Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada

Abstract Many published infection prediction models, such as the extended SEIR (E-SEIR) model, are used as a study and report tool to aid health authorities to manage the epidemic plans successfully. These models face many challenges, mainly the reliability of the infection rate predictions related...

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Autores principales:	Yas Al-Hadeethi, Intesar F El Ramley, M. I. Sayyed
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/576a07462d42464390b9c8c6770cea34
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spelling	oai:doaj.org-article:576a07462d42464390b9c8c6770cea342021-11-28T12:20:57ZConvolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada10.1038/s41598-021-00687-82045-2322https://doaj.org/article/576a07462d42464390b9c8c6770cea342021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00687-8https://doaj.org/toc/2045-2322Abstract Many published infection prediction models, such as the extended SEIR (E-SEIR) model, are used as a study and report tool to aid health authorities to manage the epidemic plans successfully. These models face many challenges, mainly the reliability of the infection rate predictions related to the initial boundary conditions, formulation complexity, lengthy computations, and the limited result scope. We attribute these challenges to the absence of a solution framework that encapsulates the interacted activities that manage: the infection growth process, the infection spread process and the health effort process. In response to these challenges, we formulated such a framework first as the basis of our new convolution prediction model (CPM). CPM links through convolution integration, three temporal profile levels: input (infected and active cases), transformational (health efforts), and output functions (recovered, quarantine, and death cases). COVID-19 data defines the input and output temporal profiles; hence it is possible to deduce the cumulative efforts temporal response (CETR) function for the health effort level. The new CETR function determines the health effort level over a period. Also, CETR plays a role in predicting the evolution of the underlying infection and active cases profiles without a system of differential equations. This work covers three countries: Saudi Arabia, France, and Canada.Yas Al-HadeethiIntesar F El RamleyM. I. SayyedNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yas Al-Hadeethi Intesar F El Ramley M. I. Sayyed Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
description	Abstract Many published infection prediction models, such as the extended SEIR (E-SEIR) model, are used as a study and report tool to aid health authorities to manage the epidemic plans successfully. These models face many challenges, mainly the reliability of the infection rate predictions related to the initial boundary conditions, formulation complexity, lengthy computations, and the limited result scope. We attribute these challenges to the absence of a solution framework that encapsulates the interacted activities that manage: the infection growth process, the infection spread process and the health effort process. In response to these challenges, we formulated such a framework first as the basis of our new convolution prediction model (CPM). CPM links through convolution integration, three temporal profile levels: input (infected and active cases), transformational (health efforts), and output functions (recovered, quarantine, and death cases). COVID-19 data defines the input and output temporal profiles; hence it is possible to deduce the cumulative efforts temporal response (CETR) function for the health effort level. The new CETR function determines the health effort level over a period. Also, CETR plays a role in predicting the evolution of the underlying infection and active cases profiles without a system of differential equations. This work covers three countries: Saudi Arabia, France, and Canada.
format	article
author	Yas Al-Hadeethi Intesar F El Ramley M. I. Sayyed
author_facet	Yas Al-Hadeethi Intesar F El Ramley M. I. Sayyed
author_sort	Yas Al-Hadeethi
title	Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
title_short	Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
title_full	Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
title_fullStr	Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
title_full_unstemmed	Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada
title_sort	convolution model for covid-19 rate predictions and health effort levels computation for saudi arabia, france, and canada
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/576a07462d42464390b9c8c6770cea34
work_keys_str_mv	AT yasalhadeethi convolutionmodelforcovid19ratepredictionsandhealtheffortlevelscomputationforsaudiarabiafranceandcanada AT intesarfelramley convolutionmodelforcovid19ratepredictionsandhealtheffortlevelscomputationforsaudiarabiafranceandcanada AT misayyed convolutionmodelforcovid19ratepredictionsandhealtheffortlevelscomputationforsaudiarabiafranceandcanada
_version_	1718408014674788352

Convolution model for COVID-19 rate predictions and health effort levels computation for Saudi Arabia, France, and Canada

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