Analysis of infectious disease transmission and prediction through SEIQR epidemic model

In literature, various mathematical models have been developed to have a better insight into the transmission dynamics and control the spread of infectious diseases. Aiming to explore more about various aspects of infectious diseases, in this work, we propose conceptual mathematical model through a...

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Autores principales:	Tyagi Swati, Gupta Shaifu, Abbas Syed, Das Krishna Pada, Riadh Baazaoui
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	infectious disease mathematical model stability analysis long short term memory networks (lstm) parameter estimation 00a71 92b20 34d20 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/9d905a275c564357935132d878490290
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spelling	oai:doaj.org-article:9d905a275c564357935132d8784902902021-12-05T14:10:56ZAnalysis of infectious disease transmission and prediction through SEIQR epidemic model2353-062610.1515/msds-2020-0126https://doaj.org/article/9d905a275c564357935132d8784902902021-04-01T00:00:00Zhttps://doi.org/10.1515/msds-2020-0126https://doaj.org/toc/2353-0626In literature, various mathematical models have been developed to have a better insight into the transmission dynamics and control the spread of infectious diseases. Aiming to explore more about various aspects of infectious diseases, in this work, we propose conceptual mathematical model through a SEIQR (Susceptible-Exposed-Infected-Quarantined-Recovered) mathematical model and its control measurement. We establish the positivity and boundedness of the solutions. We also compute the basic reproduction number and investigate the stability of equilibria for its epidemiological relevance. To validate the model and estimate the parameters to predict the disease spread, we consider the special case for COVID-19 to study the real cases of infected cases from [2] for Russia and India. For better insight, in addition to mathematical model, a history based LSTM model is trained to learn temporal patterns in COVID-19 time series and predict future trends. In the end, the future predictions from mathematical model and the LSTM based model are compared to generate reliable results.Tyagi SwatiGupta ShaifuAbbas SyedDas Krishna PadaRiadh BaazaouiDe Gruyterarticleinfectious diseasemathematical modelstability analysislong short term memory networks (lstm)parameter estimation00a7192b2034d20MathematicsQA1-939ENNonautonomous Dynamical Systems, Vol 8, Iss 1, Pp 75-86 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	infectious disease mathematical model stability analysis long short term memory networks (lstm) parameter estimation 00a71 92b20 34d20 Mathematics QA1-939
spellingShingle	infectious disease mathematical model stability analysis long short term memory networks (lstm) parameter estimation 00a71 92b20 34d20 Mathematics QA1-939 Tyagi Swati Gupta Shaifu Abbas Syed Das Krishna Pada Riadh Baazaoui Analysis of infectious disease transmission and prediction through SEIQR epidemic model
description	In literature, various mathematical models have been developed to have a better insight into the transmission dynamics and control the spread of infectious diseases. Aiming to explore more about various aspects of infectious diseases, in this work, we propose conceptual mathematical model through a SEIQR (Susceptible-Exposed-Infected-Quarantined-Recovered) mathematical model and its control measurement. We establish the positivity and boundedness of the solutions. We also compute the basic reproduction number and investigate the stability of equilibria for its epidemiological relevance. To validate the model and estimate the parameters to predict the disease spread, we consider the special case for COVID-19 to study the real cases of infected cases from [2] for Russia and India. For better insight, in addition to mathematical model, a history based LSTM model is trained to learn temporal patterns in COVID-19 time series and predict future trends. In the end, the future predictions from mathematical model and the LSTM based model are compared to generate reliable results.
format	article
author	Tyagi Swati Gupta Shaifu Abbas Syed Das Krishna Pada Riadh Baazaoui
author_facet	Tyagi Swati Gupta Shaifu Abbas Syed Das Krishna Pada Riadh Baazaoui
author_sort	Tyagi Swati
title	Analysis of infectious disease transmission and prediction through SEIQR epidemic model
title_short	Analysis of infectious disease transmission and prediction through SEIQR epidemic model
title_full	Analysis of infectious disease transmission and prediction through SEIQR epidemic model
title_fullStr	Analysis of infectious disease transmission and prediction through SEIQR epidemic model
title_full_unstemmed	Analysis of infectious disease transmission and prediction through SEIQR epidemic model
title_sort	analysis of infectious disease transmission and prediction through seiqr epidemic model
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/9d905a275c564357935132d878490290
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_version_	1718371557460410368

Analysis of infectious disease transmission and prediction through SEIQR epidemic model

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