Universal scaling law for human-to-human transmission diseases

Due to the COVID-19 pandemic, Susceptible-Infective-Recovered (SIR) models and their variants are in high demand for predicting the number of cases in urban areas. Aiming to correctly use the experience of the epidemic evolution from one local to another, we present an analysis of the transmission rate of COVID-19 as a function of population size at the metropolitan area level for the United States. Contrary to the usual hypothesis in epidemics modeling, we observe that the disease transmissibility scales with the logarithm of the local's population size. The analysis, made possible by a large amount of data available on simultaneous epidemics of the same type, is universal for any human-to-human transmission disease. We present a contact rate scaling theory that explains the results. Copyright (C) 2021 EPLA

Automated summary

During the COVID-19 pandemic, SIR models and their variants are highly sought after for predicting case numbers in urban areas. We found that the transmission rate of COVID-19 scales with the logarithm of the local population size, contrary to typical epidemic modeling assumptions. Through analysis of large amounts of data, a universal contact rate scaling theory for human-to-human transmission diseases was proposed.