XPRIZE Pandemic Response Challenge Competition | XPRIZE Foundation

3 min read Original article ↗

Prize Purse

$500 Thousand

Designed to help communities respond faster and smarter during a global crisis, this prize challenged teams to develop AI systems capable of forecasting COVID-19 surges and recommending targeted interventions, bringing data-driven decision-making to the front lines of the pandemic.

Impact

17 countries engaged in Phase 2 of the competition

1.99M+ hours of R&D contributed by competing teams

921M+ global media impressions generating $1.7M in earned value

Winner

Valencia IA4COVID
Partnered with the Valencian government to forecast epidemiological trends, predict COVID-19 surges and declines, and support targeted, region-specific interventions.

JSI vs COVID
Modeled COVID-19 spread with high accuracy to help local authorities plan and implement effective response measures that minimized disruption while protecting public health.

VALENCIA IA4COVID

VALENCIA IA4COVID

Valencia, Spain

This multi-disciplinary and diverse team is composed of 14 scientists, engineers, and social scientists from five universities and research centers in the Valencian Region of Spain. It is co-led by Dr. Nuria Oliver and Dr. J. Alberto Conejero. The team has worked intensively between March 2022 and April 2022 on the use of data and AI to help fight COVID-19, in collaboration with the Presidency of the Valencian Government of Spain. In terms of resources, it has been largely a volunteer, purpose-driven effort, leveraging the resources of the respective institutions. It has also counted on philanthropic contributions from technology companies and has obtained three competitive grants to further support the efforts.

  • Grand Prize Winner
  • Finalist

JSI vs COVID (Jozef Stefan Inst., Dept. of Intelligent Systems)

JSI vs COVID (Jozef Stefan Inst., Dept. of Intelligent Systems)

Ljubljana, Slovenia

We do applied research on artificial intelligence, and are particularly interested in the health domain: we interpret wearable sensor data from chronic patients, build risk prediction models, dispense health and wellbeing advice and similar. We have so far studied the impact of country properties on the early spread of COVID-19 using machine learning.

  • Finalist

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