Network Modeling for Epidemics (NME)
6 day Summer Workshop
University of Washington, Seattle WA
July 8-13, 2013
Mathematical modeling plays a growing role in infectious disease epidemiology, for studying the dynamics of pathogen invasion and persistence, understanding the sources of disease disparities among populations, and predicting the impact of interventions. Deterministic compartmental models (based on ordinary differential equations) have been the traditional basis for this work during the past two decades. Recent advances in statistical theory and methods have given rise to a new class of stochastic “agent-based” network models, however, and these models are more appropriate for small scale assessments, where the effects of chance lead to wide variation in potential outcomes, or when infection is spread by a small number of highly structured contacts, as with HIV and other STIs.
This course will provide an introduction to the new stochastic network models for epidemiology, with a focus on empirically based modeling of HIV transmission and control. It will be a “hands-on” course, with integrated lectures and computer lab sessions devoted to programming. Labs will be based on the user-friendly computational tools available in the package “statnet” (a free package that uses the R programming language).
Dates: Monday, July 8 – Saturday, July 13, 2013
Times: 10 am (sharp) – 4 pm
Location: Main campus of the University of Washington in Seattle
Instructors: Professors Martina Morris and Steven Goodreau
Costs: Registration is $250
On-campus housing will be available for about $70/night.
A small number of fellowships will be provided to predoctoral students and/or international (non-US/Canada) scholars
Target audience: Researchers and students in any field with an interest in epidemic modeling
Course webpage: https://statnet.csde.washington.edu/trac/wiki/NME2013
Prior experience: Basic familiarity with R is required (but see below).
Previous modeling experience (broadly defined) is recommended.
Those with general statistical/modeling skills but no knowledge of R may apply now, and
obtain familiarity with R in the interim, via