Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States

COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical intervent...

Descripción completa

Detalles Bibliográficos
Autor principal: Garg, Ashish
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068982/
http://dx.doi.org/10.1016/B978-0-323-90054-6.00008-8
_version_ 1784700337387470848
author Garg, Ashish
author_facet Garg, Ashish
author_sort Garg, Ashish
collection PubMed
description COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical interventions (NPIs), which are behavioral changes to a person and community. Most of the nations worldwide have imposed NPIs in the form of social distancing and lockdowns, which have been effective in reducing the pace of the virus's spread, but continued implementation has deemed social and economic losses. Hence strategic implementation of NPIs in a burst of periods should be done based on educated decisions using data about population mobility trends to find hot zones that lead to a spike in cases. These decisions will positively impact the virus's spread with lower damage to social and economic aspects.
format Online
Article
Text
id pubmed-9068982
institution National Center for Biotechnology Information
language English
publishDate 2022
record_format MEDLINE/PubMed
spelling pubmed-90689822022-05-04 Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States Garg, Ashish Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 Article COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical interventions (NPIs), which are behavioral changes to a person and community. Most of the nations worldwide have imposed NPIs in the form of social distancing and lockdowns, which have been effective in reducing the pace of the virus's spread, but continued implementation has deemed social and economic losses. Hence strategic implementation of NPIs in a burst of periods should be done based on educated decisions using data about population mobility trends to find hot zones that lead to a spike in cases. These decisions will positively impact the virus's spread with lower damage to social and economic aspects. 2022 2022-04-08 /pmc/articles/PMC9068982/ http://dx.doi.org/10.1016/B978-0-323-90054-6.00008-8 Text en Copyright © 2022 Elsevier Inc. 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
Garg, Ashish
Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title_full Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title_fullStr Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title_full_unstemmed Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title_short Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
title_sort using interpretable machine learning identify factors contributing to covid-19 cases in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068982/
http://dx.doi.org/10.1016/B978-0-323-90054-6.00008-8
work_keys_str_mv AT gargashish usinginterpretablemachinelearningidentifyfactorscontributingtocovid19casesintheunitedstates

Ejemplares similares