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Detecting influenza epidemics using hidden Markov models with Bayesian approach

Title:

Detecting influenza epidemics using hidden Markov models with Bayesian approach

Mousavinasab, Seyed Yousef (2007) Detecting influenza epidemics using hidden Markov models with Bayesian approach. Masters thesis, Concordia University.

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Abstract

In this thesis, we present a statistical method for detecting influenza epidemics. First, we use a hidden Markov model with Bayesian approach to partition the influenza data into two groups, one group for the epidemic states and another one for the non-epidemic states. Then, we detect the start of the epidemic phase of the disease through introducing a warning threshold. This warning threshold is efficient in increasing the detection rates while decreasing the false alarm rates. Finally, we compare the established hidden Markov model with the traditional seasonal ARIMA model.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:Mousavinasab, Seyed Yousef
Pagination:vii, 33 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:2007
Thesis Supervisor(s):Sun, W and Khalil, Z
ID Code:975474
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:08
Last Modified:18 Jan 2018 17:40
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