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Modeling and Simulation of Dynamic Networks using Egocentrically-Sampled Data

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SNAW04 - Dynamic Networks

 In spread of infections over a sexual or other contact networks, the timing of the contacts can be as important as their cross-sectional structure. However, their modeling and simulation is complicated by the difficulty of collecting data about these networks. Rather than the more traditional panel (repeated observations) or event data, in which all individuals are identified, these networks are often observed only in the form of an egocentric survey: a sample of individuals in the network reporting non-identifying demographic information (e.g., age, sex, race/ethnicity) about their contacts, as well as and contact history (e.g., start and end of past contacts).
 
This work develops a generalized method of moments approach to simulation and inference for dynamic networks models from such data by using the models' long-run properties, and proposes a network-size invariant parametrization to facilitate using these models to simulate populations with changing sizes and compositions.
 
These techniques are applied to egocentric data from the 1992 US National Health and Social Life Survey, and other applications are demonstrated as well, produced in collaboration Martina Morris and Steven Goodreau and others.

This talk is part of the Isaac Newton Institute Seminar Series series.

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