Selected article for: "acute infection and viral load"

Author: Yafei Wang; Randy Heiland; Morgan Craig; Courtney L. Davis; Ashlee N Ford Versypt; Adrianne Jenner; Jonathan Ozik; Nicholson Collier; Chase Cockrell; Andrew Becker; Gary An; James A. Glazier; Aarthi Narayanan; Amber M Smith; Paul Macklin
Title: Rapid community-driven development of a SARS-CoV-2 tissue simulator
  • Document date: 2020_4_5
  • ID: lq4tcyh4_79
    Snippet: 8 dicting treatment responses [53] [54] [55] [56] [57] , and designing novel and more effective therapeutic approaches [58] [59] [60] . The classic within-host mathematical model of viral infection uses a system of ordinary differential equations (ODEs) to describe the dynamics between uninfected epithelial ("target") cells, infected cells in the eclipse phase, infected cells producing virus, and infectious virus 61 . This basic model has been sh.....
    Document: 8 dicting treatment responses [53] [54] [55] [56] [57] , and designing novel and more effective therapeutic approaches [58] [59] [60] . The classic within-host mathematical model of viral infection uses a system of ordinary differential equations (ODEs) to describe the dynamics between uninfected epithelial ("target") cells, infected cells in the eclipse phase, infected cells producing virus, and infectious virus 61 . This basic model has been shown to capture dynamics of both acute and chronic infection 62 , and has been extended to also include multiple viral (potentially resistant) strains 58 and various aspects of host immune responses 63, 64 . While such cell population-level models ODE models generally do not account for single-cell effects, they are effective for detailing viral load, host immune response, and pathology dynamics [65] [66] [67] [68] [69] [70] . Moreover, they can often be used to constrain and estimate parameters for more detailed models, develop novel hypotheses, and design confirmatory experiments 71, 72 .

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