Open AccessA scaling investigation of pattern in the spread of COVID-19: universality in real data and a predictive analytical description
Author(s) -
Subir K. Das
Publication year - 2021
Publication title -
proceedings - royal society. mathematical, physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2946
pISSN - 1364-5021
DOI - 10.1098/rspa.2020.0689
Subject(s) - universality (dynamical systems) , scaling , exploit , covid-19 , econometrics , computer science , population , statistical physics , relevance (law) , data science , infectious disease (medical specialty) , mathematics , disease , physics , sociology , demography , political science , medicine , geometry , computer security , pathology , quantum mechanics , law
We analyse the spread of COVID-19, a disease caused by a novel coronavirus, in various countries by proposing a model that exploits the scaling and other important concepts of statistical physics. Quite expectedly, for each of the considered countries, we observe that the spread at early times occurs exponentially fast. We show how the countries can be classified into groups, likeuniversality classes in the literature of phase transitions, based on the rates of infections during late times. This method brings a new angle to the understanding of disease spread and is useful in obtaining a country-wise comparative picture of the effectiveness of lockdown-like social measures. Strong similarity, during both natural and lockdown periods, emerges in the spreads within countries having varying geographical locations, climatic conditions, population densities and economic parameters. We derive accurate mathematical forms for the corresponding scaling functions and show how the model can be used as a predictive tool, with instruction even for future waves, and, thus, as a guide for optimizing social measures and medical facilities. The model is expected to be of general relevance in the studies of epidemics.