Probability and Statistics in Complex Systems: Genomics, Networks, and Financial Engineering, September 1, 2003 - June 30, 2004

Fall 2003

IMA Workshop 3:

Networks and the Population Dynamics of Disease Transmission

November 17-21, 2003

Organizers:

Martina Morris
Departments of Sociology and Statistics
University of Washington
morrism@u.washington.edu
http://faculty.washington.edu/morrism/

Claudia Neuhauser
Department of Ecology, Evolution and Behavior
University of Minnesota
cneuhaus@cbs.umn.edu
http://www.cbs.umn.edu/eeb/faculty/NeuhauserClaudia.html

Infectious diseases are transmitted from person to person, so our understanding of disease transmission is rooted in a theory of population transmission dynamics. For Sexually Transmitted Diseases (STDs) or Blood Borne Infections (BBIs), where transmission requires an exchange of body fluids, the structure of the contact network plays a particularly critical role. The contact network can be represented as a graph, where the persons are nodes, and the partnerships are edges. Simple mathematical models of disease transmission dynamics through such networks have provided a number of insights through simulation that have led to changes in STD control strategies. Much work has been done in the last 15 years to model HIV transmission, and to collect survey data on the partnership networks. But the link between data and models is still problematic. Random graph models, and the techniques for estimating them, are the natural solution. A class of statistical exponential family models for random graphs has recently been adapted from the spatial statistics literature for social networks. Markov Chain Monte Carlo (MCMC) techniques can be used for likelihood-based and Bayesian inference. MCMC can also be used to simulate the network for given parameters, thus linking the network data to the network simulation. This workshop will cover the recent advances in network modeling, with applications to disease prevention and other social science fields. Networks, and their associated population dynamics, have a broad range of applications in both the social and physical sciences. The natural audience includes statisticians, epidemiologists, graph theorists, sociologists, and those in bio-behavioral health, ecology and evolutionary biology.

LIST OF CONFIRMED PARTICIPANTS

Photo Gallery       Material from Talks       Abstracts

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Probability and Statistics in Complex Systems: Genomics, Networks, and Financial Engineering, September 1, 2003 - June 30, 2004