Generalized growth curve model for COVID-19 in Brazilian states

Abstract

The present paper consists of using the Chapman-Richard generalized growth model to functionally relate the number of people infected by COVID-19 with the number of days. The objective of this work is to estimate the instant that the number of infected people stops growing using the dataset of the accumulated amount of infected. For this propose, one conducted a comparative study of the performances of three models of Richard in eight Brazilian States. In the methodological context, the Gauss Newton procedure was used to estimate the parameters. In addition, selection criteria of the models were used to select the one that best fits the dataset. The methodology used allowed consistent estimates of the number of people infected by COVID-19 as a function of time and, consequently, it was possible to conclude that the projections provided by the growth curves point to a scenario of general contamination acceleration. Besides, the models predict that the epidemic is close to reaching its peak in Amazonas, Ceará, Maranhão, Pernambuco, and São Paulo States.