Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

Domaine:

healthcare

Type de record:

paper
Créateur:
BelMes
Éditeur:
ope
Hôte:
Abstract In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020. Background The aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days. Methods This study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter ( d ) is carried out using OxMetrics 6 software. Results The ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan. Conclusions The forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.

Visit

doi.org

Languages

Arabic, Algerian Spoken

Similaires

Forecasting COVID‐19 Cases Using Alpha‐Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) MethodForecasting of CO2 Emissions in Algeria Using Discrete Wavelet Transform –Based Autoregressive Integrated Moving Average ModelsForecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series modelForecasting cotton lint exports in Nigeria using the autoregressive integrated moving average modelForecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) ModelsForecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

Forecasting COVID‐19 Cases Using Alpha‐Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method

COVID‐19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes

Forecasting of CO2 Emissions in Algeria Using Discrete Wavelet Transform –Based Autoregressive Integrated Moving Average Models

Abstract The increasing impact of climate change and rising temperatures has made the redu

Forecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series model

International audience Covid-19 epidemic continues to escalate globally posing life t

Forecasting cotton lint exports in Nigeria using the autoregressive integrated moving average model

Nigeria was a major global exporter of cotton lint to international market during the colonial and p

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Price stability is the primary monetary policy objective in any economy since it protects the intere

Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA)