Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection

Retsef Levi*, El Ghali Zerhouni, Shoshy Altuvia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

During more than 3 years since its emergence, SARS-CoV-2 has shown great ability to mutate rapidly into diverse variants, some of which turned out to be very infectious and have spread throughout the world causing waves of infections. At this point, many countries have already experienced up to six waves of infections. Extensive academic work has focused on the development of models to predict the pandemic trajectory based on epidemiological data, but none has focused on predicting variant-specific spread. Moreover, important scientific literature analyzes the genetic evolution of SARS-CoV-2 variants and how it might functionally affect their infectivity. However, genetic attributes have not yet been incorporated into existing epidemiological modeling that aims to capture infection trajectory. Thus, this study leverages variant-specific genetic characteristics together with epidemiological information to systematically predict the future spread trajectory of newly detected variants. The study describes the analysis of 9.0 million SARS-CoV-2 genetic sequences in 30 countries and identifies temporal characteristic patterns of SARS-CoV-2 variants that caused significant infection waves. Using this descriptive analysis, a machine-learning-enabled risk assessment model has been developed to predict, as early as 1 week after their first detection, which variants are likely to constitute the new wave of infections in the following 3 months. The model’s out-of-sample area under the curve (AUC) is 86.3% for predictions after 1 week and 90.8% for predictions after 2 weeks. The methodology described in this paper could contribute more broadly to the development of improved predictive models for variants of other infectious viruses.

Original languageAmerican English
Article numberpgad424
JournalPNAS Nexus
Volume3
Issue number1
DOIs
StatePublished - 1 Jan 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Keywords

  • SARS-CoV-2 variants
  • computational biology
  • epidemiology
  • genetics
  • machine learning

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