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

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

doi:10.1016/j.chaos.2021.110922

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

Chaos Solitons Fractals. 2021 May:146:110922. doi: 10.1016/j.chaos.2021.110922. Epub 2021 Apr 2.

Authors

Jialu Song¹, Hujin Xie¹, Bingbing Gao², Yongmin Zhong¹, Chengfan Gu³, Kup-Sze Choi³

Affiliations

¹ School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
² School of Automatics, Northwestern Polytechnical University, China.
³ Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.

Abstract

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.

Keywords: COVID-19 modelling; Extended Kalman filter; Maximum likelihood estimation; SEIRD model; Time-dependent model parameters.