Mathematical and decision analytic modeling of interventions to mitigate infectious diseases from endemic to pandemic

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Infectious diseases are responsible for millions of deaths globally each year. Difficult decisions must be made about how to allocate resources efficiently to treat infection, prevent transmission, and save lives while also mitigating the negative impacts of an outbreak. Mathematical and decision analytic modeling help inform decision makers about the most effective and most cost-effective interventions to prepare for and respond to infectious disease outbreaks. In this dissertation, I present novel applications of a variety of model types to assess interventions for recent disease outbreaks. I develop cutting edge methodological improvements for decision making amid an outbreak and provide critical evidence on how model structure could impact predicted intervention effectiveness. Specifically, I assess the cost-effectiveness of plague control interventions for the 2017 plague outbreak in Madagascar including expanded access to antibiotic treatment with doxycycline, mass distribution of doxycycline prophylaxis, and mass distribution of malathion -- alone and in combination. I focus on the trade-off between intervention timing and coverage levels as measured in terms of costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. Subsequently, I provide a novel framework for rapid decision making and advancing methods for meta-modeling in the infectious disease context. I derive the simple decision rule using a compartmental model framework and net monetary benefit to assess cost-effectiveness and compare the performance of the simple decision rule to machine learning metamodels. During the COVID-19 pandemic, I estimated the impact of various mitigation strategies on COVID-19 transmission in a large U.S. urban jail. I develop a stochastic dynamic transmission model and use this model to estimate the effectiveness of three interventions undertaken by the jail -- depopulation, increased single celling, and asymptomatic testing -- in reducing the spread of COVID-19. Finally, I explicitly address how the choice of model can influence estimates of intervention effectiveness in the short and long term for an endemic disease. I consider four disease models with different permutations of socially connected network vs. unstructured contact (mass-action mixing) model and heterogeneous vs. homogeneous disease risk. I calibrate the models to the same long-term equilibrium disease prevalence and consider a simple intervention with varying levels of coverage and efficacy. For each type of model, I measure the rate of prevalence decline post-intervention, the long-term equilibrium prevalence, and the long-term effective reproduction ratio at equilibrium.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English


Author Malloy, Giovanni Sean Paul
Degree supervisor Brandeau, Margaret L
Thesis advisor Brandeau, Margaret L
Thesis advisor Andrews, Jason
Thesis advisor Goldhaber-Fiebert, Jeremy D
Degree committee member Andrews, Jason
Degree committee member Goldhaber-Fiebert, Jeremy D
Associated with Stanford University, Department of Management Science and Engineering


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Giovanni S. P. Malloy.
Note Submitted to the Department of Management Science and Engineering.
Thesis Thesis Ph.D. Stanford University 2022.

Access conditions

© 2022 by Giovanni Sean Paul Malloy
This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).

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