A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2

Abstract

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2–3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

“…SARS-CoV-2 virus dynamics without antiviral treatment is described by a mathematical model previously proposed in [24,31,32]:…”

“…We evaluated the expected antiviral effect of the treatment on the outcomes (duration of virus shedding and cumulative viral load measured on a log scale) under different inhibition rates (ε) and initiation times (t � ). The effect of drugs that blocking de novo infection can be modeled by inhibiting both the βf(t)V(t) and γf(t) V(t) terms, and a drug promoting cytotoxicity can be modeled by increasing δV(t), as we discussed in [31]. Unfortunately, because sufficient viral load data under antiviral drug therapy are not available yet, the antiviral effect (ε) of drugs in preclinical development and in clinical trials are still unknown.…”

“…Using our mathematical model and the estimated parameter distribution for each patient (S3 Table ), we conducted in silico experiments to determine the possible therapeutic response, measured in terms of virus dynamics, of drug treatments blocking virus replication. Clinical outcomes are known to be related to the timing of initiation of antiviral treatment in general, and, especially, for influenza [31,[39][40][41][42], and the antiviral effects of a treatment are dependent on dose and the patients' immune system [43,44]. Thus, we studied several different scenarios in which we varied the time of treatment initiation (0.5 or 5 days from symptom onset, which were before and generally after the estimated peak viral load in our dataset) and…”

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

“…Most prior work uses Ordinary Differential Equation (ODE) models to represent within-host virus dynamics, e.g., [6][7][8][9][10][11][12]. Such models are useful for studying the onset and duration of the infective period [13] and the effect of various therapeutics given at different times [14][15][16]. However, they have limited ability to fully account for dynamics in the large and complex space of the lung [12,17].…”

The relative strength and timing of innate immune and CD8 T-cell responses underlie the heterogeneous outcomes of SARS-CoV-2 infection

“…In the last year, several mathematical models of within-host SAR-CoV-2 dynamics have been developed and have offered valuable insights (53). For instance, they have helped estimate the within-host basic reproductive ratio (33,34,52) and assess the effects of drugs and vaccines (26,27,44,(54)(55)(56)(57). Attempts have also been made to capture the role of the immune system in disease progression and outcome (44,55,(57)(58)(59)(60)(61).…”