Maximum likelihood-based extended Kalman filter for COVID-19 prediction. (May 2021)

• Record Type: Journal Article
• Authors: Song, Jialu; Xie, Hujin; Gao, Bingbing; Zhong, Yongmin; Gu, Chengfan; Choi, Kup-Sze
• Is Part Of: Chaos, solitons and fractals. Volume 146(2021)
• Publication Date: 2021-05
• Subjects: COVID-19 modelling -- Extended Kalman filter -- SEIRD model -- Maximum likelihood estimation -- Time-dependent model parameters; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.110922
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: 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. An extended Kalman filter is developed to estimate dynamic COVID-19 spread based on the online estimated model parameters. 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.