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Updated: Oct 23, 2025
Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
Published on: January 20, 2017
Comparing antiviral strategies against COVID-19 via multiscale within-host modelling
F Fatehi1,2, R J Bingham1,2,3, E C Dykeman1,2
1Department of Mathematics, University of York, York YO10 5DD, UK.
Abstract:
Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.
Insights
Combining antiviral and convalescent plasma therapies for COVID-19 can shorten infection duration, but effectiveness decreases with delayed treatment. Early monotherapy may prolong illness, necessitating careful clinical control.
Area of Science:
- Computational biology
- Infectious disease modeling
- Virology
Background:
- Within-host models are crucial for evaluating COVID-19 antiviral therapies.
- Understanding viral life cycle dynamics is key to targeted treatment.
Purpose of the Study:
- To develop and utilize a novel stochastic agent-based model for COVID-19 intracellular dynamics.
- To simulate disease progression under various treatment scenarios and immune responses.
- To assess the efficacy of combined antiviral and convalescent plasma therapies.
Main Methods:
- Developed a stochastic agent-based model for COVID-19 intracellular dynamics.
- Integrated agent-based model predictions with an intercellular ODE model fitted to patient data.
- Simulated disease progression by varying immune response parameters and treatment timings.
- Compared simulated outcomes for untreated, monotherapy, and combination therapy scenarios.
Main Results:
- Combined antiviral and convalescent plasma therapies are effective in reducing infection length when initiated early.
- Synergistic effects of combination therapy diminish significantly with delayed treatment initiation.
- Early monotherapy with either antiviral or convalescent plasma may paradoxically prolong infection duration.
- Infectious virions can persist even after other infection markers decline with early monotherapy.
Conclusions:
- Combination therapy for COVID-19 shows promise but requires precise timing for optimal outcomes.
- Delayed treatment initiation compromises the benefits of combined antiviral and convalescent plasma therapies.
- Careful clinical management and controlled usage of these therapies are essential to avoid adverse effects like prolonged infection.
