Emergence of universality in the transmission dynamics of COVID-19

• Published in: Scientific reports Vol. 11; no. 1; p. 18891
• Main Authors: Paul, Ayan, Bhattacharjee, Jayanta Kumar, Pal, Akshay, Chakraborty, Sagar
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.09.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/766/530/2803; 639/766/530/2804; 692/699/255/2514; Coronaviruses; COVID-19; COVID-19 - metabolism; COVID-19 - prevention & control; COVID-19 - transmission; Demography; Disease spread; Disease transmission; Disease Transmission, Infectious - prevention & control; Disease Transmission, Infectious - statistics & numerical data; Humanities and Social Sciences; Humans; Infectious diseases; Models, Statistical; Models, Theoretical; multidisciplinary; Neural networks; Pandemics; SARS-CoV-2 - metabolism; SARS-CoV-2 - pathogenicity; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-98302-3

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.