Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616878
Title: Spatio-temporal modelling of dengue fever in Zulia state, Venezuela
Author: Cabrera, Maritza
ISNI:       0000 0004 5348 0634
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2014
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Abstract:
Over half of the world's population are at risk of infection from dengue fever (Guha-Sapir 2005). This viral disease is transmitted by the female Aedes aegypti mosquito and is the major source of human death in the world when compared with any other vector borne disease (Gubler1998a). The first important epidemic of dengue haemorrhagic fever (DHF) in America was reported in Cuba in 1981 and subsequently in Venezuela during 1989 and 1990 (Oletta2006, Brightmer1998). There has been a trend of increased incidence in many Central and South American countries since 1990 - Brazil, Venezuela, Honduras and Mexico (SanMartin2010) with Venezuela having the highest number of cases of DHF. The urgent need for more effective public health measures to combat this disease in Venezuela drove the decision to undertake the work described in this dissertation. Spatio-Temporal modelling has been developed for the prediction of the occurrence of dengue fever in Zulia state, Venezuela. A systematic approach has been adopted to validate this tool. At the first stage of the analysis an exploratory study was performed to underline the most significant features of the dynamics of incidence rates of dengue fever from 2002 to 2008. In the second stage a Generalized Linear Model (GLM) approach was used in the form of Negative Binomial Generalized Linear Mixed model (GLMM) to compare Relative Risk (RR) across exposure groups by age and sex, using an epidemiological dataset covering the whole of Zulia State, Venezuela. This approach used both a frequentist and a Bayesian perspective for comparative purposes of both outcomes and methodologies. Finally a Spatio-Temporal model was constructed based on Generalized Additive Mixed model (GAMM) framework because the earlier analysis identified a complex association between covariates and response variables. This GAMM structure was further developed so that it could be used to help predict future outbreaks of the disease in Zulia state with a good degree of accuracy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.616878  DOI: Not available
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