Université de Bordj Bou Arréridj, El-Anasser, Bordj Bou Arréridj, Algérie.
Laboratoire de Génie Biologique des Cancers, Université de Bejaia, Bejaia, Algérie.
East Mediterr Health J. 2023 Jul 31;29(7):515-519. doi: 10.26719/emhj.23.054.
COVID-19 has become a threat worldwide, affecting every country.
This study aimed to identify COVID-19 cases in Algeria using times series models for forecasting the disease.
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.
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.
ARIMA models with optimally selected covariates are useful tools for predicting COVID-19 cases in Algeria.
COVID-19 已成为全球威胁，影响到每个国家。
本研究旨在使用时间序列模型识别阿尔及利亚的 COVID-19 病例，以预测该疾病。
从 2020 年 3 月 21 日至 2020 年 11 月 26 日，从阿尔及利亚卫生部获取了 COVID-19 确诊病例的每日数据。使用 Minitab 17 软件的自回归综合移动平均 (ARIMA) 模型 (0,1,1) 进行预测。
预测期内的观察病例被准确预测，并落在 ARIMA 生成的预测区间内。COVID-19 阳性病例、康复和死亡的预测值显示出准确的趋势，与第 252、253 和 254 天报告的实际病例相对应。在 100%的预测数据中，预测值与观察值之间的变化小于 5%，这一结果得到了加强。
选择最佳协变量的 ARIMA 模型是预测阿尔及利亚 COVID-19 病例的有用工具。
