This is the online headquarters for the Network Modeling for Epidemics (NME) course. NME is a 5-day short course at the University of Washington that provides an introduction to stochastic network models for infectious disease transmission dynamics, with a focus on empirically based modeling of HIV transmission. It is a “hands-on” course, using the EpiModel software package in R ( EpiModel 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 several types of epidemic models: deterministic compartmental, stochastic individual-based, and stochastic network models. Resources include simple models that run in a browser window, built-in generic models that provide basic control over population contact patterns, pathogen properties and demographics, and templates for user-programmed modules that allow EpiModel to be extended to the full range of pathogens, hosts, and disease dynamics for advanced research. This course will touch on the deterministic and individual-based models, but its primary focus is on the theory, methods and application of network models.

The course will use integrated lectures, example-driven computer lab sessions, and extensive tutorial materials to teach deterministic and stochastic models for infectious disease epidemics. Students are required to bring their own laptop computer to the course. Prior to the course, students are recommended to review the materials on the PREP page. Each day’s materials will be posted on the respective page linked above.

2017 Course Annoucement

The 2017 Network Modeling for Epidemics course will be offered from August 14 to 18 at the University of Washington in Seattle. The course scheduled will run from approximately 9 am to 5 pm each day, with breaks for lunch.

Course Syllabus

The course uses mornings for lectures, and afternoons for labs with students working in small groups. On the final day, students have the option of developing an EpiModel prototype for their own research projects, with input from the instructors, which includes the EpiModel software developers.

Day Topics
1 Introduction to epidemic modeling; Stochastic models for epidemics; Classical descriptive network analysis
2 Cross-sectional statistical network analysis (ERGMs); Dynamic statistical network analysis (STERGMs)
3 Simple epidemic models on networks; Epidemics in fixed populations with network dynamics independent of disease state
4 General epidemic models on networks; Epidemics in open populations, with interactions between networks, demographics and infection
5 Extending EpiModel for original research projects; Individual consultations on participant projects

Application Information

  • May 1: Fellowship application deadline. Decisions will be made by May 15.
  • June 1: General application deadline. Applications will continue to be accepted on a rolling basis until this date. Decisions will be made by June 15.

Course fee is $500. Travel and accommodation costs are the responsibility of the participant, although discounted hotel rates are available. We offer a limited number of fellowships for pre-doctoral students or for attendees from low income countries; these cover waiver of the registration fee only (travel and accommodation are still the responsibility of the fellowship recipient).

Returning Students

We encourage previous attendees with active modeling projects to apply to return for a refresher course. The EpiModel package has been significantly enhanced over the last few years. Returning students with active projects will have the opportunity to work with course instructors to address key challenges in the design of their network model code.

Previous Course Offerings

This course, either in its entirety or parts of it, has been offered at the following locations:


Email Lists

We encourage you to join the email lists for Statnet and EpiModel as a place to ask questions, report bugs, and tell us about your research using these tools.


This course is supported by grant number R01HD68395 from the National Institute of Child Health and Human Development.