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An application of the ensemble Kalman filter in epidemiological modelling

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  • Rajnesh Lal
  • Weidong Huang
  • Zhenquan Li

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.

Suggested Citation

  • Rajnesh Lal & Weidong Huang & Zhenquan Li, 2021. "An application of the ensemble Kalman filter in epidemiological modelling," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0256227
    DOI: 10.1371/journal.pone.0256227
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    References listed on IDEAS

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    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
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    Cited by:

    1. Lamia Alyami & Saptarshi Das & Stuart Townley, 2024. "Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia," PLOS Global Public Health, Public Library of Science, vol. 4(7), pages 1-44, July.
    2. Papageorgiou, Vasileios E. & Tsaklidis, George, 2023. "An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Vasileios E. Papageorgiou, 2025. "Boosting epidemic forecasting performance with enhanced RNN-type models," Operational Research, Springer, vol. 25(3), pages 1-23, September.

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