An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter

Ghostine, Rabih; Gharamti, Mohamad El; Hassrouny, Sally; Hoteit, Ibrahim

doi:10.3390/math9060636

Cited by 140 publications

(146 citation statements)

References 47 publications

(52 reference statements)

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“…The states are estimated by assimilating observations into the model in accordance with the Kalman filter formula [ 45 ]. The EnKF can be further adapted to estimate both model states and the unknown parameters using an augmented state-parameter scheme [ 26 , 44 , 46 ]. The steps of the augmented EnKF are summarized below [ 26 , 44 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…The EnKF can be further adapted to estimate both model states and the unknown parameters using an augmented state-parameter scheme [ 26 , 44 , 46 ]. The steps of the augmented EnKF are summarized below [ 26 , 44 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…Model parameters can be estimated by solving inverse problems using observational data [ 24 ]. Some recent works on inverse COVID-19 modelling with different models include works of Wan et al [ 7 ], Sun et al [ 10 ], Libotte et al [ 18 ], Lobato et al [ 2 ], Li et al [ 11 ], Anastassopoulou et al [ 3 ], Loli Piccolomini and Zama [ 14 ], Korolev [ 15 ], Ndaïrou et al [ 9 ], Ifguis et al [ 12 ], Engbert et al [ 21 ], Yang et al [ 20 ], Arroyo-Marioli et al [ 25 ], and Ghostine et al [ 26 ]. Recent methods of inverse modelling for parameter estimation include the least-square techniques and optimization algorithms [ 3 , 10 , 12 , 14 , 15 ], Differential Evolution method [ 18 ], Stochastic and Multiobjective Fractal Search algorithm [ 2 ], and data assimilation methods [ 11 , 20 , 21 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the time-varying effective reproduction number and disease transmission rate were employed by the SIR model in tracking the dynamics of COVID-19. Furthermore, Ghostine et al [ 26 ] demonstrated the effectiveness of a joint-EnKF based assimilation scheme in estimating eight constant parameters of an extended SEIR model using the COVID-19 data.…”

Section: Introductionmentioning

confidence: 99%

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An application of the ensemble Kalman filter in epidemiological modelling

Lal

Huang

2021

PLoS ONE

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Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An application of the ensemble Kalman filter in epidemiological modelling

Lal

Huang

2021

PLoS ONE

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“…In [17] , the authors established a modified multi-stage SEIR model to predict the probable new COVID-19 wave in Saudi Arabia after lifting all travel restrictions on 3rd January 2021. In [18] , the authors extended the SEIR model by considering the fixed value of daily vaccinations to simulate the novel COVID-19 second wave spread in Saudi Arabia.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 deterministic and stochastic modelling with optimized daily vaccinations in Saudi Arabia

Omar¹,

Alnafisah²,

Elbarkouky³

et al. 2021

Results in Physics

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In this paper, we investigate the stochastic nature of the COVID-19 temporal dynamics by generating a fractional-order dynamic model and a fractional-order-stochastic model. Initially, we considered the first and second vaccination doses as multiple vaccinations were initiated worldwide. The concerned models are then tested for the Saudi Arabia second virus wave, which is assumed to start on 1st March 2021. Four daily vaccination scenarios for the first and second dose are assumed for 100 days from the wave beginning. One of these scenarios is based on function optimization using the invasive weed optimization algorithm (IWO). After that, we numerically solve the established models using the fractional Euler method and the Euler-Murayama method. Finally, the obtained virus dynamics using the assumed scenarios and the real one started by the government are compared. The optimized scenario using the IWO effectively minimizes the predicted cumulative wave infections with a 4.4 % lower number of used vaccination doses.

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Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

Yin,

Xiao,

McDonald

et al. 2023

Journal of Medical Virology

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The COVID‐19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden‐Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84–0.94 for the raw data and ρ = 0.91–0.98 for smoothed 7‐day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real‐time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

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An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter

Cited by 140 publications

References 47 publications

An application of the ensemble Kalman filter in epidemiological modelling

An application of the ensemble Kalman filter in epidemiological modelling

COVID-19 deterministic and stochastic modelling with optimized daily vaccinations in Saudi Arabia

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

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