Modeling Heterogeneity in Infectious Disease Systems for Inference and Monitoring

dc.contributor.advisorDeardon, Rob
dc.contributor.authorRomanescu, Razvan
dc.date.accessioned2016-09-14T17:06:47Z
dc.date.available2016-09-14T17:06:47Z
dc.date.copyright2016-07-30
dc.date.created2016-09-07
dc.date.issued2016-09-14
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameDoctor of Philosophyen_US
dc.degree.programmeMathematics and Statisticsen_US
dc.description.abstractNonhomogeneity in infectious disease spread can be described most directly via a population that is heterogeneous at the individual level. Spatial and network-based individual level models (ILMs) of Deardon et al. (2010) are two classes of models that describe such a population, and that have been successfully applied to human, animal, and plant diseases. ILMs allow the use of covariate information at the individual level (e.g. spatial location, number of contacts, etc.); the cost for this level of detail, however, is the computational time they take to be fitted to data. This thesis considers of a selection of topics on inference and surveillance for such models. One general theme is the reduction in computational burden associated with IBMs via aggregation and mathematical approximations. First, we consider a spatial ILM adapted to a system with two competing pathogens. A data-intensive model is first proposed for inference within a Bayesian MCMC framework, and then approximated by a faster model that utilizes aggregated data. The second topic develops an inference methodology for a network model that has a given degree distribution. Following results from Volz (2008) and Miller (2011), we develop an analytic likelihood for count data, and fit this to single and multi-season epidemics. Thirdly, we employ the same network model to test various surveillance systems. Using simulation, we derive distributional results for alarms meant to determine the start of seasonal epidemics, and compare their performance. All of our methods are tested on simulated data, and in addition, we use real influenza data sets to illustrate the methods related to network models.en_US
dc.description.sponsorshipOntario Ministry of Agriculture, Food and Rural Affairs
dc.description.sponsorshipHighly Qualified Person Scholarship
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada
dc.description.sponsorshipCFI grant, "Centre for Public Health and Zoonoses"
dc.identifier.urihttp://hdl.handle.net/10214/10007
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectinfectious disease modelsen_US
dc.subjectsurveillanceen_US
dc.subjectinfluenzaen_US
dc.subjectidentifiability in inferenceen_US
dc.subjectstatistical computingen_US
dc.subjectnetwork heterogeneityen_US
dc.titleModeling Heterogeneity in Infectious Disease Systems for Inference and Monitoringen_US
dc.typeThesisen_US

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