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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01n870zt74n
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dc.contributor.advisorGrenfell, Bryan T-
dc.contributor.advisorMetcalf, C. Jessica E-
dc.contributor.authorKorevaar, Hannah Michelle-
dc.contributor.otherPopulation Studies Department-
dc.date.accessioned2020-07-13T03:33:15Z-
dc.date.available2020-07-13T03:33:15Z-
dc.date.issued2020-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01n870zt74n-
dc.description.abstractInfectious diseases remain a significant cause of death worldwide, in spite of advances in medical technology and treatment in the last century. The life cycles of infectious diseases depend on host populations producing hospitable conditions for survival and proliferation. Secular demographic events such as births, local contact rates and regional migration are necessary for the persistence of acute immunizing infections. Each chapter uses measles incidence data from pre-vaccination England and Wales to investigate the relationship between disease dynamics and demographic events. In the first chapter, I use the time-series susceptible-infected-recovered (TSIR) model to investigate transmission rates and measles persistence in urban and rural areas. I use a matched-pair analysis to separate the influence of space and population size to isolate the difference in disease dynamics between urban and rural areas. I find that population size, more than population density, influences the size and persistence of outbreaks; however, there is some evidence that population density may impact the transmission of measles. In chapter two, I challenge previous findings that transmission of measles is uncorrelated with population size. I also address the potential of population-correlated bias in TSIR estimates. In this chapter I leverage high volume stochastic simulations in order to determine if biases in estimates of transmission scale with population size. The results indicate that, in general, large populations have higher transmission rates and bias is greatest for small communities where outbreaks are highly stochastic. In the final chapter, I expand on previous frequency domain results by introducing tensor decomposition as a method for analyzing multiple oscillatory time-series simultaneously. Using this method of dimensionality reduction I am able to extract the dominant periodic signals in measles incidence data. I use this descriptive technique to verify a previous finding: the baby boom surge in births in England and Wales resulted in larger annual peaks in incidence. Together these results highlight the close relationship between demography and infectious disease dynamics. Furthermore, they demonstrate the importance of increasing and maintaining vaccination coverage for measles, particularly in an increasingly urban world where local populations and population density continue to rise.-
dc.language.isoen-
dc.publisherPrinceton, NJ : Princeton University-
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=http://catalog.princeton.edu> catalog.princeton.edu </a>-
dc.subjectInfectious Disease Modeling-
dc.subjectMeasles-
dc.subjectSignal Processing-
dc.subjectSpatial Analysis-
dc.subject.classificationDemography-
dc.subject.classificationEcology-
dc.titleSpatial Demography and the Epidemiology of Measles-
dc.typeAcademic dissertations (Ph.D.)-
Appears in Collections:Population Studies

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