Dynamic Adjustment of SIR Model with the Social Permissiveness: An Actual Measure of the Infection Rate

Abstract

The COVID-19 pandemic has globally impacted the behavior of the social patterns affecting the disease’s contagiousness. This effect deviates the classical SIR model from reproducing the data of COVID-19 in most countries. This study incorporates a non-constant permissiveness function to the SIR model. The resulting model is computationally solved to obtain a likely permissiveness time-function. To solve the adjusted model, a technique based on a proportional-integral controller is applied. The resulting models are compared with previous results obtained by a manual iterative adjusting method.