvec)) #this will record the peak values for each g
for (n in 1:length(wvec))
{
result <- mysimulator( S = 1000, I = 1, R = 0, b = 1e-3, g = 0.5, w = wvec[n], tstart = 0, tfinal = 300, dt = 0.1)
peak[n] <- max(result$ts[,"I"])
}
plot(wvec,peak,type='p',xlab='Rate of waning immunity',ylab='Max number of infected')
For most users and especially novice coders, it is likely best to start with one of the provided models and modify as needed. However, at this level, you have essentially full control and are only limited by what can be accomplished using the R programming language and their ability and interest in writing customized code.