Network Modeling for Epidemics
Meeting Times:
- Wednesday, July 24, 1:30 PM – 5:00 PM
- Thursday July 25, 8:30 AM – 5:00 PM
- Friday July 26, 8:30 AM – 5:00 PM
Classroom:Randall Rollins Building (RR 226)
Module Summary:
Network Modeling for Epidemics (NME) 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.
While we briefly cover some traditional (e.g., compartmental) epidemic modeling background, our primary focus is on the theory, methods, and application of network models. NME uses a mix of lectures, tutorials, and labs with students working in small groups. On the final day, students work to develop an advanced EpiModel prototype (either individually or in groups based on shared research interests), with input from the instructors.
Prerequisites:
We require at least a working knowledge of the R programming language. If you are inclined, feel free to also browse through the Statnet tutorials and literature listed here: https://statnet.org/workshops/. It is also very helpful but not required to have some prior experience with traditional (e.g., compartmental/differential equation/SIR) epidemic modeling.
Module Content:
- Cross-sectional statistical network analysis (ERGMs)
- Dynamic statistical network analysis (STERGMs)
- Simple epidemic models on networks
- Epidemics in fixed populations with network dynamics independent of disease state
- General epidemic models on networks
- Epidemics in open populations, with interactions between networks, demographics and infection.
- Extending EpiModel for original research projects with templates for user-programmed modules that allow EpiModel to be extended to the full range of pathogens, hosts, and disease dynamics needed for advanced research.
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.
Steve 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://statnet.org/nme/d0.html.
