Network Modeling for Epidemics (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 (www.epimodel.org).
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 uses a mix of lectures, tutorials, and labs with students working in small groups. On the final day, students work to develop an EpiModel prototype model (either individually or in groups based on shared research interests), with input from the instructors. 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.
The 2020 Network Modeling for Epidemics course will be offered from Monday, August 17 to Friday, August 21 at the University of Washington in Seattle. The course scheduled will run from approximately 9 am to 5 pm each day, with breaks for coffee and lunch. (Coffee, refreshments and meals are not provided, but a variety of options for each are available near the course location.)
Due to the travel restrictions associated with the COVID-19 global pandemic, we have updated our plans for NME 2020 as follows:
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.
|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|
Course fee is $800 for the in-person option and $500 for the virtual option. Travel and accommodation costs are the responsibility of the participant, although discounted hotel rates are available. We offer a limited number of fee waivers 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 fee waiver recipient.
Apply online at: https://catalyst.uw.edu/webq/survey/morrism/385149
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.
This course, either in its entirety or parts of it, has been offered at the following locations:
This course is supported by grant number R01 AI138783 from the National Institutes of Health.
These course materials are distributed under the GPL-3 license, with the following copyright and attribution requirements listed here.
Last updated: 2020-04-07