Immune computation and covid-19 mortality: A rationale for ivig

Irun R. Cohen, Sol Efroni, Henri Atlan

Research output: Contribution to journalArticlepeer-review


COVID-19 infection tends to be more lethal in older persons than in the young; death results from an over-active inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).

Original languageEnglish
Pages (from-to)195-203
Number of pages9
JournalCritical Reviews in Immunology
Issue number3
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 by Begell House, Inc.


  • Inflammation
  • Machine learning
  • Public repertoires
  • Test data
  • Training data


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