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Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling

This paper presents a new stochastic-based method for modelling and analysis of COVID-19 spread. A new deterministic Susceptible, Exposed, Infectious, Recovered (Re-infected) and Deceased-based Social Distancing model, named SEIR(R)D-SD, is proposed by introducing the re-infection rate and social di...

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Autores principales: Zhu, Xinhe, Gao, Bingbing, Zhong, Yongmin, Gu, Chengfan, Choi, Kup-Sze
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401085/
https://www.ncbi.nlm.nih.gov/pubmed/34478923
http://dx.doi.org/10.1016/j.compbiomed.2021.104810
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author Zhu, Xinhe
Gao, Bingbing
Zhong, Yongmin
Gu, Chengfan
Choi, Kup-Sze
author_facet Zhu, Xinhe
Gao, Bingbing
Zhong, Yongmin
Gu, Chengfan
Choi, Kup-Sze
author_sort Zhu, Xinhe
collection PubMed
description This paper presents a new stochastic-based method for modelling and analysis of COVID-19 spread. A new deterministic Susceptible, Exposed, Infectious, Recovered (Re-infected) and Deceased-based Social Distancing model, named SEIR(R)D-SD, is proposed by introducing the re-infection rate and social distancing factor into the traditional SEIRD (Susceptible, Exposed, Infectious, Recovered and Deceased) model to account for the effects of re-infection and social distancing on COVID-19 spread. The deterministic SEIRD(R)D-SD model is further converted into the stochastic form to account for uncertainties involved in COVID-19 spread. Based on this, an extended Kalman filter (EKF) is developed based on the stochastic SEIR(R)D-SD model to simultaneously estimate both model parameters and transmission state of COVID-19 spread. Simulation results and comparison analyses demonstrate that the proposed method can effectively account for the re-infection and social distancing as well as uncertain effects on COVID-19 spread, leading to improved accuracy for prediction of COVID-19 spread.
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spelling pubmed-84010852021-08-30 Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling Zhu, Xinhe Gao, Bingbing Zhong, Yongmin Gu, Chengfan Choi, Kup-Sze Comput Biol Med Article This paper presents a new stochastic-based method for modelling and analysis of COVID-19 spread. A new deterministic Susceptible, Exposed, Infectious, Recovered (Re-infected) and Deceased-based Social Distancing model, named SEIR(R)D-SD, is proposed by introducing the re-infection rate and social distancing factor into the traditional SEIRD (Susceptible, Exposed, Infectious, Recovered and Deceased) model to account for the effects of re-infection and social distancing on COVID-19 spread. The deterministic SEIRD(R)D-SD model is further converted into the stochastic form to account for uncertainties involved in COVID-19 spread. Based on this, an extended Kalman filter (EKF) is developed based on the stochastic SEIR(R)D-SD model to simultaneously estimate both model parameters and transmission state of COVID-19 spread. Simulation results and comparison analyses demonstrate that the proposed method can effectively account for the re-infection and social distancing as well as uncertain effects on COVID-19 spread, leading to improved accuracy for prediction of COVID-19 spread. Elsevier Ltd. 2021-10 2021-08-28 /pmc/articles/PMC8401085/ /pubmed/34478923 http://dx.doi.org/10.1016/j.compbiomed.2021.104810 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Zhu, Xinhe
Gao, Bingbing
Zhong, Yongmin
Gu, Chengfan
Choi, Kup-Sze
Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title_full Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title_fullStr Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title_full_unstemmed Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title_short Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
title_sort extended kalman filter based on stochastic epidemiological model for covid-19 modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401085/
https://www.ncbi.nlm.nih.gov/pubmed/34478923
http://dx.doi.org/10.1016/j.compbiomed.2021.104810
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