Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure.
Date
2021-11-19
Authors
Van Egeren, Debra
Novokhodko, Alexander
Stoddard, Madison
Tran, Uyen
Zetter, Bruce
Rogers, Michael S.
Joseph-McCarthy, Diane
Chakravarty, Arijit
Version
Published version
OA Version
Citation
D. Van Egeren, A. Novokhodko, M. Stoddard, U. Tran, B. Zetter, M.S. Rogers, D. Joseph-McCarthy, A. Chakravarty. 2021. "Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure.." Sci Rep, Volume 11, Issue 1, pp. 22630 - ?. https://doi.org/10.1038/s41598-021-02148-8
Abstract
The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.
Description
License
© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.