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A machine-learning algorithm to target COVID testing of travellers

Optimizing the testing of incoming travellers for COVID-19 involves predicting those who are most likely to test positive. A machine-learning algorithm for targeted testing has been implemented at the Greek border.
By
  1. Ziad Obermeyer

    1. Ziad Obermeyer is in the Division of Health Policy and Management, School of Public Health, University of California, Berkeley, Berkeley, California 94720, USA.

It seems an obvious combination: machine learning and the fight against COVID-19. And yet, despite intense interest and increasing availability of large data sets, success stories of such combinations are few and far between. Writing in Nature, Bastani et al.1 describe a system that they designed and deployed at points of entry into Greece, starting in August 2020. The algorithm, which is built on a method called reinforcement learning, markedly increased the efficiency of testing for the coronavirus SARS-CoV-2, and contributed to Greece’s ability to keep its borders open safely. The work also provides a clear warning about the shortcomings of the comparatively blunt policy tools that most other countries continue to use.

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Nature 599, 34-36 (2021)

doi: https://doi.org/10.1038/d41586-021-02556-w

References

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Competing Interests

The author declares no competing interests.

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