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Within-Host Phenotypic Evolution and the Population-Level Control of Chronic Viral Infections by Treatment and Prophylaxis

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  • Dmitry Gromov

    (Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, St. Petersburg 199034, Russia
    Department of Mathematics, National Research University Higher School of Economics, St. Petersburg Campus 16 Soyuza Pechatnikov Str., St Petersburg 190121, Russia)

  • Ethan O. Romero-Severson

    (Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA)

Abstract

Chronic viral infections can persist for decades spanning thousands of viral generations, leading to a highly diverse population of viruses with its own complex evolutionary history. We propose an expandable mathematical framework for understanding how the emergence of genetic and phenotypic diversity affects the population-level control of those infections by both non-curative treatment and chemo-prophylactic measures. Our frameworks allows both neutral and phenotypic evolution, and we consider the specific evolution of contagiousness, resistance to therapy, and efficacy of prophylaxis. We compute both the controlled and uncontrolled, population-level basic reproduction number accounting for the within-host evolutionary process where new phenotypes emerge and are lost in infected persons, which we also extend to include both treatment and prophylactic control efforts. We used these results to discuss the conditions under which the relative efficacy of prophylactic versus therapeutic methods of control are superior. Finally, we give expressions for the endemic equilibrium of these models for certain constrained versions of the within-host evolutionary model providing a potential method for estimating within-host evolutionary parameters from population-level genetic sequence data.

Suggested Citation

  • Dmitry Gromov & Ethan O. Romero-Severson, 2020. "Within-Host Phenotypic Evolution and the Population-Level Control of Chronic Viral Infections by Treatment and Prophylaxis," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1500-:d:408781
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    References listed on IDEAS

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    2. Sergey Kryazhimskiy & Ulf Dieckmann & Simon A Levin & Jonathan Dushoff, 2007. "On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-1, August.
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