COVID-19 prediction models: a systematic literature review

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sheikh Muzaffar Shakeel, Nithya Sathya Kumar, Pranita Pandurang Madalli, Rashmi Srinivasaiah, Devappa Renuka Swamy
Formato:	article
Lenguaje:	EN
Publicado:	Korea Centers for Disease Control & Prevention 2021
Materias:	covid-19 data science forecasting healthcare management models Special situations and conditions RC952-1245 Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/6c558cc2592c4163b3ac604394912734
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6c558cc2592c4163b3ac604394912734
record_format	dspace
spelling	oai:doaj.org-article:6c558cc2592c4163b3ac6043949127342021-11-05T00:06:00ZCOVID-19 prediction models: a systematic literature review2210-90992210-911010.24171/j.phrp.2021.0100https://doaj.org/article/6c558cc2592c4163b3ac6043949127342021-08-01T00:00:00Zhttp://ophrp.org/upload/pdf/j-phrp-2021-0100.pdfhttps://doaj.org/toc/2210-9099https://doaj.org/toc/2210-9110As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.Sheikh Muzaffar ShakeelNithya Sathya KumarPranita Pandurang MadalliRashmi SrinivasaiahDevappa Renuka SwamyKorea Centers for Disease Control & Preventionarticlecovid-19data scienceforecastinghealthcare managementmodelsSpecial situations and conditionsRC952-1245Infectious and parasitic diseasesRC109-216ENOsong Public Health and Research Perspectives, Vol 12, Iss 4, Pp 215-229 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 data science forecasting healthcare management models Special situations and conditions RC952-1245 Infectious and parasitic diseases RC109-216
spellingShingle	covid-19 data science forecasting healthcare management models Special situations and conditions RC952-1245 Infectious and parasitic diseases RC109-216 Sheikh Muzaffar Shakeel Nithya Sathya Kumar Pranita Pandurang Madalli Rashmi Srinivasaiah Devappa Renuka Swamy COVID-19 prediction models: a systematic literature review
description	As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
format	article
author	Sheikh Muzaffar Shakeel Nithya Sathya Kumar Pranita Pandurang Madalli Rashmi Srinivasaiah Devappa Renuka Swamy
author_facet	Sheikh Muzaffar Shakeel Nithya Sathya Kumar Pranita Pandurang Madalli Rashmi Srinivasaiah Devappa Renuka Swamy
author_sort	Sheikh Muzaffar Shakeel
title	COVID-19 prediction models: a systematic literature review
title_short	COVID-19 prediction models: a systematic literature review
title_full	COVID-19 prediction models: a systematic literature review
title_fullStr	COVID-19 prediction models: a systematic literature review
title_full_unstemmed	COVID-19 prediction models: a systematic literature review
title_sort	covid-19 prediction models: a systematic literature review
publisher	Korea Centers for Disease Control & Prevention
publishDate	2021
url	https://doaj.org/article/6c558cc2592c4163b3ac604394912734
work_keys_str_mv	AT sheikhmuzaffarshakeel covid19predictionmodelsasystematicliteraturereview AT nithyasathyakumar covid19predictionmodelsasystematicliteraturereview AT pranitapandurangmadalli covid19predictionmodelsasystematicliteraturereview AT rashmisrinivasaiah covid19predictionmodelsasystematicliteraturereview AT devapparenukaswamy covid19predictionmodelsasystematicliteraturereview
_version_	1718444532566065152

COVID-19 prediction models: a systematic literature review

Ejemplares similares