COWAVE: A labelled COVID-19 wave dataset for building predictive models.

The ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount o...

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Main Authors: Melpakkam Pradeep, Karthik Raman
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0284076
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author Melpakkam Pradeep
Karthik Raman
author_facet Melpakkam Pradeep
Karthik Raman
author_sort Melpakkam Pradeep
collection DOAJ
description The ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount of data being available in the public domain. In this manuscript, we collate COVID-19 case data from around the world (available on the World Health Organization (WHO) website), and provide various definitions for waves. Using these definitions to define labels, we create a labelled dataset, which can be used while building supervised learning classifiers. We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. This dataset will be a valuable resource for epidemiologists and others interested in the early prediction of future waves. The datasets are available from https://github.com/RamanLab/COWAVE/.
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publisher Public Library of Science (PLoS)
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spelling doaj-art-130dc4e174644e72a8ae938f94ce39db2025-08-20T03:57:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01187e028407610.1371/journal.pone.0284076COWAVE: A labelled COVID-19 wave dataset for building predictive models.Melpakkam PradeepKarthik RamanThe ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount of data being available in the public domain. In this manuscript, we collate COVID-19 case data from around the world (available on the World Health Organization (WHO) website), and provide various definitions for waves. Using these definitions to define labels, we create a labelled dataset, which can be used while building supervised learning classifiers. We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. This dataset will be a valuable resource for epidemiologists and others interested in the early prediction of future waves. The datasets are available from https://github.com/RamanLab/COWAVE/.https://doi.org/10.1371/journal.pone.0284076
spellingShingle Melpakkam Pradeep
Karthik Raman
COWAVE: A labelled COVID-19 wave dataset for building predictive models.
PLoS ONE
title COWAVE: A labelled COVID-19 wave dataset for building predictive models.
title_full COWAVE: A labelled COVID-19 wave dataset for building predictive models.
title_fullStr COWAVE: A labelled COVID-19 wave dataset for building predictive models.
title_full_unstemmed COWAVE: A labelled COVID-19 wave dataset for building predictive models.
title_short COWAVE: A labelled COVID-19 wave dataset for building predictive models.
title_sort cowave a labelled covid 19 wave dataset for building predictive models
url https://doi.org/10.1371/journal.pone.0284076
work_keys_str_mv AT melpakkampradeep cowavealabelledcovid19wavedatasetforbuildingpredictivemodels
AT karthikraman cowavealabelledcovid19wavedatasetforbuildingpredictivemodels