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2021 | OriginalPaper | Chapter

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

Authors : Miguel Angel Lozano, Òscar Garibo i Orts, Eloy Piñol, Miguel Rebollo, Kristina Polotskaya, Miguel Angel Garcia-March, J. Alberto Conejero, Francisco Escolano, Nuria Oliver

Published in: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

Publisher: Springer International Publishing

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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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Footnotes
3
Tonga, Malta, Turkmenistan and Virgin Islands- were not considered due to lack of reliable data.
 
6
We selected amongst the most affected countries and regions across the globe.
 
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Metadata
Title
Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge
Authors
Miguel Angel Lozano
Òscar Garibo i Orts
Eloy Piñol
Miguel Rebollo
Kristina Polotskaya
Miguel Angel Garcia-March
J. Alberto Conejero
Francisco Escolano
Nuria Oliver
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

Print ISBN: 978-3-030-86513-9

Electronic ISBN: 978-3-030-86514-6

Copyright Year: 2021

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

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