Publication Date

Fall 12-16-2016

Abstract

Dengue virus is a mosquito-borne multi-serotype disease whose dynamics are not precisely understood despite half of the world’s human population being at risk of infection. A recent dataset of dengue case reports from an isolated Amazonian city— Iquitos, Peru—provides a unique opportunity to assess dengue dynamics in a simpli- fied setting. Ten years of clinical surveillance data reveal a specific pattern: two novel serotypes, in turn, invaded and exclusively dominated incidence over several seasonal cycles, despite limited intra-annual variation in climate conditions. Together with mechanistic mathematical models, these data can provide an improved understand- ing of the nonlinear interactions between the environmental and biological factors underlying dengue transmission as well as aid in the prediction of future epidemics. To examine the drivers of dengue in Iquitos we develop several stochastic discrete- time models and use likelihood-based plug-and-play inference techniques to explore potential factors that may explain the seasonal transmission pattern. By including climate-informed variables and accounting for known vector control measures in our model, we illustrate scenarios that can replicate the observed data and uncover the contribution of previously overlooked factors, such as the role of disease importation from human population migration. We discuss the implications of these results for understanding dengue dynamics in other endemic settings.

Degree Name

Mathematics

Level of Degree

Masters

Department Name

Mathematics & Statistics

First Committee Member (Chair)

Helen Wearing

Second Committee Member

Yan Lu

Third Committee Member

Daniel Appelo

Keywords

dengue, MIF, iterated filtering, POMP, Iquitos, maximum likelihood

Document Type

Thesis

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