A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation...

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Autores principales:	Yukun Tan, Durward Cator III, Martial Ndeffo-Mbah, Ulisses Braga-Neto
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	epidemic model seird model nonlinear stochastic model unscented kalman filter maximum likelihood adaptive filtering parameter estimation Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/95326ea6d298413f9deb1de05bf51620
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id	oai:doaj.org-article:95326ea6d298413f9deb1de05bf51620
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spelling	oai:doaj.org-article:95326ea6d298413f9deb1de05bf516202021-11-23T02:32:22ZA stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread10.3934/mbe.20213811551-0018https://doaj.org/article/95326ea6d298413f9deb1de05bf516202021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021381?viewType=HTMLhttps://doaj.org/toc/1551-0018Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.Yukun TanDurward Cator III Martial Ndeffo-MbahUlisses Braga-Neto AIMS Pressarticleepidemic modelseird modelnonlinear stochastic modelunscented kalman filtermaximum likelihoodadaptive filteringparameter estimationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7685-7710 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	epidemic model seird model nonlinear stochastic model unscented kalman filter maximum likelihood adaptive filtering parameter estimation Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	epidemic model seird model nonlinear stochastic model unscented kalman filter maximum likelihood adaptive filtering parameter estimation Biotechnology TP248.13-248.65 Mathematics QA1-939 Yukun Tan Durward Cator III Martial Ndeffo-Mbah Ulisses Braga-Neto A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
description	Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.
format	article
author	Yukun Tan Durward Cator III Martial Ndeffo-Mbah Ulisses Braga-Neto
author_facet	Yukun Tan Durward Cator III Martial Ndeffo-Mbah Ulisses Braga-Neto
author_sort	Yukun Tan
title	A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
title_short	A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
title_full	A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
title_fullStr	A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
title_full_unstemmed	A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread
title_sort	stochastic metapopulation state-space approach to modeling and estimating covid-19 spread
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/95326ea6d298413f9deb1de05bf51620
work_keys_str_mv	AT yukuntan astochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT durwardcatoriii astochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT martialndeffombah astochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT ulissesbraganeto astochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT yukuntan stochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT durwardcatoriii stochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT martialndeffombah stochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread AT ulissesbraganeto stochasticmetapopulationstatespaceapproachtomodelingandestimatingcovid19spread
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A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

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