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Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses
Katherine Owens, … , Shadisadat Esmaeili, Joshua T. Schiffer
Katherine Owens, … , Shadisadat Esmaeili, Joshua T. Schiffer
Published April 4, 2024
Citation Information: JCI Insight. 2024;9(9):e176286. https://doi.org/10.1172/jci.insight.176286.
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Research Article Infectious disease Virology

Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses

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Abstract

The viral kinetics of documented SARS-CoV-2 infections exhibit a high degree of interindividual variability. We identified 6 distinct viral shedding patterns, which differed according to peak viral load, duration, expansion rate, and clearance rate, by clustering data from 768 infections in the National Basketball Association cohort. Omicron variant infections in previously vaccinated individuals generally led to lower cumulative shedding levels of SARS-CoV-2 than other scenarios. We then developed a mechanistic mathematical model that recapitulated 1,510 observed viral trajectories, including viral rebound and cases of reinfection. Lower peak viral loads were explained by a more rapid and sustained transition of susceptible cells to a refractory state during infection as well as by an earlier and more potent late, cytolytic immune response. Our results suggest that viral elimination occurs more rapidly during Omicron infection, following vaccination, and following reinfection due to enhanced innate and acquired immune responses. Because viral load has been linked with COVID-19 severity and transmission risk, our model provides a framework for understanding the wide range of observed SARS-CoV-2 infection outcomes.

Authors

Katherine Owens, Shadisadat Esmaeili, Joshua T. Schiffer

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Figure 2

Distinct viral dynamic profiles in the National Basketball Association cohort from June 2020 to January 2022.

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Distinct viral dynamic profiles in the National Basketball Association c...
(A) Trajectories stratified by cluster assignment after k-means clustering with k = 6. Cluster centers are shown in black. (B) Heatmap of log viral load over time. Each row corresponds to an infection, and trajectories are ordered according to cluster. (C) Cluster centers plotted on the same axis demonstrate differing peak viral loads, time of viral peak, clearance rate, and time to clearance by cluster. (D) The proportion of infections cleared over time for each cluster with 95% CI shaded. (E–G) Box plots of the log10 viral load AUC (E), peak viral load for different dynamic groups (F), and days between detection and peak viral load (G). According to a Mann-Whitney U test with Bonferroni adjustment for multiple comparisons, distinctions in the mean for all possible pairs of groups are significant (P < 0.05) except for the pairs marked “ns.” (H–J) In the final row, stacked bar charts indicate the percentage of cases that fall into each dynamic group when cases are stratified by age group (H), symptom status (I), lineage of infecting variant (J), and vaccination status (K).

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