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dc.creatorHladish, Thomas Joseph
dc.date.accessioned2012-11-13T17:14:48Z
dc.date.available2012-11-13T17:14:48Z
dc.date.created2012-08
dc.date.issued2012-11-13
dc.date.submittedAugust 2012
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-08-5982
dc.descriptiontext
dc.description.abstractEpidemiological models traditionally make the assumption that populations are homogeneous. By relaxing that assumption, models often become more complicated, but better representations of the real world. Here we describe new computational tools for studying heterogeneous populations, and we examine consequences of two particular types of heterogeneity: that people are not all equally likely to interact, and that people are not all equally likely to become infected if exposed to a pathogen. Contact network epidemiology provides a robust and flexible paradigm for thinking about heterogeneous populations. Despite extensive mathematical and algorithmic methods, however, we lack a programming framework for working with epidemiological contact networks and for the simulation of disease transmission through such networks. We present EpiFire, a C++ applications programming interface and graphical user interface, which includes a fast and efficient library for generating, analyzing and manipulating networks. EpiFire also provides a variety of traditional and network-based epidemic simulations. Heterogeneous population structure may cause multi-wave epidemics, but urban populations are generally assumed to be too well mixed to have such structure. Multi-wave epidemics are not predicted by simple models, and are particularly problematic for public health officials deploying limited resources. Using a unique empirical interaction network for 103,000 people in Montreal, Canada, we show that large, urban populations may feature sufficient community structure to drive multi-wave dynamics, and that highly connected individuals may play an important role in whether communities are synchronized. Finally, we show that heterogeneous immunity is an important determinant of influenza epidemic size. While many epidemic models assume a homogeneously susceptible population and describe dynamics for one season, the trans-seasonal dynamics of partially immunizing diseases likely play a critical role in determining both future epidemic size and pathogen evolution. We present a multi-season network model of a population exposed to a pathogen conferring partial cross-immunity that decays over time. We fit the model to 25 years of influenza-like illness epidemic data from France using a novel Bayesian technique. Using conservative priors, we estimate important epidemiological quantities that are consistent with empirical studies.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectInfluenza
dc.subjectContact network
dc.subjectEpidemiology
dc.subjectModularity
dc.subjectApproximate bayesian computation
dc.subjectC++
dc.subjectApplications programming interface
dc.titleEpidemic dynamics in heterogeneous populations
dc.date.updated2012-11-13T17:15:10Z
dc.identifier.slug2152/ETD-UT-2012-08-5982
dc.description.departmentEcology, Evolution and Behavior
dc.type.genrethesis*
thesis.degree.departmentEcology, Evolution and Behavior
thesis.degree.disciplineEcology, Evolution and Behavior
thesis.degree.grantorUniversity of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy


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