Renormalisation Group approach to pandemics as a time-dependent SIR model

TL;DR

This paper extends the epidemic Renormalisation Group framework to a time-dependent SIR model, successfully fitting COVID-19 data from multiple countries and highlighting the importance of death rates in modeling accuracy.

Contribution

It introduces a generalized RG-based epidemic model with time-dependent coefficients and demonstrates its effectiveness with real COVID-19 data from several countries.

Findings

Model accurately reproduces COVID-19 data in Denmark, Germany, Italy, and France.

Incorporating death rates over 15% improves model accuracy.

Highlights the significance of time-dependent recovery rates in epidemic modeling.

Abstract

We generalise the epidemic Renormalisation Group framework while connecting it to a SIR model with time-dependent coefficients. We then confront the model with COVID-19 in Denmark, Germany, Italy and France and show that the approach works rather well in reproducing the data. We also show that a better understanding of the time dependence of the recovery rate would require extending the model to take into account the number of deaths whenever these are over 15% of the total number of infected cases.