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1000 Titel
  • Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions
1000 Autor/in
  1. Alzahrani, Saleh I. |
  2. Aljamaan, Ibrahim A. |
  3. Al-Fakih, Ebrahim A. |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-08
1000 Erschienen in
1000 Quellenangabe
  • 13(7):914-919
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1016/j.jiph.2020.06.001 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The substantial increase in the number of daily new cases infected with coronavirus around the world is alarming, and several researchers are currently using various mathematical and machine learning-based prediction models to estimate the future trend of this pandemic. In this work, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the expected daily number of COVID-19 cases in Saudi Arabia in the next four weeks. We first performed four different prediction models; Autoregressive Model, Moving Average, a combination of both (ARMA), and integrated ARMA (ARIMA), to determine the best model fit, and we found out that the ARIMA model outperformed the other models. The forecasting results showed that the trend in Saudi Arabia will continue growing and may reach up to 7668 new cases per day and over 127,129 cumulative daily cases in a matter of four weeks if stringent precautionary and control measures are not implemented to limit the spread of COVID-19. This indicates that the Umrah and Hajj Pilgrimages to the two holy cities of Mecca and Medina in Saudi Arabia that are supposedly scheduled to be performed by nearly 2 million Muslims in mid-July may be suspended. A set of extreme preventive and control measures are proposed in an effort to avoid such a situation.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal SARS-Cov-2
lokal Saudi Arabia
lokal Pandemic
lokal Time Series models
lokal mARIMA Prediction Model
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/QWx6YWhyYW5pLCBTYWxlaCBJLg==|https://frl.publisso.de/adhoc/uri/QWxqYW1hYW4sIElicmFoaW0gQS4=|https://frl.publisso.de/adhoc/uri/QWwtRmFraWgsIEVicmFoaW0gQS4=
1000 Label
1000 Förderer
  1. Imam Abdulrahman Bin Faisal University |
1000 Fördernummer
  1. Covid19-2020-040-Eng
1000 Förderprogramm
  1. Deanship of Scientific Research (DSR)
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Imam Abdulrahman Bin Faisal University |
    1000 Förderprogramm Deanship of Scientific Research (DSR)
    1000 Fördernummer Covid19-2020-040-Eng
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6421916.rdf
1000 Erstellt am 2020-07-16T11:39:20.155+0200
1000 Erstellt von 21
1000 beschreibt frl:6421916
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet 2022-07-04T10:17:10.691+0200
1000 Objekt bearb. Mon Jul 04 10:17:10 CEST 2022
1000 Vgl. frl:6421916
1000 Oai Id
  1. oai:frl.publisso.de:frl:6421916 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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