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Permanent link (DOI): https://doi.org/10.7939/R3XP6VB7M

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Predicting the Peak of Influenza Cases by Geograhic Zones in Alberta Open Access

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Other title
Subject/Keyword
Influenza peak prediction
influenza model
influenza forecasting models
contact mixing matrix
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Amissah, Jeannette
Supervisor and department
Pass, Brendan (Mathematical and Statistical Science)
Li, Michael Y (Mathematical and Statistical Science)
Examining committee member and department
Muldowney, James (Mathematical and Statistical Science)
Han, Bin (Mathematical and Statistical Science)
Pass, Brendan (Mathematical and Statistical Science)
Li, Michael Y (Mathematical and Statistical Science)
Department
Department of Mathematical and Statistical Sciences
Specialization
Biostatistics
Date accepted
2016-09-19T14:14:08Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
Influenza or the ‘flu’ can affect people from all walks of life. The burden from influenza epidemics puts tremendous pressure on health services and other resources during a flu season. To better prepare for an incoming flu season, clinicians, health services and policy makers have a great interest in developing the capacity to predict the timing of the peak of an influenza epidemic based on identified cases in the early phase of the season. The objective of this thesis is to formulate an influenza model that can predict the week when the laboratory confirmed influenza cases peak for different geographical zones in the Province of Alberta: Edmonton, Calgary, North, Central and South zones. A Kermarck-McKendrick type compartmental model that comprises of the susceptible-infected (SI) compartments for three age groups (0-18 years, 19-64 years, and 64 years and over) is proposed. Contact mixing matrix among the age groups is computed. Estimates of model parameters are obtained by fitting the model to past influenza data (Year 2014-2015) from Alberta Health, using the nonlinear least squares method and the Mathematica software. The 95% confidence intervals for model parameters are obtained using the Markov Chain Monte Carlo method and then used for uncertainty analysis of our model predictions. Our model predictions for the peak time have shown a good agreement with past data for all Alberta zones. The findings in this thesis provide the groundwork and insight valuable for further development of influenza forecasting models.
Language
English
DOI
doi:10.7939/R3XP6VB7M
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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