wiki:NME2014

2014 Network Modeling for Epidemics (NME) Course

5+1 day Summer Workshop
University of Washington, Seattle WA
June 16-21, 2014

Course Website: http://statnet.csde.washington.edu/EpiModel/nme/2014/index.html
Target Audience: Researchers and PhD students from any field who have an interest in epidemic modeling

Apply for Course: The deadlines have passed for 2014

Deadlines:

  • March 1st: Fellowship applications must be received by this date to be considered.
  • April 1st: Regular applications will be processed on this date, applications received after this date will be considered only if space is available.

Mathematical modeling plays a growing role in infectious disease epidemiology, for studying the dynamics of pathogen invasion and persistence, understanding the determinants 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 three decades. However, recent advances in statistical theory and methods have given rise to a new class of stochastic network models that provide an integrated framework for the statistical estimation of generative contact network parameters and the stochastic simulation of dynamic networks and transmission processes. Stochastic network models are more appropriate when infection is spread by a small number of highly structured contacts, as with HIV and other STIs, or for small scale assessments, where the effects of chance lead to wide variation in potential outcomes.

This course will start with a brief review of traditional compartmental (SI, SIR, SIS) models, and the methodology for classical descriptive network analysis. It will then provide a thorough introduction to the new statistical methods for network analysis: Exponential family Random Graph Models (ERGMs). The course will conclude with instruction on using these methods to develop an integrated framework for stochastic network models for epidemics, with a focus on empirical models of HIV transmission and control.

This will be a “hands-on” course, with integrated lectures, example-driven computer lab sessions, and extensive tutorial materials. The labs will develop programming skills in statnet and EpiModel, an integrated suite of R-based software packages that simplify statistical network analysis, simulation and visualization, and provide built-in functionality for both deterministic compartmental and stochastic network modeling of epidemics. Participants will learn to estimate and evaluate generative network models from empirical data, use the estimated models to simulate dynamic transmission networks consistent with the data, analyze the results (using all the functionality of R packages) and construct network movies that show the infection spreading on the network.

The course outline for the week is:

Date Course Content
Mon, June 16 Introduction to epidemic modeling
Deterministic and stochastic models for epidemics
Lab: Programming simple deterministic and stochastic SI, SIS and SIR models with EpiModel
Tue, June 17 Classical network analysis
Introduction to Exponential Random Graph Models (ERGMs) for statistical network analysis
Lab: Using statnet for static network modeling and visualization
Wed, June 18 Separable temporal ERGMs (STERGMs) for dynamic networks
Using egocentrically sampled network data in ERGMs and STERGMs
Lab: Using statnet for dynamic network modeling and visualization
Thu, June 19 Simple epidemic models on networks
Epidemics in fixed populations with network dynamics independent of disease state
Lab: Using the EpiModel package for dynamic networks in a fixed population
Fri, June 20 General epidemic models on networks
Epidemics in open populations, with interactions between networks, demographics and infection
Lab: EpiModel and dynamic networks with changing population size/composition

Participants interested in using EpiModel for their own research projects should consider staying for the advanced topic session on Saturday. Saturday attendance is optional, and applicants who wish to attend should indicate this on the application form.

Date Course Content
Sat, June 21 Extending EpiModel: Modifying the code for user-specific research applications
Computing Issues: Parallelization, memory management, and other computing tips and tricks
Individual Consultations: Discussion of participant research projects


Course Instructors

Instructor Title Department Webpage email
Martina Morris Professor Dept. of Sociology and Statistics,
University of Washington
http://faculty.washington.edu/morrism/ morrism at uw.edu
Steven M. Goodreau Assoc. Professor Dept. of Anthropology,
University of Washington
http://faculty.washington.edu/goodreau/ goodreau at uw.edu
Samuel M. Jenness PhD Candidate Dept. of Epidemiology,
University of Washington
http://samueljenness.org/ sjenness at uw.edu


Prerequisites

Required: Basic familiarity with R (see below).
Recommended: Knowledge of intermediate level statistics, especially generalized linear models, and some epidemic modeling experience (broadly defined).

Those with general statistical/modeling skills but no knowledge of R may apply now, and obtain familiarity with R in the interim, via:

You may also want to look through some of the tutorials for the statnet software.

This online tutorial contains a nice, brief introduction to compartmental models for infectious disease, for those new to this area.

Logistics

Dates: Monday, June 16 – Saturday, June 21, 2014

Times: 9:30 am (sharp) – 4:30 pm

Location: On the main campus of the University of Washington in Seattle. http://www.washington.edu/maps/

Accommodations: A block of hotel rooms will be available at a discounted rate.

Costs: Registration cost is $250. A small number of fellowships will be provided to predoctoral students and/or international (non-US/Canada) scholars.

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

Last modified 3 years ago Last modified on 06/09/14 11:49:38