An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an Ensemble Kalman Filter

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model's forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic.

To Access Resource:

Questions? Email Resource Support Contact:

  • opensky@ucar.edu
    UCAR/NCAR - Library

Resource Type publication
Temporal Range Begin N/A
Temporal Range End N/A
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links N/A
Additional Information N/A
Resource Format PDF
Standardized Resource Format PDF
Asset Size N/A
Legal Constraints

Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email opensky@ucar.edu
Resource Support Organization UCAR/NCAR - Library
Distributor N/A
Metadata Contact Name N/A
Metadata Contact Email opensky@ucar.edu
Metadata Contact Organization UCAR/NCAR - Library

Author Ghostine, Rabih
Gharamti, Mohamad
Hassrouny, Sally
Hoteit, Ibrahim
Publisher UCAR/NCAR - Library
Publication Date 2021-03-02T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
Progress N/A
Metadata Date 2023-08-18T18:29:52.524624
Metadata Record Identifier edu.ucar.opensky::articles:24319
Metadata Language eng; USA
Suggested Citation Ghostine, Rabih, Gharamti, Mohamad, Hassrouny, Sally, Hoteit, Ibrahim. (2021). An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an Ensemble Kalman Filter. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7154mgd. Accessed 17 July 2024.

Harvest Source