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Within-Host Phenotypic Evolution and the Population-Level Control of Chronic Viral Infections by Treatment and
Dmitry Gromov1,2, Ethan O Romero-Severson3
1Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, St. Petersburg 199034, Russia.
This study introduces a mathematical framework to understand how viral evolution impacts chronic infection control. It analyzes how viral diversity, contagiousness, and treatment resistance affect public health strategies for managing infections.
Area of Science:
- Mathematical modeling
- Virology
- Evolutionary biology
- Epidemiology
Background:
- Chronic viral infections persist for decades, generating significant viral diversity.
- Understanding viral evolution is crucial for effective population-level control strategies.
Purpose of the Study:
- To develop a mathematical framework for analyzing the impact of viral genetic and phenotypic diversity on chronic infection control.
- To evaluate the effectiveness of non-curative treatments and prophylactic measures in the context of evolving viruses.
Main Methods:
- Developed an expandable mathematical framework incorporating neutral and phenotypic evolution.
- Modeled the evolution of contagiousness, therapy resistance, and prophylaxis efficacy.
- Computed population-level basic reproduction numbers, accounting for within-host evolution and control efforts.
Main Results:
- The framework integrates within-host evolutionary dynamics (phenotype emergence/loss) into population-level control models.
- Calculations provide insights into the relative efficacy of prophylactic versus therapeutic control measures.
- Derived expressions for endemic equilibrium, enabling estimation of within-host evolutionary parameters from population data.
Conclusions:
- Viral evolution significantly influences the effectiveness of public health interventions against chronic infections.
- The developed framework offers a quantitative approach to assess and optimize control strategies.
- This work provides a potential method for estimating crucial within-host evolutionary parameters using population-level genetic data.
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