In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness

Ke, Ruian; Zitzmann, Carolin; Ho, David D.; Ribeiro, Ruy M.; Perelson, Alan S.

doi:10.1101/2021.06.26.21259581

Cited by 9 publications

(9 citation statements)

References 56 publications

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“…Although some studies have measured the response to SARS-CoV-2 infection longitudinally in humans [22][23][24] , none can capture host features at the time of virus exposure, the early events prior to symptom onset, or the detailed course of infection that can be shown by experimental challenge. Whilst the incubation period from the estimated time of natural exposure to perceived symptom onset has previously been estimated as ~5 days 25,26 , this best aligns with peak symptoms and is longer than the true incubation period.…”

Section: Discussionmentioning

confidence: 99%

Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge

Chiu

Killingley

Mann

et al. 2022

Preprint

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To establish a novel SARS-CoV-2 human challenge model, 36 volunteers aged 18-29 years without evidence of previous infection or vaccination were inoculated with 10 TCID50 of a wild-type virus (SARS-CoV-2/human/GBR/484861/2020) intranasally. Two participants were excluded from per protocol analysis due to seroconversion between screening and inoculation. Eighteen (~53%) became infected, with viral load (VL) rising steeply and peaking at ~5 days post-inoculation. Virus was first detected in the throat but rose to significantly higher levels in the nose, peaking at ~8.87 log10 copies/ml (median, 95% CI [8.41,9.53). Viable virus was recoverable from the nose up to ~10 days post-inoculation, on average. There were no serious adverse events. Mild-to-moderate symptoms were reported by 16 (89%) infected individuals, beginning 2-4 days post-inoculation. Anosmia/dysosmia developed more gradually in 12 (67%) participants. No quantitative correlation was noted between VL and symptoms, with high VLs even in asymptomatic infection, followed by the development of serum spike-specific and neutralising antibodies. However, lateral flow results were strongly associated with viable virus and modelling showed that twice-weekly rapid tests could diagnose infection before 70-80% of viable virus had been generated. Thus, in this first SARS-CoV-2 human challenge study, no serious safety signals were detected and the detailed characteristics of early infection and their public health implications were shown. ClinicalTrials.gov identifier: NCT04865237.

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Section: Discussionmentioning

confidence: 99%

Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge

Chiu

Killingley

Mann

et al. 2022

Preprint

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“…To better quantify the infectious potential of each individual, we first used the viral culture data as a measure for intrinsic infectiousness (infectiousness for short below) to characterize how infectiousness depends on the viral genome load. We fitted three alternative models as proposed before (31) to the paired nasal RTqPCR and viral culture data collected from each individual using a non-linear mixed effect modeling approach (see Fig. S6 for a workflow and Supporting Text for more details).…”

Section: Significant Heterogeneity In the Infectious Potential Of Individualsmentioning

confidence: 99%

Daily sampling of early SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness

Martinez

Smith

et al. 2021

Preprint

Self Cite

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The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimate viral reproduction and clearance rates, and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to superspreading. Viral genome load often peaked days earlier in saliva than in nasal swabs, indicating strong compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of B.1.1.7 and non-B.1.1.7 viruses in nasal swabs were indistinguishable, however B.1.1.7 exhibited a significantly slower pre-peak growth rate in saliva. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.

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“…et al, 2021;Jeong et al, The rate of cells in the eclipse phase can become productively infected (transition rate from the eclipse phase to the productively infected, k) is 3.75 (95% CI:−0.04, 7.54) day −1 in five models from five studies (Czuppon P. et al, 2021;Fatehi et al, 2021;Jenner A.L. et al, 2021;Jeong et al, 2021;Ke et al, 2021) S1).…”

Section: Resultsmentioning

confidence: 99%

“…The rate of viral particles infecting susceptible cells (virus infection rate, β) is −4.97 (95% CI:−9.77, −0.16) log([copies/ml] −1 day −1 ) in six models from eight studies(Hernandez-Vargas and Velasco-Hernandez, 2020;Fatehi et al, 2021;Iwanami et al, 2021;Jenner A.L. et al, 2021;Jeong et al, 2021;Ke et al, 2021; Kim K.S. et al, 2021; Sadria M. and Layton A.T., 2021) (Figure 2(b)), with pooled estimates of −6.96…”

mentioning

confidence: 99%

Within‐host dynamics of SARS‐CoV‐2 infection: A systematic review and meta‐analysis

Wang²,

Bai

et al. 2022

Transbounding Emerging Dis

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Within‐host model specified by viral dynamic parameters is a mainstream tool to understand SARS‐CoV‐2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within‐host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within‐host modelling studies. We identified seven independent within‐host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell‐mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within‐host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is −1.00 (95% CI:−1.94, −0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as −6.96 (95% CI:−7.66, −6.25) log([copies/ml]–1 day–1) and 0.92 (95% CI:−0.09, 1.93) day–1, respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta‐analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within‐host models have been published for SARS‐CoV‐2. The reviewed viral dynamic parameters can be used in the same within‐host model to understand SARS‐CoV‐2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.

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In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness

Cited by 9 publications

References 56 publications

Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge

Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge

Daily sampling of early SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness

Within‐host dynamics of SARS‐CoV‐2 infection: A systematic review and meta‐analysis

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