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Volume 30, Number 2—February 2024
Research

Public Health Impact of Paxlovid as Treatment for COVID-19, United States

Yuan Bai1, Zhanwei Du1, Lin Wang1, Eric H.Y. Lau1, Isaac Chun-Hai Fung, Petter Holme, Benjamin J. Cowling, Alison P. Galvani, Robert M. Krug, and Lauren Ancel MeyersComments to Author 
Author affiliations: The University of Hong Kong, Hong Kong (Y. Bai, Z. Du, E.H.Y. Lau, B.J. Cowling); Hong Kong Science and Technology Park, Hong Kong, China (Y. Bai, Z. Du, E.H.Y. Lau, B.J. Cowling); University of Cambridge, Cambridge, UK (L. Wang); Deakin University, Burwood, Victoria, Australia (E.H.Y. Lau); Georgia Southern University, Statesboro, Georgia, USA (I. C.-H. Fung); Aalto University, Espoo, Finland (P. Holme); Kobe University, Kobe, Japan (P. Holme); Yale School of Public Health, New Haven, Connecticut, USA (A.P. Galvani); University of Texas at Austin, Austin, Texas, USA (R.M. Krug, L.A. Meyers); Santa Fe Institute, Santa Fe, New Mexico, USA (L.A. Meyers)

Main Article

Table 2

Within-host parameter estimates used in study of public health impact of Paxlovid in treatment of COVID-19, United States*

Parameter Mean (95% CI)
Cell infection rate in 10−6 mL/copies/day (b) 3.92 (2.82–5.38)
Infected cell death rate per day () 0.62 (0.42–0.92)
Virus production rate in copies/mL/day/cell (p) 3.19 (2.35–4.35)
Virus death rate per day (c) 2.21 (2.10–2.33)
Antiviral efficacy () 0.9937 (0.9917–0.9952)

*We fit the within-host model to the mean viral load dynamics reported from a clinical trial involving 2,246 infected adults treated with either Paxlovid or a placebo (11) using nonlinear mixed-effects model method (19). This method allows between-subject variability to improve the precision and accuracy of estimates (20). Values are means and 95% CI of parameter values in population, assuming that antiviral efficacy follows logit-normal distribution, and all other individual parameters follow log-normal distributions.

Main Article

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Main Article

1These authors contributed equally to this article.

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