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

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

Chaos, Solitons & Fractals, 2021, vol. 146, issue C

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; SEIRD model; Maximum likelihood estimation; Time-dependent model parameters (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921002769

DOI: 10.1016/j.chaos.2021.110922

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