Nature Communications (Sep 2020)

Developing a COVID-19 mortality risk prediction model when individual-level data are not available

  • Noam Barda,
  • Dan Riesel,
  • Amichay Akriv,
  • Joseph Levy,
  • Uriah Finkel,
  • Gal Yona,
  • Daniel Greenfeld,
  • Shimon Sheiba,
  • Jonathan Somer,
  • Eitan Bachmat,
  • Guy N. Rothblum,
  • Uri Shalit,
  • Doron Netzer,
  • Ran Balicer,
  • Noa Dagan

DOI
https://doi.org/10.1038/s41467-020-18297-9
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 9

Abstract

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Identification of individuals at risk of severe COVID-19 disease could inform treatment and public health planning. Here, the authors develop and validate a risk prediction model for COVID-19 mortality in Israel by building a model for severe respiratory infection and recalibrating it using COVID-19 case fatality rates.