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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2512.03655 |
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| _version_ | 1866908815234957312 |
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| author | Chen, Yong-Shou |
| author_facet | Chen, Yong-Shou |
| contents | 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 ($ρ$), a measurable quantity derived from viral replication, immune neutralization, and drug interaction cross sections. We show that $ρ$ dictates both intra-host viral load phases, peak ($ρ>0$), plateau ($ρ\approx0$), and clearance ($ρ<0$), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | V-Reactor Dynamics: Dual Chaotic Systems and Synchronizing Human Defenses with Viral Evolution Chen, Yong-Shou Biological Physics 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 ($ρ$), a measurable quantity derived from viral replication, immune neutralization, and drug interaction cross sections. We show that $ρ$ dictates both intra-host viral load phases, peak ($ρ>0$), plateau ($ρ\approx0$), and clearance ($ρ<0$), 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. |
| title | V-Reactor Dynamics: Dual Chaotic Systems and Synchronizing Human Defenses with Viral Evolution |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2512.03655 |