Network Modeling for Epidemics

SISMID Course Materials

Authors

Samuel Jenness, PhD MPH

Steven Goodreau, PhD

Martina Morris, PhD

Published

April 7, 2026

Welcome

Network Modeling for Epidemics (NME) is a hands-on short course on stochastic network models for infectious disease transmission dynamics, part of the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) at Emory University.

Why Network Models?

Traditional epidemic models assume contacts are random and uniform — but real disease transmission happens across structured, heterogeneous, and evolving networks of human (and animal) contact. When contacts are sparse, clustered, or changing over time, network models provide more realistic and accurate projections than standard compartmental approaches. This course teaches you how to build, fit, and simulate these models using a principled statistical framework.

The EpiModel Platform

NME is built around EpiModel, an open-source R package for simulating epidemic dynamics on dynamic networks. EpiModel integrates:

Statistical network models (ERGMs/TERGMs) from the Statnet suite for representing complex contact patterns from data
Stochastic simulation of disease transmission, progression, and recovery over evolving networks
A modular API for building custom disease models for any pathogen or population

EpiModel has been used in 125+ published studies spanning HIV/STI epidemiology, COVID-19, mpox, MRSA, and wildlife disease. Explore worked examples in the EpiModel Gallery and source code on GitHub.

Course Structure

NME consists of two independently enrollable SISMID modules:

NME-I: Foundations introduces stochastic network epidemic modeling through lectures, R tutorials, and labs. You will learn to specify network models from data using temporal exponential random graph models (TERGMs), simulate epidemic dynamics over these networks, and compare results to traditional compartmental approaches.

NME-II: Applications extends NME-I to research-level model building. You will learn to use EpiModel’s API to design custom epidemic modules, work with multi-layer networks (e.g., household and community contacts), and parameterize models from egocentric network survey data. The course includes collaborative model-building exercises, lab work on disease-specific components, and individual project consultations.

SISMID 2026

Dates: NME-I meets Monday July 20 (9 AM) through Wednesday July 22 (12:30 PM). NME-II meets Wednesday July 22 (1:30 PM) through Friday July 24 (5 PM). Both sections are in-person only at the Rollins School of Public Health, Emory University. See the SISMID website for registration and logistics.

Prerequisites

Before the course, please complete the NME Preparation materials to install the required software and review background reading.

NME-I: Working knowledge of R. Background in infectious disease modeling is helpful but not required.
NME-II: Completion of NME-I or equivalent experience.

Instructors

Steven Goodreau, PhD — Professor of Biological Anthropology, University of Washington. Co-lead of the Network Modeling Group and member of the Statnet Development Team. His modeling work focuses on HIV epidemiology among men who have sex with men, the evolution of HIV virulence, and adolescent sexual health.

Samuel Jenness, PhD MPH — Associate Professor of Epidemiology, Emory University. PI of the EpiModel Research Lab, funded by NIH and CDC. His research develops methods and software for modeling infectious diseases, with applications to HIV/STI transmission, network science, and tuberculosis.

Martina Morris, PhD — Professor Emerita of Statistics and Sociology, University of Washington. Co-led the NIH-funded team that developed the ERGM statistical framework and the Statnet software suite. Her work established the foundation for stochastic epidemic modeling on dynamic networks now implemented in EpiModel.

Acknowledgements

The development of EpiModel has been supported by the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC). The primary software development grants are NIH R01 AI138783 and R01 HD68395. Details on our full funding support are available on the EpiModel project page.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

# Welcome {.unnumbered} **Network Modeling for Epidemics (NME)** is a hands-on short course on stochastic network models for infectious disease transmission dynamics, part of the [Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID)](https://sismid.sph.emory.edu) at Emory University. ## Why Network Models? Traditional epidemic models assume contacts are random and uniform — but real disease transmission happens across structured, heterogeneous, and evolving networks of human (and animal) contact. When contacts are sparse, clustered, or changing over time, network models provide more realistic and accurate projections than standard compartmental approaches. This course teaches you how to build, fit, and simulate these models using a principled statistical framework. ## The EpiModel Platform NME is built around [EpiModel](https://www.epimodel.org/), an open-source R package for simulating epidemic dynamics on dynamic networks. EpiModel integrates: - **Statistical network models** (ERGMs/TERGMs) from the [Statnet](https://statnet.org/) suite for representing complex contact patterns from data - **Stochastic simulation** of disease transmission, progression, and recovery over evolving networks - **A modular API** for building custom disease models for any pathogen or population EpiModel has been used in [125+ published studies](https://github.com/statnet/EpiModel/wiki/EpiModel-in-the-Scientific-Literature) spanning HIV/STI epidemiology, COVID-19, mpox, MRSA, and wildlife disease. Explore worked examples in the [EpiModel Gallery](https://epimodel.github.io/EpiModel-Gallery/) and source code on [GitHub](https://github.com/EpiModel). ## Course Structure NME consists of two independently enrollable SISMID modules: **NME-I: Foundations** introduces stochastic network epidemic modeling through lectures, R tutorials, and labs. You will learn to specify network models from data using temporal exponential random graph models (TERGMs), simulate epidemic dynamics over these networks, and compare results to traditional compartmental approaches. **NME-II: Applications** extends NME-I to research-level model building. You will learn to use EpiModel's API to design custom epidemic modules, work with multi-layer networks (e.g., household and community contacts), and parameterize models from egocentric network survey data. The course includes collaborative model-building exercises, lab work on disease-specific components, and individual project consultations. ::: {.callout-note} ## SISMID 2026 **Dates:** NME-I meets Monday July 20 (9 AM) through Wednesday July 22 (12:30 PM). NME-II meets Wednesday July 22 (1:30 PM) through Friday July 24 (5 PM). Both sections are **in-person only** at the Rollins School of Public Health, Emory University. See the [SISMID website](https://sismid.sph.emory.edu/index.html) for registration and logistics. ::: ## Prerequisites Before the course, please complete the [NME Preparation](0_nme_prep/software.qmd) materials to install the required software and review background reading. - **NME-I:** Working knowledge of R. Background in infectious disease modeling is helpful but not required. - **NME-II:** Completion of NME-I or equivalent experience. ## Instructors **[Steven Goodreau, PhD](https://faculty.washington.edu/goodreau/)** — Professor of Biological Anthropology, University of Washington. Co-lead of the Network Modeling Group and member of the Statnet Development Team. His modeling work focuses on HIV epidemiology among men who have sex with men, the evolution of HIV virulence, and adolescent sexual health. **[Samuel Jenness, PhD MPH](https://samueljenness.org)** — Associate Professor of Epidemiology, Emory University. PI of the EpiModel Research Lab, funded by NIH and CDC. His research develops methods and software for modeling infectious diseases, with applications to HIV/STI transmission, network science, and tuberculosis. **[Martina Morris, PhD](https://faculty.washington.edu/morrism/)** — Professor Emerita of Statistics and Sociology, University of Washington. Co-led the NIH-funded team that developed the ERGM statistical framework and the Statnet software suite. Her work established the foundation for stochastic epidemic modeling on dynamic networks now implemented in EpiModel. ## Acknowledgements The development of EpiModel has been supported by the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC). The primary software development grants are NIH [R01 AI138783](https://reporter.nih.gov/search/uTz00QWdaUaMsBeUiCwj_w/project-details/9623724) and [R01 HD68395](https://reporter.nih.gov/search/uTz00QWdaUaMsBeUiCwj_w/project-details/8588873). Details on our full funding support are available on the [EpiModel project page](https://www.epimodel.org/about.html). This work is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/).