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SUMMARY:Modeling and Simulation of Dynamic Networks using Egocentrically-S
 ampled Data - Pavel Krivitsky (University of Wollongong)
DTSTART:20161216T141500Z
DTEND:20161216T150000Z
UID:TALK69529@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:&nbsp\;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 complicat
 ed by the  difficulty of collecting data about these networks. Rather than
  the more  traditional panel (repeated observations) or event data\, in wh
 ich all  individuals are identified\, these networks are often observed on
 ly in  the form of an egocentric survey: a sample of individuals in the ne
 twork  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). <br>  &nbsp\;<br>This work devel
 ops a generalized method of moments approach to  simulation and inference 
 for dynamic networks models from such data by  using the models&#39\; long
 -run properties\, and proposes a network-size  invariant parametrization t
 o facilitate using these models to simulate  populations with changing siz
 es and compositions. <br>  <span>&nbsp\;<br>These techniques are applied t
 o egocentric data from the 1992 US  National Health and Social Life Survey
 \, and other applications are  demonstrated as well\, produced in collabor
 ation Martina Morris and Steven Goodreau and others.<br></span>
LOCATION:Seminar Room 1\, Newton Institute
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