Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance

  1. Fares Z Najar
  2. Evan Linde
  3. Chelsea L Murphy
  4. Veniamin A Borin
  5. Haun Wang
  6. Shozeb Haider
  7. Pratul K Agarwal  Is a corresponding author
  1. Oklahoma State University, United States
  2. University College London, United Kingdom

Abstract

COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate, combined with high rates of transmission and fatality can cause a deadly world-wide pandemic in a matter of weeks.1 Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections ('surges') before they occur. We describe here real time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.

Data availability

All sequences used in this work are available from GenBank. The protocol used for analysis are described in the supporting information.

The following previously published data sets were used

Article and author information

Author details

  1. Fares Z Najar

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  2. Evan Linde

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2053-6721
  3. Chelsea L Murphy

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  4. Veniamin A Borin

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  5. Haun Wang

    School of Pharmacy, Pharmaceutical and Biological Chemistry, University College London, London, United Kingdom
    Competing interests
    No competing interests declared.
  6. Shozeb Haider

    School of Pharmacy, Pharmaceutical and Biological Chemistry, University College London, London, United Kingdom
    Competing interests
    Shozeb Haider, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2650-2925
  7. Pratul K Agarwal

    1High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    For correspondence
    pratul.agarwal@okstate.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3848-9492

Funding

No external funding was received for this work.

Copyright

© 2023, Najar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Fares Z Najar
  2. Evan Linde
  3. Chelsea L Murphy
  4. Veniamin A Borin
  5. Haun Wang
  6. Shozeb Haider
  7. Pratul K Agarwal
(2023)
Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance
eLife 12:e82980.
https://doi.org/10.7554/eLife.82980

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https://doi.org/10.7554/eLife.82980