Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Alex Savachkin

Keywords

Antiviral Agent, Computer Simulation, Design of Experiments, Disease Outbreaks, Dynamic Programming

Abstract

Public health data show the tremendous economic and societal impact of pandemic influenza in the past. Currently, the welfare of society is threatened by the lack of planning to ensure an adequate response to a pandemic. This preparation is difficult because the characteristics of the virus that would cause the pandemic are unknown, but primarily because the response requires tools to support decision-making based on scientific methods. The response to the next pandemic influenza will likely include extensive use of antiviral drugs, which will create an unprecedented selective pressure for the emergence of antiviral resistant strains. Nevertheless, the literature has insufficient exhaustive models to simulate the spread and mitigation of pandemic influenza, including infection by an antiviral resistant strain.

We are building a large-scale simulation optimization framework for development of dynamic antiviral strategies including treatment of symptomatic cases and chemoprophylaxis of pre- and post-exposure cases. The model considers an oseltamivir-sensitive strain and a resistant strain with low/high fitness cost, induced by the use of the several antiviral measures. The mitigation strategies incorporate age/immunitybased risk groups for treatment and pre-/post-exposure chemoprophylaxis, and duration of pre-exposure chemoprophylaxis. The model is tested on a hypothetical region in Florida, U.S., involving more than one million people. The analysis is conducted under different virus transmissibility and severity scenarios, varying intensity of non-pharmaceutical interventions, measuring the levels of antiviral stockpile availability. The model is intended to support pandemic preparedness and response policy making.

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