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Time series analysis of count data with an application to the incidence of cholera

Includes bibliographical references (leaves 88-93). / This dissertation comprises a study into the application of count data time series models to weekly counts of cholera cases that have been recorded in Beira, Mozambique. The study specifically looks at two classes of time series models for count data, namely observation-driven and parameter-driven, and two models from each of these classes are investigated. The autoregressive conditional Poisson (ACP) and double autoregressive conditional Poisson (DACP) are considered under the observation-driven class, while the parameter-driven models used are the Poisson-gamma and stochastic autoregressive mean (SAM) model. An in-depth case study of the cholera counts is presented in which the four selected count data time series models are compared. In addition the time series models are compared to static Poisson and negative binomial regression, thereby indicating the benefits gained in using count data time series models when the counts exhibit serial correlation. In the process of comparing the models, the effect of environmental drivers on the outbreaks of cholera are observed and discussed.
Date January 2011
CreatorsHolloway, Jennifer Patricia
ContributorsHaines, Linda, Leask, Kerry, Elphinstone, Chris
PublisherUniversity of Cape Town, Faculty of Science, Department of Statistical Sciences
Source SetsSouth African National ETD Portal
Detected LanguageEnglish
TypeMaster Thesis, Masters, MSc

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