For each of the three model classes in EpiModel, the tutorials are organized into basic integrated models to guide new users in the features of the model class, and advanced extension models to build out the models to answer new research questions.

Basic Integrated Models

Basic DCMs with EpiModel    This tutorial provides some mathematical background for deterministic compartmental models, with exploration of different model types and parameterizations within EpiModel.
Tutorial    Code

Basic ICMs with EpiModel    Stochastic individual contact models (ICMs) are the microsimulation analogs to DCMs. This tutorial explains the general differences between deterministic and stochastic modeling, with hands-on basic examples.
Tutorial    Code

Basic Network Models with EpiModel    Stochastic network models build in arbitrarily complex contact or partnership relational structures that form and dissolve over time, using the framework of temporal exponential random graph models. This tutorial shows how to simulate epidemics over simple networks with easily defined network structures.
Tutorial    Code

Advanced Extension Models

New DCMs with EpiModel    Creating new deterministic compartmental models in EpiModel involves writing new model functions defining the mathematical transition processes, and then parameterizing and simulating those models. This tutorial shows examples of how to write model functions, including new parameters, and run new models.
Tutorial    Code

New Network Models with EpiModel    Writing new network models requires creating modules that are plugged in to the epidemic simulation. This framework allows for new epidemiological processes of arbitrary complexity that interact with the unique dynamic network structures of interest. In addition, we have a growing library of examples for extension models for EpiModel at our EpiModel Gallery!
Tutorial     Code


Network Utility Functions   This tutorial shows extended examples of utility functions within EpiModel that help with the parameterization and simulation of stochastic network models.