arXiv subjects

Juan C. Muñoz-Sánchez

Publications and source records attributed to Juan C. Muñoz-Sánchez.

2 recordsLinked to original sources

arXiv2026

Multiscale feedback drives viral evolution and epidemic dynamics

We introduce a minimal multiscale framework that links within-host virus dynamics to population-level SIRS epidemiology through explicit, bidirectional coupling. At the microscopic layer, a two variant quasispecies (master and mutant genomes with packaged virions) evolves on a fast timescale. At the macroscopic layer, two infectious classes (master- and mutant-infected), susceptible, recovered, and deceased individuals evolve slowly. The two scales are connected through transmission rates that depend on instantaneous virion abundance and through prevalence-weighted effective replication rates. Exploiting the timescale separation, we formalize a coarse-grained slow-fast closure: the genome-virion subsystem rapidly relaxes to quasi-steady states that parameterize time-varying transmission in the slow epidemiological system. This yields an integrated expression for the basic reproduction number and sharp inequalities that delineate coexistence versus exclusion. A key prediction is a context-dependent error threshold that shifts with the prevalence ratio, enabling transient pseudo-error catastrophes driven by epidemic composition rather than intrinsic fidelity. Linearization reveals parameter regions with damped oscillations arising solely from the microscopic-macroscopic feedback. Two illustrative extremes bracket the model's behavior: an avirulent strongly immunizing strain that benignly replaces the master, and a hypervirulent weakly immunizing that self-limits via host depletion and collapses transmission. This framework yields testable signatures linking viral load, incidence, and within-host composition.

math.DS↗

arXiv2024

Quasineutral multistability in an epidemiological-like model for defective-helper betacoronavirus infection in cell cultures

It is well known that, during replication, RNA viruses spontaneously generate defective viral genomes (DVGs). DVGs are unable to complete an infectious cycle autonomously, and depend on coinfection with a helper wild-type virus (HV) for their replication and/or transmission. The study of the dynamics arising from a HV and its DVGs has been a longstanding question in virology. It has been shown that DVGs can modulate HV replication and, depending on the strength of interference, result in HV extinctions or self-sustained persistent fluctuations. Extensive experimental work has provided mechanistic explanations for DVG generation and compelling evidences of HV-DVGs virus coevolution. Some of these observations have been captured in mathematical models. Here, we develop and investigate an epidemiological-like mathematical model specifically designed to study the dynamics of betacoronavirus in cell cultures experiments. The dynamics of the model is governed by several degenerate normally hyperbolic invariant manifolds given by quasineutral planes - i.e. filled by equilibrium points. Three different quasineutral planes have been identified depending on parameters and involving: (i) persistence of HV and DVGs; (\emph{ii}) persistence of non-infected cells and DVG-infected cells; and (iii) persistence of DVG-infected cells and DVGs. Sensitivity analyses indicate that model dynamics largely depend on the maximum burst size ($B$), and both the production rate ($β$) and replicative advantage ($δ$) of DVGs. Finally, the model has been fitted to single-passage experimental data using artificial intelligence and key virological parameters have been estimated.

math.DS↗