ML Anthology

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

Miguel Angel Lozano, Òscar Garibo i Orts, Eloy Piñol, Miguel Rebollo, Kristina Polotskaya, Miguel Ángel García-March, J. Alberto Conejero, Francisco Escolano, Nuria Oliver
ECML-PKDD 2021 pp. 384-399
doi:10.1007/978-3-030-86514-6_24 /ecmlpkdd/2021/lozano2021ecmlpkdd-open/

Abstract

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.

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Cite

Text

Lozano et al. "Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86514-6_24

Markdown

[Lozano et al. "Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/lozano2021ecmlpkdd-open/) doi:10.1007/978-3-030-86514-6_24

BibTeX

@inproceedings{lozano2021ecmlpkdd-open,
  title     = {{Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge}},
  author    = {Lozano, Miguel Angel and Orts, Òscar Garibo i and Piñol, Eloy and Rebollo, Miguel and Polotskaya, Kristina and García-March, Miguel Ángel and Conejero, J. Alberto and Escolano, Francisco and Oliver, Nuria},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2021},
  pages     = {384-399},
  doi       = {10.1007/978-3-030-86514-6_24},
  url       = {https://mlanthology.org/ecmlpkdd/2021/lozano2021ecmlpkdd-open/}
}