Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing

The novel coronavirus disease 2019 (COVID-19) is a novel and exponentially growing disease, and consequently, the accelerated development of knowledge from good data is possible quickly and globally. In order to combat the global pandemic of COVID-19, all humans on earth need to make difficult strat...

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Detalles Bibliográficos
Autores principales: Galvin, Cooper J, Fernandez-Luque, Luis, Li, Yu-Chuan (Jack)
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Inc. 2021
Materias:
Commentaries
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204661/
https://www.ncbi.nlm.nih.gov/pubmed/34167045
http://dx.doi.org/10.1016/j.diagmicrobio.2020.115070
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author Galvin, Cooper J
Fernandez-Luque, Luis
Li, Yu-Chuan (Jack)
author_facet Galvin, Cooper J
Fernandez-Luque, Luis
Li, Yu-Chuan (Jack)
author_sort Galvin, Cooper J
collection PubMed
description The novel coronavirus disease 2019 (COVID-19) is a novel and exponentially growing disease, and consequently, the accelerated development of knowledge from good data is possible quickly and globally. In order to combat the global pandemic of COVID-19, all humans on earth need to make difficult strategic decisions on three very different scales, all fueled by Analytical and Artificial Intelligence-based predictive Models.
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spelling pubmed-72046612020-05-07 Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing Galvin, Cooper J Fernandez-Luque, Luis Li, Yu-Chuan (Jack) Diagn Microbiol Infect Dis Commentaries The novel coronavirus disease 2019 (COVID-19) is a novel and exponentially growing disease, and consequently, the accelerated development of knowledge from good data is possible quickly and globally. In order to combat the global pandemic of COVID-19, all humans on earth need to make difficult strategic decisions on three very different scales, all fueled by Analytical and Artificial Intelligence-based predictive Models. Published by Elsevier Inc. 2021-10 2020-05-07 /pmc/articles/PMC7204661/ /pubmed/34167045 http://dx.doi.org/10.1016/j.diagmicrobio.2020.115070 Text en © 2020 Published by Elsevier Inc. 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 Commentaries
Galvin, Cooper J
Fernandez-Luque, Luis
Li, Yu-Chuan (Jack)
Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title_full Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title_fullStr Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title_full_unstemmed Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title_short Accelerating the global response against the exponentially growing COVID-19 outbreak through decent data sharing
title_sort accelerating the global response against the exponentially growing covid-19 outbreak through decent data sharing
topic Commentaries
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204661/
https://www.ncbi.nlm.nih.gov/pubmed/34167045
http://dx.doi.org/10.1016/j.diagmicrobio.2020.115070
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