Graduation Year

2017

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Tapas K. Das, Ph.D.

Committee Member

Alex Savachkin, Ph.D.

Committee Member

Mingyang Li, Ph.D.

Committee Member

Yicheng Tu, Ph.D.

Committee Member

Ricardo Izurieta, M.D.

Keywords

Agent Based Simulation, Non-pharmaceutical intervention, Community Resilience, Time of Infection

Abstract

This dissertation presents a collection of manuscripts that describe development of models and model implementation to analyze impact of potential A(H7N9) pandemic influenza outbreak in the U.S. Though this virus is still only animal-to-human transmittable, it has potential to become human-to-human transmittable and trigger a pandemic. This work is motivated by the negative impact on human lives that this virus has already caused in China, and is intended to support public health officials in preparing to protect U.S. population from a potential outbreak of pandemic scale.

An agent-based (AB) simulation model is used to replicate the social dynamics of the contacts between the infected and the susceptible individuals. The model updates at the end of each day the status of all individuals by estimating the infection probabilities. This considers the contact process and the contagiousness of the infected individuals given by the disease natural history of the virus.

The model is implemented on sample outbreak scenarios in selected regions in the U.S. The sampling results are used to estimate disease burden for the whole U.S. The results are also used to examine the impact of various virus strengths as well as the efficacy of different intervention strategies in mitigating a pandemic burden.

This dissertation, also characterizes the infection time during a A(H7N9) influenza pandemic. Continuous distributions including exponential, Weibull, and lognormal are considered as possible candidates to model the infection time. Based on the negative likelihood, lognormal distribution provides the best fit. Such characterization is important, as many critical questions about the pandemic impact can be answered from using the distribution.

Finally, the dissertation focuses on assessing community preparedness to deal with pandemic outbreaks using resilience as a measure. Resilience considers the ability to recover quickly from a pandemic outbreak and is defined as a function of the percentage of healthy population at any time.

The analysis, estimations, and metrics presented in this dissertation are new contributions to the literature and they offer helpful perspectives for the public health decision makers in preparing for a potential threat of A(H7N9) pandemic.

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