19 – Pandemic resilience case studies of an AI-calibrated ensemble of models to inform decision-making

This report from Global Partnership on Artificial Intelligence (GPAI)’s Pandemic Resilience project follows its 2023 report and is focused on practically implementing the concepts pre- viously developed by the project team. Indeed, the 2023 report laid the foundation for this research while presenting recommendations on various approaches that aligned pandemic modelling with responsible Artificial Intelligence (AI). The 2023 report showcased a calibra- tion framework approach and an ensemble modelling concept, focusing on the added value and pertinence of both consistent calibration and ensembling; that is, ensuring models are consistent in shared parameter values while using the strengths of different models and creat- ing a digital “task force”. The combination of the calibration framework and ensemble model encourages and enables modellers from different locations and backgrounds to work to- gether by using standardised versions of their work. Although there has been substantial modelling activity of Non-Pharmaceutical Interventions (NPIs) for COVID-19, this activity has been fragmented across different countries, with mixed access and sharing of data and models. This report documents a prototype calibration frame- work – based on a multi-objective genetic algorithm – that simultaneously calibrates multiple models across different locations and ensures consistent parameter values across models. The resulting, calibrated models are then combined using an ensemble modelling concept that provides more accurate model results than any of the models do individually. Hence, consistent models for multiple locations are created and can be shared easily with these lo- cations. In addition, diverse perspectives from the models can provide more accurate results for each location through the ensemble model.