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Mining Epidemiological Data Sources in H1N1 Pandemic Using Probabilistic Graphical Models

Authors:
Masoumeh Tabaeh Izadi
David Buckeridge
Katia Charland

Keywords: data integration, Bayesian networks, time series analysis, surveillance

Abstract:
It is generally difficult to estimate disease prevalence or true infection probabilities because these are not observable quantities. However, these parameters can be estimated from available data sources that can provide partial indications of the true incidence of infected cases or prevalence rates. However, building a construct capable of incorporating data from these various sources in a coherent manner is not trivial. In addition, the prevalence of an infectious strain must be estimated in a timely manner. For instance, in an epidemic, this estimate must be obtained within a day or so. We propose to use dynamic Bayesian networks from the class of probabilistic graphical models in order to identify probabilistic relationships between different data streams. This is an initial step towards building a framework that can support data integration and real-time estimation of disease prevalence. Our preliminary results on data sources related to H1N1 pandemic show that the proposed models generalize well.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2011

Publication date: October 23, 2011

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-162-5

Location: Barcelona, Spain

Dates: from October 23, 2011 to October 29, 2011