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.
Original language | English |
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Article number | 110922 |
Journal | Chaos, Solitons and Fractals |
Volume | 146 |
DOIs | |
Publication status | Published - May 2021 |
Keywords
- 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