Open Data Science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge

Lozano, Miguel Angel; Garibo, O.; Piñol, E.; Rebollo, M.; Polotskaya, K.; Garcia-March, M. A.; Conejero, J. A.; Escolano, F.; Oliver, N.

doi:https://doi.org/10.1007/978-3-030-86514-6_24

Open Data Science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge

Authors: Lozano, M.A. , Garibo, O., Piñol, E., Rebollo, M., Polotskaya, K., Garcia-March, M. A., Conejero, J. A., Escolano, F. , Oliver, N.

External link: https://link.springer.com/chapter/10.1007/978-3-030-86514-6_24
Publication: Joint European Conference on Machine Learning and Knowledge Discovery in Databases p. 384-399, 2021
DOI: https://doi.org/10.1007/978-3-030-86514-6_24
PDF: Click here for the PDF paper

In this paper, 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, a four-month global competition organized by the XPRIZE Foundation. 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, the winning models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. We believe that this experience contributes to the necessary transition to more evidence-driven policy-making, particularly during a pandemic.