Multi-feature SEIR model for epidemic analysis and vaccine prioritization

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Traditional SEIR models simplify population dynamics.
• Existing models lack individual heterogeneity in disease sensitivity and social contact rates.
• COVID-19 pandemic highlighted limitations of current infectious disease models.

Purpose of the Study:

• Develop a novel multi-feature SEIR model.
• Incorporate population heterogeneity in health conditions and social activity.
• Enhance predictive accuracy for infectious disease spread.

Main Methods:

• Developed a multi-feature SEIR model accounting for disease sensitivity and contact rates.
• Validated the model using COVID-19 case data from Allegheny County, PA, and Hamilton County, OH.
• Formulated optimization problems for vaccine prioritization strategies.

Main Results:

• The multi-feature SEIR model demonstrated superior predictive accuracy compared to traditional SEIR models.
• Evaluated and compared various vaccine prioritization strategies through extensive simulations.
• Identified effective vaccine distribution strategies for heterogeneous populations.

Conclusions:

• The multi-feature SEIR model provides a more accurate representation of infectious disease dynamics.
• This enhanced model can significantly inform public health interventions and pandemic preparedness.
• The model facilitates tailored vaccine distribution strategies for diverse populations.