{"title":"Data Science in a Pandemic","authors":"Dennis F. X. Mathaisel","doi":"10.5334/dsj-2023-041","DOIUrl":null,"url":null,"abstract":"Data Science has the potential to provide humanity with critical insight into the massive data being collected during a pandemic. The COVID-19 pandemic presented that opportunity, and Data Science supported an international audience promptly, reliably, effectively, and frequently during that difficult time. The most significant contributions were data visualizations and data dashboards, however, other tools, such as predictive and prescriptive analytics, were equally critical to the effort. The urgency at the start of the pandemic was to quickly communicate information to citizens, governments, and institutions. The change in modality from traditional statistical metrics and tables to data visualizations was extremely significant and helpful to so many. This paper reviews these contributions by demonstrating how the COVID-19 story unfolded through author-generated data visualizations and dashboards, and by providing the community with open-source access to the scripts that generated these visualizations. The open-source access to the (R language) scripts reflects this article’s novelty in the literature. Using publicly available datasets from multiple sources, and employing R toolkits, the author validates the role that Data Science can play in a pandemic, and that can be implemented by anyone with some basic knowledge of scripting languages, like R. The intent is to provide these valuable tools to the community and to demonstrate their effectiveness in the likely event when there is another crisis.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/dsj-2023-041","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Data Science has the potential to provide humanity with critical insight into the massive data being collected during a pandemic. The COVID-19 pandemic presented that opportunity, and Data Science supported an international audience promptly, reliably, effectively, and frequently during that difficult time. The most significant contributions were data visualizations and data dashboards, however, other tools, such as predictive and prescriptive analytics, were equally critical to the effort. The urgency at the start of the pandemic was to quickly communicate information to citizens, governments, and institutions. The change in modality from traditional statistical metrics and tables to data visualizations was extremely significant and helpful to so many. This paper reviews these contributions by demonstrating how the COVID-19 story unfolded through author-generated data visualizations and dashboards, and by providing the community with open-source access to the scripts that generated these visualizations. The open-source access to the (R language) scripts reflects this article’s novelty in the literature. Using publicly available datasets from multiple sources, and employing R toolkits, the author validates the role that Data Science can play in a pandemic, and that can be implemented by anyone with some basic knowledge of scripting languages, like R. The intent is to provide these valuable tools to the community and to demonstrate their effectiveness in the likely event when there is another crisis.
期刊介绍:
The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.