Pandemic Resilience Developing an AI-calibrated ensemble of models to inform decision making

This report explores the use of ensemble modeling of infectious diseases to enable better data-driven decisions and policies related to public health threats in the face of uncertainty. It demonstrates how Artificial Intelligence (AI)-driven techniques can automatically calibrate ensemble models consistently across multiple locations and models. The ensembling, cali bration, and evidence-generation reported here was conducted by an interdisciplinary team recruited by the Pandemic Resilience project team via the Global Partnership on Artificial In telligence (GPAI) Pandemic Resilience living repository. This diverse team co-developed and tested a collaborative ensemble model that assesses the level of use of Non-Pharmaceutical Interventions (NPIs) and predicts the consequent effect on both epidemic spread and eco nomic indicators within specified locations. The disease of interest was COVID-19 and its variants. The development of the ensemble model was undertaken in five main phases from June 2022 to October 2023: 1. Definition of a standardized set of inputs and outputs; 2. Adapta tion of individual models to the standard; 3. Development of a calibration framework for the ensemble; 4. Deployment and testing of the ensemble across different different locations; 5. Automated calibration of the ensemble using a Genetic Algorithm (GA) metaheuristic op timization approach. Having constructed and tested the ensemble, the study team has prepared this report to share key findings about the use of such models and communicate key recommendations for governments and policymakers about their development and support: