Network Modeling for Epidemics

SISMID 2025 Course

Authors

Samuel Jenness, PhD MPH

Steven Goodreau, PhD

Martina Morris, PhD

Published

July 25, 2025

Welcome

This is the course website for the 2025 Network Modeling for Epidemics (NME) course. We are part of the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) hosted at Emory University. This website includes all the materials and information needed for the 2025 NME course.

Details

NME is a short course that provides an introduction to stochastic network models for infectious disease transmission dynamics. It is a ‘’hands-on’’ course, using the EpiModel software package in R. EpiModel software provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics on these networks. It has a flexible open-source platform for learning and building these models. The course uses a mix of lectures, tutorials, and discussions with students.

NME at SISMID focuses on the theory, methods, and application of network models for epidemics using a principled statistical framework known as “Exponential-family Random Graph Models” (ERGMs) that allow researchers to represent everything from simple random graphs (aka “Erdos-Reyni” or “Bernoulli random graphs”) to very complex networks.

Description and Course Logistics

The NME is split into two parts (two SISMID “modules” that may be independently taken): NME-I and NME-II.

NME-I introduces stochastic network models for infectious disease transmission dynamics. It is a ‘’hands-on’’ course, using the EpiModel software package in R. EpiModel software provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics over these networks. This explicit modeling of networks is essential for accurate projections when the contacts that enable transmission are sparse, highly structured, heterogenous and/or evolving over time. The course material covers the basic theory, methods, and application of network models for epidemics, with a specific focus on the statistical framework of temporal exponential random graph models (TERGMs). TERGMs provide a unique, flexible, principled data-driven foundation for dynamic network modeling and stochastic simulation. Comparisons to traditional mathematical modeling (e.g., compartmental or differential equation) are included to help highlight the differences between these frameworks.

NME-II extends the material in NME-I to developing research-level applications of EpiModel and its underlying TERGM statistical framework. Here, we focus on learning how to use the application programming interface (API) in EpiModel to design and program epidemic model components (or “modules”) that define a network-based epidemic model for a specific research question. The goal is to enable students to build EpiModel extensions to represent any infectious disease components in a system of interest. This intermediate course will cover more advanced methods, such as working with multi-layer networks that represent different types of contacts (e.g., home and community) within the same population. We also demonstrate the process of working with egocentric network data, an inexpensive sample survey data collection design, to specify an epidemic model. Examples will cover the whole workflow: from conceptualization and data collection to estimation and simulation in EpiModel. NME-II utilizes multiple learning approaches: collective exercises to design and build a complex, disease-specific network-based epidemic model; lab work to design specific epidemic module components from demographics to prevention interventions; and individual consultations on students’ own network-based epidemic modeling projects.

SISMID 2025 Course Details

NME-I will be meeting Monday, July 21 at 9 am to Wednesday, July 23 at Noon. NME-II will be meeting Wednesday, July 23 at 1:30 pm to Friday, July 25 at 5 pm. Both sections of NME will be offered in-person only, at the Rollins School of Public Health at Emory University.

Prerequisites and Preparation

Before joining NME, please review NME Preparation chapter materials to ensure that you have an updated version of course software installed on your computer, and that you come into the course with some background and motivation for network modeling.

For NME-I, we assume a working knowledge of R; some background in infectious disease modeling (e.g., compartmental or differential equation modeling) is helpful but not necessary. For NME-II, we assume knowledge of NME-I.

Instructors

Steven Goodreau, PhD // Professor of Biological Anthropology // University of Washington Dr. Goodreau received his PhD from Penn State in 2001, where his work focused on the intersections of population genetics, social network analysis and demography in the context of HIV evolution. He has been at UW since then, as an active member of the Statnet Development Team and co-lead of the Network Modeling Group. Among his administrative hats is Development Director for the Center for Studies in Demography and Ecology (CSDE), UW’s NIH-funded population center. Dr. Goodreau’s modeling work focuses primarily on the epidemiology of HIV among men who have sex with men, both domestically and internationally, with interests in both the origin of disparities and the likely impact of interventions. He maintains additional research foci on the evolution of HIV virulence, and on sexual health among adolescents more broadly.

