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

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12978))

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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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Notes

  1. 1.

    https://www.dw.com/en/coronavirus-global-gdp-to-sink-by-22-trillion-over-covid-says-imf/a-56349323.

  2. 2.

    https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker.

  3. 3.

    Tonga, Malta, Turkmenistan and Virgin Islands- were not considered due to lack of reliable data.

  4. 4.

    https://github.com/malozano/valencia-ia4covid-xprize/raw/master/docs/supplementary.pdf.

  5. 5.

    https://tinyurl.com/cjstz4yc.

  6. 6.

    We selected amongst the most affected countries and regions across the globe.

  7. 7.

    See https://en.wikipedia.org/wiki/Knapsack_problem..

  8. 8.

    https://public.tableau.com/app/profile/kristina.p8284/viz/PrescriptionsWeb/Visualize.

  9. 9.

    https://github.com/malozano/valencia-ia4covid-xprize.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. University of Alicante, Alicante, Spain

    Miguel Angel Lozano & Francisco Escolano

  2. IUMPA and VRAIN, Universitat Politècnica de València, València, Spain

    Òscar Garibo i Orts, Eloy Piñol, Miguel Rebollo, Miguel Angel Garcia-March & J. Alberto Conejero

  3. University Miguel Hernández, Elche, Spain

    Kristina Polotskaya

  4. ELLIS (European Lab. for Learning and Intelligent Systems) Unit Alicante, Alicante, Spain

    Nuria Oliver

Authors
  1. Miguel Angel Lozano
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  2. Òscar Garibo i Orts
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  3. Eloy Piñol
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  4. Miguel Rebollo
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  5. Kristina Polotskaya
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  6. Miguel Angel Garcia-March
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  7. J. Alberto Conejero
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  8. Francisco Escolano
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  9. Nuria Oliver
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Corresponding author

Correspondence to Miguel Angel Lozano .

Editor information

Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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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

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

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