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Updated: Oct 4, 2025
Reverse Genetics to Engineer Positive-Sense RNA Virus Variants
Published on: June 9, 2022
Effect of an antiviral drug control and its variable order fractional network in host COVID-19 kinetics
Bo Wang1,2, Jayanta Mondal3, Piu Samui3
1School of Electronic Information and Automation, Aba Teachers University, Wenchuan, 623002 China.
Abstract:
In December 2019, a novel coronavirus disease (COVID-19) appeared in Wuhan, China. After that, it spread rapidly all over the world. Novel coronavirus belongs to the family of Coronaviridae and this new strain is called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epithelial cells of our throat and lungs are the main target area of the SARS-CoV-2 virus which leads to COVID-19 disease. In this article, we propose a mathematical model for examining the effects of antiviral treatment over viral mutation to control disease transmission. We have considered here three populations namely uninfected epithelial cells, infected epithelial cells, and SARS-CoV-2 virus. To explore the model in light of the optimal control-theoretic strategy, we use Pontryagin's maximum principle. We also illustrate the existence of the optimal control and the effectiveness of the optimal control is studied here. Cost-effectiveness and efficiency analysis confirms that time-dependent antiviral controlled drug therapy can reduce the viral load and infection process at a low cost. Numerical simulations have been done to illustrate our analytical findings. In addition, a new variable-order fractional model is proposed to investigate the effect of antiviral treatment over viral mutation to control disease transmission. Considering the superiority of fractional order calculus in the modeling of systems and processes, the proposed variable-order fractional model can provide more accurate insight for the modeling of the disease. Then through the genetic algorithm, optimal treatment is presented, and its numerical simulations are illustrated.
Insights
Antiviral treatment strategies can effectively control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission by reducing viral load. Mathematical modeling, including a novel fractional model, demonstrates the cost-effectiveness of time-dependent drug therapy.
Area of Science:
- Mathematical Epidemiology
- Virology
- Pharmacology
Background:
- The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 led to the COVID-19 pandemic.
- SARS-CoV-2 primarily infects epithelial cells in the throat and lungs, driving disease transmission.
- Understanding viral mutation and the impact of antiviral treatments is crucial for disease control.
Purpose of the Study:
- To develop and analyze a mathematical model for assessing the impact of antiviral treatment on viral mutation and disease transmission.
- To investigate the effectiveness and cost-efficiency of optimal control strategies for managing SARS-CoV-2 infection.
- To propose and evaluate a novel variable-order fractional model for more accurate disease modeling.
Main Methods:
- Development of a mathematical model incorporating uninfected cells, infected cells, and SARS-CoV-2 virus populations.
- Application of Pontryagin's maximum principle to determine optimal control strategies for antiviral therapy.
- Numerical simulations and cost-effectiveness analysis to validate model findings.
- Introduction of a variable-order fractional model and optimization using a genetic algorithm.
Main Results:
- Optimal control theory confirms the existence and effectiveness of time-dependent antiviral treatment.
- Cost-effectiveness analysis indicates that controlled drug therapy significantly reduces viral load and infection at a low cost.
- The variable-order fractional model provides enhanced accuracy in simulating disease dynamics and treatment effects.
Conclusions:
- Time-dependent antiviral therapy is a viable and cost-effective strategy for controlling SARS-CoV-2 transmission.
- Mathematical modeling, particularly with fractional calculus, offers valuable insights into optimizing treatment protocols.
- Optimal treatment strategies derived from these models can aid in mitigating the impact of COVID-19.
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