Optimal timing for staged vaccination campaigns: Insights from scenario tree-based stochastic optimization.

We develop a mathematical modeling framework to address the challenge in launching an effective staged vaccination campaign during a typical viral infection season to avoid an overwhelmed healthcare system for the entire season. Using the COVID-19 pandemic following its acute phase as a motivating example, our model takes into account the willingness of the public to receive vaccines, as well as the uncertainty of vaccination delivery and administration, to achieve the objective of optimizing the timing and distribution of the vaccination campaign, subject to the constraint that hospitalized cases do not exceed healthcare capacity. The integration of a dynamic transmission model with a scenario tree-based stochastic optimization framework enables the evaluation of future scenarios characterized by uncertainty in vaccination rates, contact mixing, and public adherence to safety measures. Accounting for these future scenarios facilitates the identification of strategies to dynamically adjust the timing and scale of vaccine and contact mixing during distinct phases of the viral season. Our study demonstrates that a well-timed, well-phased vaccination campaign, along with other public health interventions, can prevent overcrowding in hospitalization in a typical viral infection season.