Network Modeling for Epidemics II
Meeting Times:
- Wednesday, July 22, 1:30 PM – 5:00 PM
- Thursday July 23, 9:00 AM – 5:00 PM
- Friday July 24, 9:00 AM – 5:00 PM
Classroom: TBA
Module Summary:
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.
NoteSISMID 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
Samuel Jenness, PhD
Associate Professor, Department of Epidemiology, Emory University
Samuel Jenness, PhD MPH is an Associate Professor in the Department of Epidemiology at Emory University. He is the Principal Investigator of the EpiModel Research Lab, which uses epidemiological and economic modeling approaches to understand the dynamics of sexually transmitted and respiratory infectious diseases. Recent studies have investigated the co-circulation of multiple infectious pathogens and optimizing the scale-up of prevention interventions to reduce health disparities.
His methodological research has led to the development of an open-source software platform, EpiModel, which allows users to build and simulate data-driven mechanistic models for infectious disease dynamics that integrate network data and models.
Martina Morris, PhD
Professor Emerita, Department of Statistics and Department of Sociology, University of Washington
Dr. Morris is a sociologist with an interest in the analysis of social structure and population dynamics. Her research is interdisciplinary, intersecting with demography, economics, epidemiology and public health, and statistics. Examples from her current projects include the study of partnership networks in the spread of HIV/AIDS, the impact of economic restructuring on inequality and mobility, and the development of Relative Distribution methods for statistical analysis.
Steven Goodreau, PhD
Professor, Department of Anthropology, University of Washington
Dr. Goodreau's research interests are in the use of network modeling and network data to explore the epidemiology of HIV and other STIs. He is a co-developer of the statnet and EpiModel suites for network epidemic modeling. He has published on behavioral and clinical drivers of HIV disparities, as well as on assessments of interventions, primarily among communities of men who have sex with men, both domestically and internationally. His current work also explores behavioral and clinical impacts on HIV viral evolution.
Required Software:
We will be using the R statistical programming language throughout. Within R, users will install EpiModel and the related Statnet suite of packages for network analysis.
Recommended Reading:
Prior to the course, we recommend students review the materials on this page: https://epimodel.io/0_nme_prep/reading.html.