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Modeling predicts mechanisms altered by mutations of the SARS-CoV-2 delta and omicron variants
Jason Pearson; Timothy Wessler; Alex Chen; Richard C. Boucher; Ronit Freeman; Samuel K. Lai; Raymond Pickles; M. Gregory Forest.
Affiliation
  • Jason Pearson; University of North Carolina at Chapel Hill
  • Timothy Wessler; University of North Carolina at Chapel Hill
  • Alex Chen; California State University--Dominguez Hills
  • Richard C. Boucher; University of North Carolina at Chapel Hill
  • Ronit Freeman; University of North Carolina at Chapel Hill
  • Samuel K. Lai; University of North Carolina at Chapel Hill
  • Raymond Pickles; University of North Carolina at Chapel Hill
  • M. Gregory Forest; University of North Carolina at Chapel Hill
Preprint in English | bioRxiv | ID: ppbiorxiv-481492
ABSTRACT
We apply our mechanistic, within-host, pre-immunity, respiratory tract infection model for unvaccinated, previously uninfected, and immune-compromised individuals. Starting from published cell infection and viral replication data for the SARS-CoV-2 alpha variant, we explore variability in outcomes of viral load and cell infection due to three plausible mechanisms altered by SARS-CoV-2 mutations of delta and omicron. We seek a mechanistic explanation of clinical test

results:

delta nasal infections express [~]3 orders-of-magnitude higher viral load than alpha, while omicron infections express an additional 1 to 2 orders-of-magnitude rise over delta. Model simulations reveal shortening of the eclipse phase (the time between cellular uptake of the virus and onset of infectious viral replication and shedding) alone can generate 3-5 orders-of-magnitude higher viral load within 2 days post initial infection. Higher viral replication rates by an infected cell can generate at most one order-of-magnitude rise in viral load, whereas higher cell infectability has minimal impact and lowers the viral load.
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Full text: Available Collection: Preprints Database: bioRxiv Type of study: Prognostic study Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: bioRxiv Type of study: Prognostic study Language: English Year: 2022 Document type: Preprint
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