Forecasting COVID-19 Cases in Algeria using Logistic Growth and Polynomial Regression Models

Forecasting COVID-19 Cases in Algeria using Logistic Growth and Polynomial Regression Models

Coronavirus disease 2019 (COVID-19) continues to spread worldwide since its emergence in December 2019 in Wuhan, China, and as of January 3, 2021 more than 84.4 million cases and 1.8 million deaths have been reported. To predict COVID-19 cases in Algeria, we applied two models—the logistic growth mo...

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Bibliographic Details
Main Authors: Mohamed Lounis, Malavika Babu
Format: Article
Language:English
Published: Springer 2021-07-01
Series:Dr. Sulaiman Al Habib Medical Journal
Subjects:
COVID-19
logistic growth model
polynomial regression model
forecasting
Online Access:https://www.atlantis-press.com/article/125958420/view
Description
Summary:Coronavirus disease 2019 (COVID-19) continues to spread worldwide since its emergence in December 2019 in Wuhan, China, and as of January 3, 2021 more than 84.4 million cases and 1.8 million deaths have been reported. To predict COVID-19 cases in Algeria, we applied two models—the logistic growth model and the polynomial regression model—using the data on COVID-19 cases reported by the Algerian Ministry of Health from February 25 to December 2, 2020. Results showed that the polynomial regression model better fitted the data of COVID-19 in Algeria compared with the logistic model. The first model estimated the number of cases on January 19, 2021 to reach 387,673. This model can help Algerian authorities in the fight against this disease.
ISSN:2590-3349

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