ArticleChaos, solitons, and fractals2021
Maximum likelihood-based extended Kalman filter for COVID-19 prediction.
Article in Chaos, solitons, and fractals, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Toward Drift-Aware Multimodal Early Warning for Acute Exacerbations of Chronic Obstructive Pulmonary Disease.JMIR medical informatics · 2026Article
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- A filtering approach for statistical inference in a stochastic SIR model with an application to Covid-19 data.Biostatistics (Oxford, England) · 2025Article
- Kalman Filter-Based Epidemiological Model for Post-COVID-19 Era Surveillance and Prediction.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models.International journal of environmental research and public health · 2024Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
- Kalman-based compartmental estimation for covid-19 pandemic using advanced epidemic model.Biomedical signal processing and control · 2023Article
- A new hybrid prediction model of COVID-19 daily new case data.Engineering applications of artificial intelligence · 2023Article
- Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.Sensors (Basel, Switzerland) · 2023Article
- DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.SN computer science · 2023Article
- How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey.Chaos, solitons, and fractals · 2022Article
- Recursive state and parameter estimation of COVID-19 circulating variants dynamics.Scientific reports · 2022Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
- Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling.Computers in biology and medicine · 2021Article
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6 authors.
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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.
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