| Autor: |
Miguel Angel Lozano, Òscar Garibo-i-Orts, Eloy Piñol, Miguel Rebollo, Kristina Polotskaya, Miguel Ángel García-March, J. Alberto Conejero, Francisco Escolano, Nuria Oliver |
| Rok vydání: |
2022 |
| Zdroj: |
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. |
| DOI: |
10.24963/ijcai.2022/740 |
| Popis: |
We describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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