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Publication Detail
Epidemic mitigation by statistical inference from contact tracing data
  • Publication Type:
    Journal article
  • Authors:
    Baker A, Biazzo I, Braunstein A, Catania G, Dall’Asta L, Ingrosso A, Krzakala F, Mazza F, Mézard M, Muntoni AP, Refinetti M, Sarao Mannelli S, Zdeborová L
  • Publisher:
    Proceedings of the National Academy of Sciences
  • Publication date:
  • Journal:
    Proceedings of the National Academy of Sciences
  • Volume:
  • Issue:
  • Status:
  • Print ISSN:
  • Language:
Significance Contact tracing mobile applications are clear candidates for enabling us to slow down an epidemic and keep society running while holding the health risks down. Currently used mobile applications aim to notify individuals who were recently in significant contact with an individual who tested COVID-19 positive. In our work, we aim to quantify the epidemiological gain one would obtain if, additionally, individuals who were recently in contact could exchange messages of information. With such a message-passing addition, the risk of contracting COVID-19 could be estimated with much better accuracy than simple contact tracing. We conclude that probabilistic risk estimation is capable of enhancing performance of digital contact tracing and should be considered in the mobile tracing applications.
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