In this lab, you will work modify the new diagnostic and case isolation features of the COVID model in the previous tutorial to investigate the impact of different forms and timing of screen-based interventions on prevention of SARS-CoV-2 transmission. The specific learning objectives for this lab are to:

  1. Simulate and experiment with a full-scale research model for COVID-19 programmed with extension modules in EpiModel;
  2. Define next steps for extending diagnostic-based interventions further, including with contact tracing activities.

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

  1. Start with the model code and module functions from the tutorial, and fit and run the diagnostics for the network model. Inspect and source in the new epidemic modules. Discuss any conceptual questions about the new module design with your group.
  2. Run through the new infection module in browser mode, and inspect the discordant edgelist at a particular time step.
  3. Run a couple different scenarios of the model with different intervention parameters. This could include changing the diagnosis rates or the case isolation intensity after diagnosis. How much does disease prevention depend on natural history of disease (e.g., the proportion in the subclinical pathway) or the diagnostic assumptions (e.g., the PCR sensitivity)?

Lab Questions

  1. Discuss how you might add a “contact tracing” intervention to the current model. Contact tracing involves identifying and then isolating and/or screening contacts of positive diagnosed cases. What existing modules would you modify to accomplish this, and how would you do so.
  2. What are remaining intervention scenarios that we have not built out for COVID yet, but are of interest to your group. What are the natural next steps for building those out in code?

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