Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus

This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the v...

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Detalles Bibliográficos
Autores principales: Bilgehan, Bülent, Özyapıcı, Ali, Hammouch, Zakia, Gurefe, Yusuf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Focus
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994092/
https://www.ncbi.nlm.nih.gov/pubmed/35431642
http://dx.doi.org/10.1007/s00500-022-06996-y
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author Bilgehan, Bülent
Özyapıcı, Ali
Hammouch, Zakia
Gurefe, Yusuf
author_facet Bilgehan, Bülent
Özyapıcı, Ali
Hammouch, Zakia
Gurefe, Yusuf
author_sort Bilgehan, Bülent
collection PubMed
description This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution. It is a data-driven parametric dependent function, for which the parameters are extracted from the data and the exponential function derived using multiplicative calculus. The results of the proposed model are compared to real recorded data from different countries and the performance of this model is investigated using error analysis theory. We stress that all statistics, collected data, etc., included in this study were extracted from official website of the World Health Organization (WHO). Therefore, the obtained results demonstrate its applicability and efficiency.
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spelling pubmed-89940922022-04-11 Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus Bilgehan, Bülent Özyapıcı, Ali Hammouch, Zakia Gurefe, Yusuf Soft comput Focus This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution. It is a data-driven parametric dependent function, for which the parameters are extracted from the data and the exponential function derived using multiplicative calculus. The results of the proposed model are compared to real recorded data from different countries and the performance of this model is investigated using error analysis theory. We stress that all statistics, collected data, etc., included in this study were extracted from official website of the World Health Organization (WHO). Therefore, the obtained results demonstrate its applicability and efficiency. Springer Berlin Heidelberg 2022-04-09 2022 /pmc/articles/PMC8994092/ /pubmed/35431642 http://dx.doi.org/10.1007/s00500-022-06996-y Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Focus
Bilgehan, Bülent
Özyapıcı, Ali
Hammouch, Zakia
Gurefe, Yusuf
Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title_full Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title_fullStr Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title_full_unstemmed Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title_short Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus
title_sort predicting the spread of covid-19 with a machine learning technique and multiplicative calculus
topic Focus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994092/
https://www.ncbi.nlm.nih.gov/pubmed/35431642
http://dx.doi.org/10.1007/s00500-022-06996-y
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