Network Modeling for Epidemics I
Meeting Times:
- Monday, July 21, 9:00 AM – 5:00 PM
- Tuesday July 22, 9:00 AM – 5:00 PM
- Wednesday July 23, 9:00 AM – 12:00 PM
Classroom: TBA
Module Summary:
Network Modeling for Epidemics I (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 uses a mix of lectures, tutorials, and labs with students working in small groups.
Prerequisites:
We require 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 helpful but not required to have some prior experience with traditional mathematical modeling methods for infectious diseases.
Module Content:
- Comparison of traditional mathematical models, agent-based models, mathematical network models, and stochastic statistical network models for epidemics
- Statistical models for network analysis and simulation (ERGMs & TERGMs)
- 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.
- Brief introduction to extending EpiModel for original research projects (a teaser for Network Modeling for Epidemics II)
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 work has also explored 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.
