24 Introduction

This module builds on the basics of network-based epidemic modeling with TERGMs using EpiModel demonstrated in Module 4. Here, we focus on working with a special group nodal attribute that can be flexibly used in built-in network models with EpiModel to represent basic population heterogeneity.

We will build an SIR (susceptible-infected-recovered) model on top of a network representation that involves some differences in the degree distribution by group.

24.1 Module Learning Objectives

Parameterize population heterogeneity in the network degree distribution by dichotomous groups.
Describe issues with balancing of a degree distribution in a purely disortative mixing network configuration.
Parameterize an SIR epidemic model in EpiModel.
Summarize and and visualize group-specific epidemic outcomes from the SIR model.
Visualize a transmission tree of the epidemic with a phylogram.

# Introduction

This module builds on the basics of network-based epidemic modeling with TERGMs using EpiModel demonstrated in Module 4. Here, we focus on working with a special `group` nodal attribute that can be flexibly used in built-in network models with EpiModel to represent basic population heterogeneity. 

We will build an SIR (susceptible-infected-recovered) model on top of a network representation that involves some differences in the degree distribution by group.

## Module Learning Objectives

* Parameterize population heterogeneity in the network degree distribution by dichotomous groups.
* Describe issues with balancing of a degree distribution in a purely disortative mixing network configuration.
* Parameterize an SIR epidemic model in EpiModel.
* Summarize and and visualize group-specific epidemic outcomes from the SIR model.
* Visualize a transmission tree of the epidemic with a phylogram.