Samuel Jenness, PhD MPH // Associate Professor of Epidemiology // Emory University Dr. Jenness is the Principal Investigator of the EpiModel Research Lab, funded by the National Institutes of Health and the Centers for Disease Control and Prevention. His research focuses on developing methods and software tools for modeling infectious diseases, with primary applications focused on understanding HIV and STI transmission in the United States and globally. He also works on epidemiological problems at the intersection of infectious diseases and network science, including measurement and quantification of dynamic social and genomic networks for HIV/STIs and tuberculosis. He received his PhD in Epidemiology at the University of Washington, prior to which he conducted applied prevention research at the health departments of New York City and Massachusetts.

Martina Morris, PhD // Professor Emerita of Statistics and Sociology // University of Washington Dr. Morris’s primary contributions to science have been statistical methods for network analysis, with applications to the population dynamics of HIV transmission. For two decades, she has co-led (with Drs. Mark S. Handcock and Steven Goodreau), the NIH-funded team that developed the Exponential family Random Graph Models (ERGMs) framework for statistical network analysis. Their methods are designed to work with both sampled and census network data, and are published in a suite of open-source R packages under the statnet organization on CRAN and GitHub. Over the last decade, she extended this foundation to develop a comprehensive, principled framework for stochastic modeling of epidemics on dynamic networks that is now implemented in the open-source software package EpiModel. Her current applied research projects focus on the application of these methods to local HIV prevention planning efforts. She is committed to the development of innovative statistical methodology that addresses critical needs in public health, to applications of these methods to support HIV prevention efforts, and to a transparent, reproducible science workflow, ensuring access to the methodology by creating open-source, user-friendly tools.

Acknowledgements

EpiModel has been developed with the generous support of the U.S. National Institutes of Health. The publication of this ebook was supported by the NIH grant R01 AI138783.

