Kalman-based compartmental estimation for covid-19 pandemic using advanced epidemic model

Nanda, Sumanta Kumar; Kumar, Guddu; Bhatia, Vimal; Singh, Abhinoy Kumar

doi:10.1016/j.bspc.2023.104727

Cited by 7 publications

(1 citation statement)

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“…Tracking the reproduction number (

) with confidence bounds based on the KF has been introduced in [ 12 ]. In addition, some literature has compared the performance of COVID-19 prediction with different types of KFs, e.g., fractional-order EKF with an SEIR model [ 13 ], switching KF with time-series models [ 14 ], cubature KF with the SEIRRPV model (Susceptible–Exposed–Infected–Recovered from exposure–Recovered from infection–Passed away–Vaccinated) in [ 15 ] and Quadratic KF with SEIR/ARIMA (AutoRegressive Integrated Moving Average) models [ 16 ]. The novelty of this work as opposed to the existing Kalman filtering works on COVID-19 data is that it reports a thorough benchmarking of the state estimation performances using four alternative formations of the EKF with and without correlated noise and skewed distribution based on the residual of the deterministic model and real data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters

Alyami

Panda

Das

2023

Sensors

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The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible–Exposed–Infected–Quarantined–Recovered–Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.

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“…Tracking the reproduction number (

Section: Introductionmentioning

confidence: 99%