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Optimal timing for staged vaccination campaigns: Insights from scenario tree-based stochastic optimization
Farah Al Hashimi1,2, Shengyuan Chen2, Jianhong Wu1,2
1Laboratory for Industrial and Applied Mathematics (LIAM), 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
Infectious Disease Modelling
|July 28, 2026
Summary
A mathematical model optimizes staged vaccination campaigns to prevent healthcare system overload during viral seasons. Strategic timing and distribution, considering public willingness and delivery uncertainties, are key to managing hospital capacity.
Area of Science:
- Mathematical modeling
- Epidemiology
- Public health
Background:
- Viral infection seasons pose challenges to healthcare systems.
- The COVID-19 pandemic highlighted the need for effective vaccination strategies.
- Overwhelmed healthcare systems can result from unmanaged infection surges.
Purpose of the Study:
- To develop a mathematical framework for optimizing staged vaccination campaigns.
- To prevent healthcare system overload during typical viral seasons.
- To inform dynamic adjustments in vaccination and intervention strategies.
Main Methods:
- Developed a mathematical modeling framework integrating dynamic transmission models.
- Utilized a scenario tree-based stochastic optimization approach.
- Incorporated public willingness, vaccination delivery uncertainty, and adherence to safety measures.
Main Results:
- The model optimizes vaccination timing and distribution to manage hospitalized cases.
- Identified strategies for dynamically adjusting interventions based on future scenarios.
- Demonstrated that phased vaccination campaigns can prevent healthcare overcrowding.
Conclusions:
- A well-timed, phased vaccination campaign is crucial for managing viral seasons.
- Mathematical modeling provides a framework for optimizing public health interventions.
- Dynamic adjustment of vaccination and non-pharmaceutical interventions can enhance healthcare system resilience.
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