Emergence of universality in the transmission dynamics of COVID-19

TL;DR

This paper demonstrates that COVID-19 transmission dynamics exhibit universal scaling behavior, allowing for simplified, model-independent predictions across different regions and mitigation strategies, aiding policy development.

Contribution

The study introduces a simple two-parameter Blue Sky model and reveals universal scaling laws that unify COVID-19 transmission dynamics across diverse settings.

Findings

Data collapse shows independence from geopolitical and demographic factors.

A deep neural network confirms model-independent predictability.

Transmission dynamics can be explained by a bifurcation at the edge of a blue sky.

Abstract

The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods also needs to be built into any effective model for making such predictions. We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The ensuing data collapse renders the transmission dynamics largely independent of geopolitical variations, the effectiveness of various mitigation strategies, population demographics, etc. We propose a simple two-parameter model -- the Blue Sky model -- and show that one class of transmission dynamics can be explained by a solution that lives at the edge of a blue sky bifurcation. In addition, the data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can also be realized in a model-independent manner as we show using a deep neural network. The methodology adopted in this work can potentially be applied to the transmission of other infectious diseases and new universality classes may be found. The predictability in transmission dynamics and the simplicity of our methodology can help in building policies for exit strategies and mitigation methods during a pandemic.