Agent-based models (ABMs) are powerful for simulating detailed infectious disease dynamics, but they are computationally intensive, limiting parameter inference. We present a Bayesian history matching approach with Gaussian process emulation to efficiently calibrate an ABM of SARS-CoV-2 transmission in Norway, enabling inference of numerous parameters and reducing computational costs.

We apply this technique to model the emergence and subsequent winter wave of the Omicron BA.1/BA.2 variants in Norway from September 2021 to April 2022. By fitting age-specific hospitalisation data, we infer 15 key parameters, including age-specific susceptibility, variant transmissibility, and the infection-hospitalisation ratio. The calibrated model accurately reproduces epidemic curves and provides insights into Omicron’s spread.

We estimate that around 60% of the Norwegian population was infected with Omicron BA.1/BA.2 during the 2021/2022 winter wave. The model suggests the variant circulated in Norway before its detection in South Africa in late November 2021. Our findings indicate that Omicron’s transmission advantage over Delta was primarily due to immune evasion rather than a substantial increase in intrinsic transmissibility. Counterfactual scenarios reveal the critical role of the November 2021 booster vaccine rollout in mitigating the ensuing winter wave, indicating near-optimal timing for balancing immunisation and waning. Our estimates also suggest that the December 2021 interventions had limited impact beyond altering general mobility.

This study demonstrates the power of combining ABMs with advanced calibration to derive detailed epidemiological insights from limited data. Efficiently fitting parameters enables comprehensive epidemic modelling. Emulator-based calibration greatly enhances ABMs’ utility for retrospective analysis and real-time decision support during outbreaks, providing a valuable tool for public health planning and response.