A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases

Area of Science:

• Epidemiology
• Computational modeling
• Public health policy

Background:

• Viral propagation necessitates effective mitigation strategies.
• Understanding vaccination impacts requires detailed simulation.
• Differentiating between sterilizing and effective immunization is crucial.

Purpose of the Study:

• To present a particle-based SEIR epidemic simulator for assessing vaccination strategies.
• To model sterilizing and effective immunization outcomes.
• To enable region-specific policy measures and resource allocation.

Main Methods:

• Developed a particle-based SEIR simulator with age-structured populations.
• Incorporated modules for contact tracing and epidemiological testing.
• Calibrated the model using data from Lecco, Italy, to simulate various vaccination policies.

Main Results:

• Sterilizing, age-based vaccination strategies significantly reduce mortality.
• Prioritizing older populations for vaccination lowers overall mortality rates.
• Increased vaccination rates diminish mortality differences across simulated scenarios.

Conclusions:

• The simulator effectively evaluates vaccination strategies considering local conditions and demographics.
• Age-based, sterilizing immunization is the most effective strategy for reducing deaths.
• Targeted vaccination policies, particularly for the elderly, are vital for public health.