COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Vaccine hesitancy poses a significant challenge to achieving herd immunity and controlling the COVID-19 pandemic.
• It undermines the effectiveness of vaccination campaigns globally, presenting an acute public health concern.

Purpose of the Study:

• To develop and present an open-source, particle-based COVID-19 simulator.
• To incorporate a module that accounts for population vaccine hesitancy in epidemic modeling.

Main Methods:

• Utilized a particle-based simulation approach for COVID-19 dynamics.
• Integrated a vaccination module to model varying levels of vaccine hesitancy.
• Conducted extensive simulations for the province of Lecco, Italy to validate the model.

Main Results:

• The simulation results demonstrate that a high vaccination rate, coupled with low vaccine hesitancy, significantly accelerates epidemic suppression.
• The findings highlight the critical interplay between vaccination coverage and population attitudes towards vaccines in controlling infectious disease outbreaks.

Conclusions:

• The developed simulator is a valuable tool for predicting the impact of vaccination strategies on epidemic trajectories.
• Addressing vaccine hesitancy is crucial for maximizing the public health benefits of vaccination campaigns and achieving faster disease control.