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

Melpakkam Pradeep and Karthik Raman

PLOS ONE, 2023, vol. 18, issue 7, 1-14

Abstract: 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/.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0284076

DOI: 10.1371/journal.pone.0284076

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