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Can infectious modelling be applicable globally – lessons from COVID 19
Contagious diseases are needed to be monitored to prevent spreading within communities. Timely advice and predictions are necessary to overcome the consequences of those epidemics. Currently, emphasis has been placed on computer modelling to achieve the needed forecasts, the best example being the C...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612404/ https://www.ncbi.nlm.nih.gov/pubmed/36320817 http://dx.doi.org/10.1016/j.coesh.2022.100399 |
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author | Magana-Arachchi, Dhammika N. Wanigatunge, Rasika P. Vithanage, Meththika S. |
author_facet | Magana-Arachchi, Dhammika N. Wanigatunge, Rasika P. Vithanage, Meththika S. |
author_sort | Magana-Arachchi, Dhammika N. |
collection | PubMed |
description | Contagious diseases are needed to be monitored to prevent spreading within communities. Timely advice and predictions are necessary to overcome the consequences of those epidemics. Currently, emphasis has been placed on computer modelling to achieve the needed forecasts, the best example being the COVID-19 pandemic. Scientists used various models to determine how diverse sociodemographic factors correlated and influenced COVID-19 Global transmission and demonstrated the utility of computer models as tools in disease management. However, as modelling is done with assumptions with set rules, calculating uncertainty quantification is essential in infectious modelling when reporting the results and trustfully describing the limitations. This article summarizes the infectious disease modelling strategies, challenges, and global applicability by focusing on the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-9612404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96124042022-10-28 Can infectious modelling be applicable globally – lessons from COVID 19 Magana-Arachchi, Dhammika N. Wanigatunge, Rasika P. Vithanage, Meththika S. Curr Opin Environ Sci Health Article Contagious diseases are needed to be monitored to prevent spreading within communities. Timely advice and predictions are necessary to overcome the consequences of those epidemics. Currently, emphasis has been placed on computer modelling to achieve the needed forecasts, the best example being the COVID-19 pandemic. Scientists used various models to determine how diverse sociodemographic factors correlated and influenced COVID-19 Global transmission and demonstrated the utility of computer models as tools in disease management. However, as modelling is done with assumptions with set rules, calculating uncertainty quantification is essential in infectious modelling when reporting the results and trustfully describing the limitations. This article summarizes the infectious disease modelling strategies, challenges, and global applicability by focusing on the COVID-19 pandemic. Elsevier B.V. 2022-10-22 /pmc/articles/PMC9612404/ /pubmed/36320817 http://dx.doi.org/10.1016/j.coesh.2022.100399 Text en © 2022 Elsevier B.V. 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 Magana-Arachchi, Dhammika N. Wanigatunge, Rasika P. Vithanage, Meththika S. Can infectious modelling be applicable globally – lessons from COVID 19 |
title | Can infectious modelling be applicable globally – lessons
from COVID 19 |
title_full | Can infectious modelling be applicable globally – lessons
from COVID 19 |
title_fullStr | Can infectious modelling be applicable globally – lessons
from COVID 19 |
title_full_unstemmed | Can infectious modelling be applicable globally – lessons
from COVID 19 |
title_short | Can infectious modelling be applicable globally – lessons
from COVID 19 |
title_sort | can infectious modelling be applicable globally – lessons
from covid 19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612404/ https://www.ncbi.nlm.nih.gov/pubmed/36320817 http://dx.doi.org/10.1016/j.coesh.2022.100399 |
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