Strategies for Effective Mitigation of Infectious Diseases, with Focus on COVID-19

We present a comprehensive approach to designing and optimizing infectious disease mitigation strategies, with a focus on COVID-19 and closed communities like college campuses. By integrating vaccination and routine screening, we first develop a model to evaluate the efficacy of various strategies in reducing infections, hospitalizations, and deaths on a college campus during the Fall 2021 semester. The findings emphasize the importance of customizing interventions based on factors such as initial vaccine coverage, vaccine effectiveness, compliance rates, and disease transmission dynamics. As COVID-19 variants continue to emerge, we highlight the necessity for adaptive screening strategies that account for the existing variants and differences in transmission and outcomes among population groups, such as faculty/staff, and students, based on their vaccination status and level of natural immunity. Using the Spring 2022 academic semester as a case study, we study various routine screening strategies and find that screening faculty and staff less frequently than students, and/or screening the boosted and vaccinated less frequently than the unvaccinated, may avert a higher number of infections per test compared to universal screening of the entire population at a common frequency. We also discuss key policy issues, including the need to revisit the mitigation objectives over time and determine if and when screening alone can compensate for low booster coverage. In contexts where mandates are not feasible and vaccine hesitancy is prevalent, we explore the role of voluntary vaccination compliance, supported by monetary incentives and routine screening. We introduce an optimization framework that considers the dual role of screening as both a mitigation tool and a non-monetary incentive. This framework necessitates a novel optimization model for incentive design, integrated with a utility-based decision model that accounts for resource constraints and uncertainties in community response to mitigation efforts. We establish structural properties of Pareto sets of strategies and analyze how they adjust with community characteristics, leading to key insights. Our findings offer actionable strategies for diverse communities and underscore the substantial value of tailoring mitigation efforts to community characteristics and incorporating the incentive effect of routine screening. Overall, this research provides actionable insights into the development of targeted and adaptive mitigation strategies that can be applied in diverse community settings, ensuring safe operations and effective disease control amidst evolving epidemiological challenges. The methodologies and insights from our study are poised to inform and guide the design of mitigation strategies in a variety of institution and community settings, contributing significantly to the collective efforts against infectious diseases.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Rabil, Marie Jeanne

Chair dc:contributor.committeechair

• Tunc, Sait

Committee members dc:contributor.committeemember

• Bish, Douglas R.
• Bish, Ebru K.
• Bansal, Manish
• Chen, Xi

• infectious disease
• COVID-19
• mitigation
• screening
• nudge mechanism
• chance-constrained optimization
• compartmental model
• behavioral issues

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41499

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121293