Amidst the global challenge posed by the COVID-19 pandemic in early 2020, countries implemented containment measures to curb the virus's spread, leading to widespread disruptions. Cornell University faced the dilemma of whether to reopen its campus for in-person instruction in the fall of 2020 among uncertainties about disease transmission. Leveraging operations research methods such as stochastic modeling, simulation, and statistics, the Cornell COVID-19 mathematical modeling team, of which I was a member, arrived at a somewhat surprising conclusion that prompted the university's decision to reopen: In-person instruction would offer greater safety to students than virtual classes. Over the subsequent two years, the team continued to study various aspects of the virus and disease, providing recommendations on travel policies, vaccine mandates, and other measures, in response to emerging variants and evolving social distancing protocols. Building on these efforts, this dissertation presents a series of research projects that deepen our understanding of COVID-19 and advance strategies for combating infectious diseases. These projects include theoretical exploration of group testing, modeling analysis of gateway testing protocols, retrospective calibration of stochastic simulation models, and statistical analysis of booster vaccination effectiveness during Omicron outbreaks. Together, they highlight the critical role of operations research in informing pandemic response and broader public health decision-making.