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, calibration, and evidence-generation reported here was conducted by an interdisciplinary team recruited by the Pandemic Resilience project team via the Global Partnership on Artificial Intelligence (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 economic indicators within specified locations. The disease of interest was COVID-19 and its variants.

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Technical Report, Global Partnership on Artificial Intelligence.

@techreport{gpai:report23, title={Pandemic Resilience: Developing an AI-calibrated Ensemble of Models to Inform Decision Making}, author={GPAI}, month={December}, institution={Global Partnership on Artificial Intelligence}, type={Report}, url="http://www.cs.utexas.edu/users/ai-lab?gpai:report23", year={2023} }