Maximum likelihood-based extended Kalman filter for COVID-19 prediction

Jialu Song, Hujin Xie, Bingbing Gao, Yongmin Zhong, Chengfan Gu, Kup Sze Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)


Prediction of COVID-19 spread plays a significant role in the epidemiology study and government battles against the epidemic. However, the existing studies on COVID-19 prediction are dominated by constant model parameters, unable to reflect the actual situation of COVID-19 spread. This paper presents a new method for dynamic prediction of COVID-19 spread by considering time-dependent model parameters. This method discretises the susceptible-exposed-infected-recovered-dead (SEIRD) epidemiological model in time domain to construct the nonlinear state-space equation for dynamic estimation of COVID-19 spread. A maximum likelihood estimation theory is established to online estimate time-dependent model parameters. Subsequently, an extended Kalman filter is developed to estimate dynamic COVID-19 spread based on the online estimated model parameters. The proposed method is applied to simulate and analyse the COVID-19 pandemics in China and the United States based on daily reported cases, demonstrating its efficacy in modelling and prediction of COVID-19 spread.

Original languageEnglish
Article number110922
JournalChaos, Solitons and Fractals
Publication statusPublished - May 2021


  • COVID-19 modelling
  • Extended Kalman filter
  • Maximum likelihood estimation
  • SEIRD model
  • Time-dependent model parameters

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • General Mathematics
  • General Physics and Astronomy
  • Applied Mathematics


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