Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10045/119336
| Title: | Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge |
|---|---|
| Authors: | Lozano, Miguel Angel | Garibo i Orts, Òscar | Piñol, Eloy | Rebollo, Miguel | Polotskaya, Kristina | Garcia-March, Miguel Angel | Conejero, J. Alberto | Escolano, Francisco | Oliver, Nuria |
| Research Group/s: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
| Center, Department or Service: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
| Keywords: | SARS-CoV-2 | Computational epidemiology | Data science for public health | Recurrent neural networks | Non-pharmaceutical interventions | Pareto-front optimization |
| Knowledge Area: | Ciencia de la Computación e Inteligencia Artificial |
| Issue Date: | 10-Sep-2021 |
| Publisher: | Springer, Cham |
| Citation: | Lozano M.A. et al. (2021) Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge. In: Dong Y., Kourtellis N., Hammer B., Lozano J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science, vol 12978. Springer, Cham. https://doi.org/10.1007/978-3-030-86514-6_24 |
| 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. |
| Sponsor: | The authors have been partially supported by grants FONDOS SUPERA COVID-19 Santander-CRUE (CD4COVID19 2020–2021), Fundación BBVA for SARS-CoV-2 research (IA4COVID19 2020-2022) and the Valencian Government. We thank the University of Alicante’s Institute for Computer Research for their support with computing resources, co-financed by the European Union and ERDF funds through IDIFEDER/2020/003. MAGM acknowledges funding from MEFP Beatriz Galindo program (BEAGAL18/00203). |
| URI: | http://hdl.handle.net/10045/119336 |
| ISBN: | 978-3-030-86513-9 | 978-3-030-86514-6 |
| DOI: | 10.1007/978-3-030-86514-6_24 |
| Language: | eng |
| Type: | info:eu-repo/semantics/conferenceObject |
| Rights: | © Springer Nature Switzerland AG 2021 |
| Peer Review: | si |
| Publisher version: | https://doi.org/10.1007/978-3-030-86514-6_24 |
| Appears in Collections: | INV - MVRLab - Comunicaciones a Congresos, Conferencias, etc. |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
 Lozano_etal_2021_ECML-PKDD_final.pdf | Versión final (acceso restringido) | 1,07 MB | Adobe PDF | Open Request a copy |
 Lozano_etal_2021_ECML-PKDD_preprint.pdf | Preprint (acceso abierto) | 1,45 MB | Adobe PDF | Open Preview |
See citations in Google Scholar
Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.