Optimizing Spatial Allocation of COVID-19 Vaccine by Agent-Based Spatiotemporal Simulations

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• COVID-19 vaccine allocation is a critical challenge.
• Existing strategies often overlook spatial factors in disease transmission.
• Understanding spatial heterogeneity is key to optimizing vaccine distribution.

Purpose of the Study:

• To examine spatial COVID-19 transmission patterns.
• To optimize vaccine distribution strategies using spatial prioritization.
• To identify the most effective vaccine allocation approach.

Main Methods:

• Developed an integrated spatial model combining agent-based modeling and SEIR.
• Simulated COVID-19 transmission using real-world mobile phone user data.
• Evaluated seven scenarios, including different vaccination strategies and coverage levels.

Main Results:

• Herd immunity exhibits significant spatial heterogeneity.
• The space & age vaccination strategy proved most efficient.
• This strategy reduced attack rates by 7.7% and delayed herd immunity by 44 days compared to random allocation at 20% uptake.

Conclusions:

• Vaccine intervention strategies must incorporate spatial considerations.
• The space & age strategy requires 30%-40% vaccine coverage for epidemic control, versus 60%-70% for random strategies.
• Spatialized vaccine distribution significantly enhances usability and effectiveness.