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

# Welcome {.unnumbered} ```{r setup, include=FALSE} rv <- R.version.string requireNamespace("EpiModel") ``` This is the course website for the 2025 Network Modeling for Epidemics (NME) course. We are part of the [Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID)](https://sismid.sph.emory.edu) hosted at Emory University. This website includes all the materials and information needed for the 2025 NME course. ## Details NME is a short course that provides an introduction to stochastic network models for infectious disease transmission dynamics. It is a ''hands-on'' course, using the [EpiModel software package in R](https://www.epimodel.org/). EpiModel software provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics on these networks. It has a flexible open-source platform for learning and building these models. The course uses a mix of lectures, tutorials, and discussions with students. NME at SISMID focuses on the theory, methods, and application of network models for epidemics using a principled statistical framework known as "Exponential-family Random Graph Models" (ERGMs) that allow researchers to represent everything from simple random graphs (aka "Erdos-Reyni" or "Bernoulli random graphs") to very complex networks. ## Description and Course Logistics The NME is split into two parts (two SISMID "modules" that may be independently taken): NME-I and NME-II. **NME-I** introduces stochastic network models for infectious disease transmission dynamics. It is a ‘’hands-on’’ course, using the EpiModel software package in R. EpiModel software provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics over these networks. This explicit modeling of networks is essential for accurate projections when the contacts that enable transmission are sparse, highly structured, heterogenous and/or evolving over time. The course material covers the basic theory, methods, and application of network models for epidemics, with a specific focus on the statistical framework of temporal exponential random graph models (TERGMs). TERGMs provide a unique, flexible, principled data-driven foundation for dynamic network modeling and stochastic simulation. Comparisons to traditional mathematical modeling (e.g., compartmental or differential equation) are included to help highlight the differences between these frameworks. **NME-II** extends the material in NME-I to developing research-level applications of EpiModel and its underlying TERGM statistical framework. Here, we focus on learning how to use the application programming interface (API) in EpiModel to design and program epidemic model components (or “modules”) that define a network-based epidemic model for a specific research question. The goal is to enable students to build EpiModel extensions to represent any infectious disease components in a system of interest. This intermediate course will cover more advanced methods, such as working with multi-layer networks that represent different types of contacts (e.g., home and community) within the same population. We also demonstrate the process of working with egocentric network data, an inexpensive sample survey data collection design, to specify an epidemic model. Examples will cover the whole workflow: from conceptualization and data collection to estimation and simulation in EpiModel. NME-II utilizes multiple learning approaches: collective exercises to design and build a complex, disease-specific network-based epidemic model; lab work to design specific epidemic module components from demographics to prevention interventions; and individual consultations on students’ own network-based epidemic modeling projects. ### SISMID 2025 Course Details NME-I will be meeting Monday, July 21 at 9 am to Wednesday, July 23 at Noon. NME-II will be meeting Wednesday, July 23 at 1:30 pm to Friday, July 25 at 5 pm. Both sections of NME will be offered in-person only, at the Rollins School of Public Health at Emory University. ## Prerequisites and Preparation Before joining NME, please review [NME Preparation](0_nme_prep/software.qmd) chapter materials to ensure that you have an updated version of course software installed on your computer, and that you come into the course with some background and motivation for network modeling. For NME-I, we assume a working knowledge of R; some background in infectious disease modeling (e.g., compartmental or differential equation modeling) is helpful but not necessary. For NME-II, we assume knowledge of NME-I. ## Instructors **Steven Goodreau, PhD // Professor of Biological Anthropology // University of Washington** [Dr. Goodreau](https://faculty.washington.edu/goodreau/) received his PhD from Penn State in 2001, where his work focused on the intersections of population genetics, social network analysis and demography in the context of HIV evolution. He has been at UW since then, as an active member of the Statnet Development Team and co-lead of the Network Modeling Group. Among his administrative hats is Development Director for the Center for Studies in Demography and Ecology (CSDE), UW’s NIH-funded population center. Dr. Goodreau’s modeling work focuses primarily on the epidemiology of HIV among men who have sex with men, both domestically and internationally, with interests in both the origin of disparities and the likely impact of interventions. He maintains additional research foci on the evolution of HIV virulence, and on sexual health among adolescents more broadly. **Samuel Jenness, PhD MPH // Associate Professor of Epidemiology // Emory University** [Dr. Jenness](https://samueljenness.org) is the Principal Investigator of the EpiModel Research Lab, funded by the National Institutes of Health and the Centers for Disease Control and Prevention. His research focuses on developing methods and software tools for modeling infectious diseases, with primary applications focused on understanding HIV and STI transmission in the United States and globally. He also works on epidemiological problems at the intersection of infectious diseases and network science, including measurement and quantification of dynamic social and genomic networks for HIV/STIs and tuberculosis. He received his PhD in Epidemiology at the University of Washington, prior to which he conducted applied prevention research at the health departments of New York City and Massachusetts. **Martina Morris, PhD // Professor Emerita of Statistics and Sociology // University of Washington** [Dr. Morris’s](https://faculty.washington.edu/morrism/) primary contributions to science have been statistical methods for network analysis, with applications to the population dynamics of HIV transmission. For two decades, she has co-led (with Drs. Mark S. Handcock and Steven Goodreau), the NIH-funded team that developed the Exponential family Random Graph Models (ERGMs) framework for statistical network analysis. Their methods are designed to work with both sampled and census network data, and are published in a suite of open-source R packages under the statnet organization on CRAN and GitHub. Over the last decade, she extended this foundation to develop a comprehensive, principled framework for stochastic modeling of epidemics on dynamic networks that is now implemented in the open-source software package EpiModel. Her current applied research projects focus on the application of these methods to local HIV prevention planning efforts. She is committed to the development of innovative statistical methodology that addresses critical needs in public health, to applications of these methods to support HIV prevention efforts, and to a transparent, reproducible science workflow, ensuring access to the methodology by creating open-source, user-friendly tools. ## Acknowledgements EpiModel has been developed with the generous support of the U.S. National Institutes of Health. The publication of this ebook was supported by the NIH grant [R01 AI138783](https://reporter.nih.gov/search/uTz00QWdaUaMsBeUiCwj_w/project-details/9623724). <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" alt="Creative Commons License" style="border-width:0"/></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.