Prediction of CoVid-19 infection, transmission and recovery rates: A new analysis and global societal comparisons

Abstract

We analyze the process of infection rate growth and decline for the recent global pandemic, applying a new method to the available global data. We describe and utilize an original approach based on statistical physics to predict the societal transmission timescale and the universal recovery trajectory resulting from the countermeasures implemented in entire societies. We compare the whole-society infection growth rates for many countries and local regions, to illustrate the common physical and mathematical basis for the viral spread and infection rate reduction, and validate the theory and resulting correlations. We show that methods traditionally considered for the numerical analysis and the control of individual virus transmission (e.g. ℜ0 scaling) represent one special case of the theory, and also compare our results to the available IHME computer model outcomes. We proceed to illustrate several interesting features of the different approaches to the mitigation of the pandemic, related to social isolation and “lockdown” tactics. Finally, we use presently available data from many countries to make actual predictions of the time needed for securing minimum infection rates in the future, highlighting the differences that emerge between isolated “islands” and mobile cities, and identifying the desired overall recovery trajectory.