Abstract
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.
Highlights
The novel coronavirus disease (COVID-19) first appeared in Wuhan city, and due to its high transmission rate, the virus has been rapidly spreading all over the world
Mathematical epidemic models based on the SIR model [2] are widely used to study the spread of a disease in a population [3,4,5,6,7]
We extend the SEIR model to seven compartments to simulate the epidemic of COVID19
Summary
The novel coronavirus disease (COVID-19) first appeared in Wuhan city, and due to its high transmission rate, the virus has been rapidly spreading all over the world. In the absence of a ready-to-use vaccine, and besides medical and biological research, mathematical models can play an important role in understanding and predicting disease transmission It helps to implement appropriate measures and efficient strategies to control the pandemic’s spread and mitigate its impact. Several mathematical models have been developed to study the transmission dynamics of COVID-19 [8,9,10,11] These models suffer from various sources of uncertainties, due to the incomplete description of the biological processes governing the disease spread, and due to some involved parameters being poorly known. One way to mitigate these uncertainties is to constrain epidemic forecasting models with available data These data can be combined with the model output to improve its prediction and reduce uncertainties [12,13,14]. This process, known as data assimilation, sequentially adjusts the model state and parameters once data become available to keep the model as close as possible to the real track [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
