Abstract
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.
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