GENERALIZED GROWTH CURVE MODEL FOR COVID-19 IN BRAZILIAN STATES
Main Article Content
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.
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).