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

Authors

Mohamed Lounis^{1, *,} , Malavika Babu²

¹Department of Agro-veterinary Science, Faculty of Natural and Life Sciences, University of ZianeAchour, BP 3117, Road of Moudjbara, Djelfa 17000, Algeria

²Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, 632 002, India

^*Corresponding author. Email: lounisvet@gmail.com

DOI

10.2991/dsahmj.k.210630.001How to use a DOI?

Keywords

COVID-19; logistic growth model; polynomial regression model; forecasting

Abstract

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.

Copyright

© 2021 Dr. Sulaiman Al Habib Medical Group. Publishing services by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Mohamed Lounis
AU  - Malavika Babu
PY  - 2021
DA  - 2021/07/08
TI  - Forecasting COVID-19 Cases in Algeria using Logistic Growth and Polynomial Regression Models
JO  - Dr. Sulaiman Al Habib Medical Journal
SP  - 83
EP  - 87
VL  - 3
IS  - 3
SN  - 2590-3349
UR  - https://doi.org/10.2991/dsahmj.k.210630.001
DO  - 10.2991/dsahmj.k.210630.001
ID  - Lounis2021
ER  -