ArticlePloS one2021
An application of the ensemble Kalman filter in epidemiological modelling.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A data-driven analysis and forecasting of Leishmaniasis-COVID-19 co-infection model using ensemble Kalman filter.Scientific reports · 2026Article
- Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation.PLoS computational biology · 2026Article
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- A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction.Scientific reports · 2025Article
- A real-time forecasting and estimating system of West Nile virus: a case study of the 2023 WNV outbreak in Colorado, USA.Royal Society open science · 2024Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
- Early estimation of the number of hidden HIV infected subjects: An extended Kalman filter approach.Infectious Disease Modelling · 2023Article
- Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.Sensors (Basel, Switzerland) · 2023Article
- An assessment of transmission dynamics via time-varying reproduction number of the second wave of the COVID-19 epidemic in Fiji.Royal Society open science · 2022Article
- Time-discrete SIR model for COVID-19 in Fiji.Epidemiology and infection · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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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