Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models.

Autor:	Abdelaziz M; Université de Bordj Bou Arréridj, El-Anasser, Bordj Bou Arréridj, Algérie.; Laboratoire de Génie Biologique des Cancers, Université de Bejaia, Bejaia, Algérie., Ahmed A; Laboratoire d'Ecologie Microbienne, faculté des sciences de la nature et de la vie, université de Bejaia, Bejaia, Algérie., Riad A; Laboratoire Caractérisation et Valorisation des Ressources Naturelles, Université de Bordj Bou Arreridj, El-Anasser, Bordj Bou Arréridj, Algérie., Abderrezak G; Laboratoire de Génie Biologique des Cancers, Université de Bejaia, Bejaia, Algérie., Djida AA; Laboratoire de Génie Biologique des Cancers, Université de Bejaia, Bejaia, Algérie.
Jazyk:	angličtina
Zdroj:	Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit [East Mediterr Health J] 2023 Jul 31; Vol. 29 (7), pp. 515-519. Date of Electronic Publication: 2023 Jul 31.
DOI:	10.26719/emhj.23.054
Abstrakt:	Background: COVID-19 has become a threat worldwide, affecting every country. Aims: This study aimed to identify COVID-19 cases in Algeria using times series models for forecasting the disease. Methods: Confirmed COVID-19 daily cases data were obtained from 21 March 2020 to 26 November 2020 from the Algerian Ministry of Health. Forecasting was done using the Autoregressive Integrated Moving Average (ARIMA) models (0,1,1) with Minitab 17 software. Results: Observed cases during the forecast period were accurately predicted and placed within prediction intervals generated by ARIMA. Forecasted values of COVID-19 positive cases, recoveries and deaths showed an accurate trend, which corresponded to actual cases reported during 252, 253 and 254 days. Results were strengthened by variations of less than 5% between forecast and observed cases in 100% of forecasted data. Conclusion: ARIMA models with optimally selected covariates are useful tools for predicting COVID-19 cases in Algeria. (Copyright © Authors 2023; Licensee: World Health Organization. EMHJ is an open access journal. This paper is available under the Creative Commons Attribution Non-Commercial ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo).)
Databáze:	MEDLINE
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