Using neural networks to calibrate agent based models enables improved regional evidence for vaccine strategy and policy.

Distribution and administration strategy are critical to successful population immunization efforts. Agent-based modeling (ABM) can reflect the complexity of real-world populations and can experimentally evaluate vaccine strategy and policy. However, ABMs historically have been limited in their time-to-development, long runtime, and difficulty calibrating.

Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study.

Objective: To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.

Design: Simulation agent based modeling study.

Setting: Simulated population based on real world US county.