V-Reactor Dynamics: Dual Chaotic System and Rewriting the Antiviral Response History

TL;DR

V-Reactor Dynamics (V-Dynamics) is introduced, a physics-based framework modeling host-virus interaction as a synchronized dual chaotic system that provides a quantitative roadmap to preempt future pandemics.

Abstract

The COVID-19 pandemic revealed a key vulnerability: our failure to anticipate novel viral threats. Moving beyond descriptive virology, we introduce V-Reactor Dynamics (V-Dynamics), a physics-based framework modeling host-virus interaction as a synchronized dual chaotic system. This paradigm predicts viral evolution, immune response, transmission, and virulence through equations governed by the parameter reactivity ($ρ$). It quantifies infection phases, peak ($ρ>0$), plateau ($ρ\approx0$), clearance ($ρ<0$), transmission, and modality via $ρ/\ell$ (Reactivity/Generation time). Retrospectively, it correctly predicted SARS-CoV-2's higher transmissibility versus SARS-CoV's lethality and forecasted Omicron waves, exposing the lockdown-socioeconomic cost trade-off. We introduce measurable constants, viral replication, immune evasion, and drug absorption cross sections, derived from in vitro virion interactions. These quantum mechanical analogues relate to $ρ$ and $\ell$, enabling pre-outbreak predictive surveillance.V-Dynamics reveals a duality: microscopic chaos in viral production and macroscopic chaos in population transmission, linked by a scaling law. The sign of $ρ$, tied to the Lyapunov Exponent, dictates pandemic trajectory ($ρ>0$ for outbreak, $ρ<0$ for termination), offering a control mechanism. By unifying kinetics, cross-scale dynamics, and chaos theory, this framework provides a quantitative roadmap to preempt future pandemics.

Figures (4)

• Figure 1: a Theoretical viral loads of SARS-CoV-2, $n_{V}(t)$, (solid lines) in a comparison with the URT viral load data in patients no.2 in China Pan2020, no.2 and no.3 in German, and the blue dash line is the detection limit Wolfel2020. b The reactivity rate $\rho/\ell$ (in unites of hour$^{-1}$) used to generate the viral loads in a.
• Figure 2: a The theoretical viral load of SARS-CoV-2 (violet solid curve) compared to the URT viral load data in patients no.3, no.7, no.8, and no.10 in German, and the blue dash line is the detection limit Wolfel2020. b The theoretical viral load of SARS-CoV (red solid curve) compared to the URT viral load data in 14 patients in China Peiris2003. The viral load curves in a and b are generated by using the reactivity rates $\rho/\ell$ (in unites of hour$^{-1}$) in c and d, respectively.
• Figure 3: a The simulated viral loads in the lung core for SARS-CoV-2 (violet curve) and SAES-CoV (red curve), generated using the reactivity rates in Figure \ref{['sa2-sa']} panels c and d, respectively. b The burnup of H-particles, calculated from Eq.(\ref{['burnu2']}) using the corresponding viral loads in a as the virion flux inputs. c The number of people infected as a function of time, calculated by using Eq.(\ref{['npos1']}) with the (LE) values, which are obtained as $\lambda=0.05\rho_0/\ell$ using the scaling law with $\chi=0.05$ and the initial reactivity rates in Figure \ref{['sa2-sa']}c and d.
• Figure 4: Comparison of computed number of people tested positive for COVID-19 variant Omicron virus with the experimental data for Hongkong (solid square) and Shanghai(solid circle). The fitted parameter values are marked in the graph.