V-Reactor Dynamics: Dual Chaotic Systems and Synchronizing Human Defenses with Viral Evolution

TL;DR

V-Reactor Dynamics introduces a physics-based chaotic system model of host-virus interactions, enabling prediction of viral behavior and transmission dynamics, and offering a proactive approach to pandemic management.

Contribution

The paper presents a novel, physics-based framework modeling viral-host interactions as dual chaotic systems, incorporating a measurable reactivity parameter for improved prediction and control of viral outbreaks.

Findings

Accurately differentiates SARS-CoV-2 transmissibility and lethality.

Forecasts Omicron waves using the model.

Quantifies trade-offs between lockdown measures and socioeconomic impact.

Abstract

The COVID-19 pandemic exposed critical gaps in our ability to predict viral emergence and trajectory. Moving beyond sequence-dependent surveillance, we introduce V-Reactor Dynamics, a physics-based framework that models host-virus interaction as a synchronized dual chaotic system. At its core is the reactivity parameter ($\rho$), a measurable quantity derived from viral replication, immune neutralization, and drug interaction cross sections. We show that $\rho$ dictates both intra-host viral load phases, peak ($\rho>0$), plateau ($\rho\approx0$), and clearance ($\rho<0$), and, through a scaling law, the Lyapunov Exponent governing population-level transmission dynamics. Retrospectively, the model correctly differentiates SARS-CoV-2's higher transmissibility from SARS-CoV's lethality, accurately forecasts Omicron waves, and quantifies trade-offs between lockdown intensity and socioeconomic cost. Crucially, V-Dynamics enables pre-outbreak prediction via in vitro measurement of viral reaction cross sections, offering a pathway to proactive pandemic defense. By integrating quantum-mechanical interaction models with chaos theory across scales, this framework provides a quantitative roadmap for anticipating, controlling, and ultimately preempting future viral threats.