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Updated: Nov 23, 2025

Production of a SARS-CoV-2 Virus-Like-Particle System to Investigate Viral Life Cycles In Vitro

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Structure and Hierarchy of SARS-CoV-2 Infection Dynamics Models Revealed by Reaction Network Analysis.

Stephan Peter^1,2, Peter Dittrich², Bashar Ibrahim^2,3,4

¹Ernst-Abbe University of Applied Sciences Jena, Department of Fundamental Sciences, Carl-Zeiss-Promenade 2, 07745 Jena, Germany.

|December 30, 2020

Summary

This study introduces a mathematical method to compare SARS-CoV-2 infection models based on their long-term behavior, creating a hierarchy. SARS-CoV-2 models appear simpler than Influenza-A models, guiding future research.

Keywords:

Covid-19 ODEs PDEs SARS-CoV-2 between hosts chemical organization theory corona reaction networks analysis virus dynamics modeling within hosts

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Area of Science:

Mathematical Biology
Infectious Disease Modeling
Systems Biology

Background:

Understanding the long-term behavior of infectious disease models is crucial for predicting disease dynamics and informing public health strategies.
Existing models for SARS-CoV-2 and Influenza-A viruses vary in complexity and scale, making direct comparison challenging.

Purpose of the Study:

To develop and apply a novel mathematical technique for analyzing and comparing the long-term behavior of infection dynamics models.
To establish a unified hierarchy of SARS-CoV-2 and Influenza-A virus models based on their structural complexity and potential long-term dynamics.

Main Methods:

Utilized the theory of chemical organizations to assess model structure without requiring quantitative kinetic data.
Applied a mathematical technique to analyze coupled ordinary and partial differential equation models of SARS-CoV-2 infection dynamics at organismal and host-to-host scales.
Developed Hasse diagrams of organizations and mapped species to four types (uninfected, infected, immune, bacterial) for comparative analysis.

Main Results:

A hierarchy integrating twelve SARS-CoV-2 models was constructed, revealing their structural properties and long-term behaviors.
SARS-CoV-2 models demonstrated simpler long-term dynamics and hierarchies with fewer dependencies compared to Influenza-A models.
Integration with previous Influenza-A virus model analysis resulted in a joint hierarchy of 24 models.

Conclusions:

The developed mathematical technique provides a robust framework for comparing diverse infection dynamics models.
The findings suggest that current SARS-CoV-2 models are less complex in their long-term behavior than those for Influenza-A.
Results can guide the development of more sophisticated SARS-CoV-2 models by identifying areas for complexity enhancement within the established hierarchy.