Evidence for complex fixed points in pandemic data

The preprint attempts to revolutionize the existing paradigm of epidemiological modeling, by introducing complex fixed points into the Renormalization Group (RG) framework. It challenges the conventional compartmental models and complex network diffusion techniques, which primarily focus on initial growth, by proposing a unified way to model both the initial wave, the inter-wave ‘strolling’ phase, and potentially even subsequent waves. This approach represents a potential paradigm shift from previous models that struggled to incorporate the inter-wave dynamics.

This preprint presents a compelling case for a Model Revolution, potentially leading to a Paradigm Change. The introduction of complex fixed points in the eRG framework offers a novel approach to understanding epidemiological dynamics. The ‘strolling’ phase, previously a challenge for existing models, is now interpreted as a region of near time-scale invariance by the preprint authors, which explains the quasi-linear growth in infections. The preprint demonstrates the effectiveness of their model by fitting it to COVID-19 data, showing its accuracy compared to previous eRG models with real fixed points.While the preprint focuses on COVID-19 data, its theoretical framework is presented as applicable to a wide range of infectious diseases. The potential of the CeRG framework lies in unifying the modeling of epidemic waves and inter-wave periods, offering deeper insights into the underlying dynamics. Further validation and broader application of this framework could solidify its standing as a Paradigm Change. However, at this stage, it represents a substantial Model Revolution that has the potential to reshape the field if its principles are further validated and adopted by the scientific community.