Optimizing vaccination strategies under uncertainty to prevent epidemics

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Future infectious disease epidemics pose significant public health challenges.
• Heterogeneous populations and uncertain disease dynamics complicate optimal vaccination strategies.
• Existing models may not fully capture the complexities of real-world disease spread and intervention effectiveness.

Purpose of the Study:

• To develop a data-driven model for optimal vaccination strategies in multi-community settings.
• To incorporate uncertainty in social mixing, disease transmission, and vaccine efficacy.
• To enable public health policy analysis for epidemic preparedness.

Main Methods:

• Derivation of an integrated chance constraints stochastic programming (ICC-SP) disease spread model.
• Utilizing readily available data: census demographics, age-related susceptibility/infectivity, virus variants, and vaccine efficacy.
• Defining vaccination strategies based on proportion, household-type, age-group, and intervention level.

Main Results:

• The ICC-SP model effectively determines optimal vaccination strategies under uncertainty.
• Vaccination strategies must be combined with specific intervention levels for epidemic control.
• Vaccine efficacy criteria and intervention levels influence the proportion of the population needing vaccination.
• Optimal strategies prioritize vaccinating high-susceptibility and high-infectivity household and age groups.

Conclusions:

• The data-driven ICC-SP model provides a robust framework for epidemic preparedness.
• Effective epidemic prevention requires a combination of targeted vaccination and public health interventions.
• The model's flexibility allows for risk-based policy analysis and adaptation to varying epidemic scenarios.