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SBDiEM: A new mathematical model of infectious disease dynamics
A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177179/ https://www.ncbi.nlm.nih.gov/pubmed/32327901 http://dx.doi.org/10.1016/j.chaos.2020.109828 |
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author | Bekiros, Stelios Kouloumpou, Dimitra |
author_facet | Bekiros, Stelios Kouloumpou, Dimitra |
author_sort | Bekiros, Stelios |
collection | PubMed |
description | A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers. |
format | Online Article Text |
id | pubmed-7177179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71771792020-04-23 SBDiEM: A new mathematical model of infectious disease dynamics Bekiros, Stelios Kouloumpou, Dimitra Chaos Solitons Fractals Article A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers. Elsevier Ltd. 2020-07 2020-04-23 /pmc/articles/PMC7177179/ /pubmed/32327901 http://dx.doi.org/10.1016/j.chaos.2020.109828 Text en © 2020 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 Bekiros, Stelios Kouloumpou, Dimitra SBDiEM: A new mathematical model of infectious disease dynamics |
title | SBDiEM: A new mathematical model of infectious disease dynamics |
title_full | SBDiEM: A new mathematical model of infectious disease dynamics |
title_fullStr | SBDiEM: A new mathematical model of infectious disease dynamics |
title_full_unstemmed | SBDiEM: A new mathematical model of infectious disease dynamics |
title_short | SBDiEM: A new mathematical model of infectious disease dynamics |
title_sort | sbdiem: a new mathematical model of infectious disease dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177179/ https://www.ncbi.nlm.nih.gov/pubmed/32327901 http://dx.doi.org/10.1016/j.chaos.2020.109828 |
work_keys_str_mv | AT bekirosstelios sbdiemanewmathematicalmodelofinfectiousdiseasedynamics AT kouloumpoudimitra sbdiemanewmathematicalmodelofinfectiousdiseasedynamics |