In this lab, you will work to put all the pieces together to construct a network-based epidemic model. In contrast to some of the earlier labs, here you will have more flexibility within your group to parameterize, simulate, and summarize your model. The specific learning objectives for this lab are to:

  1. Practice parameterizing, simulating, and summarizing a network-based epidemic model with a simple vaccine intervention;
  2. Understand the workflow of network model building from a smaller-scale development model to a larger-scale “research” model.
  3. Identify key gaps in the epidemic structure of the built-in models that must be addressed with model extensions to answer your own research questions.

Setup

Once you are ready, start out by clearing your R object environment, to make sure that you do not have any objects lingering from the tutorial. This can be accomplished with:

rm(list = ls())

Lab Steps

As noted above, your group has flexibility of what components to include in the network model parameterization here, but it is best to start with one of your previous models in the prior lab and work from there. We recommend following these general steps:

  1. Using your prior model as a starting point, change the total population size so that the number of nodes is 100. Pay attention to which network model parameters require updating (e.g., if mean degree is constant across population size then any target statistics will need to be reduced by the same proportion as the population size). Run a single simulation of the model over 25 time steps, then produce a network animation.

  2. Scale up the same model to 1000 nodes. You will need to reestimate the network model and reparameterize the epidemic model. Add in a vaccine intervention (following the Vaccine Tutorial) to your model with specified parameters for efficacy and intervention start time. Run 1 or 2 counterfactuals with different vaccine parameters to test a causal question. Visualize the primary contrast in a plot. If time permits here, you could return to the network model and change the parameterization of the network structure to experiment with vaccine performance under different network conditions.

Lab Questions

After you have completed running the models above, please answer the following questions and discuss in your work group.

  1. What are the properties of the network and epidemic model parameterizations that must be updated when the population is scaled. What assumptions does this make about individual-level behavior? Do you think this is realistic across different disease types?

  2. What are your groups key questions about network and epidemic model parameterization at this point? We’ll discuss some of these as a full group.

  3. What are some limitations in running these models in this lab versus what you would like to do? What elements would you like to add to the model, above and beyond changes to the network parameters? Our hope is to show you how to build these into EpiModel in the rest of the workshop.


Last updated: 2022-07-07 with EpiModel v2.3.0