Publication Date

Spring 5-16-2026

Abstract

Emerging infectious diseases are a persistent public health threat that challenge deterministic, mechanistic modeling approaches. Because outbreaks start with few infected hosts, their dynamics are highly stochastic, making deterministic methods, e.g. ordinary differential equations, unsuitable for capturing transmission dynamics. Instead, stochastic models are used, such as Markov chain models, but these present their own challenges: to infer uncertainty in a quantity of interest, we need to sample the model’s distribution many times, introducing an additional source of noise that makes the inference more difficult and computationally expensive. Here, we show how novel tools in global sensitivity analysis can be used to efficiently separate these two channels of noise, allowing us to make rigorous statistical inferences about processes important for disease emergence. We demonstrate the utility of these tools across different modeling tasks in disease ecology and epidemiology, from inferring mechanism of disease emergence to model calibration and feature selection.

Degree Name

Mathematics

Level of Degree

Doctoral

Department Name

Mathematics & Statistics

First Committee Member (Chair)

Helen J Wearing

Second Committee Member

Owen L Lewis

Third Committee Member

Jacob B Schroder

Fourth Committee Member

Emma E Goldberg

Fifth Committee Member

Davorka Gulisija

Language

English

Keywords

infectious disease modeling, uncertainty quantification, emergence, stochastic

Document Type

Dissertation

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Available for download on Tuesday, May 16, 2028

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