Analysis and trend prediction of COVID-19 pandemic data based on big data visualization

Xinyuan Lu

doi:10.54254/2755-2721/69/20241513

Research Article

Open access

Published on 25 July 2024

Download pdf

Export citation

Analysis and trend prediction of COVID-19 pandemic data based on big data visualization

Xinyuan Lu *^,1,

¹ Yango UniversityNo. 99 Denglong Road, Mawei Town, Mawei District, Fuzhou City, Fujian Province, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/69/20241513

Abstract

Since the outbreak of COVID-19 at the end of 2019, this global public health crisis has profoundly impacted the socio-economic conditions and daily life of countries worldwide. To effectively combat the pandemic, scientists and public health experts rely on vast amounts of data to track the progression of the disease, evaluate the effectiveness of control measures, and predict future trends. Big data technology plays a crucial role in the analysis of pandemic data and trend forecasting. This paper will explore the methods of analyzing COVID-19 pandemic data and the application of trend forecasting.

Keywords

Big Data Analysis, Pandemic, Data Visualization, Control Measures, Future Trends

View pdf

References

[1]. World Health Organization. COVID-19 Dashboard. [Internet]. 2020 [cited 2024 May 30]. Available from: https://covid19.who.int/

[2]. Johns Hopkins University. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE). [Internet]. 2020 [cited 2024 May 30]. Available from: https://github.com/CSSEGISandData/COVID-19

[3]. Tableau Software. Tableau COVID-19 Data Hub. [Internet]. 2020 [cited 2024 May 30]. Available from: https://www.tableau.com/covid-19-coronavirus-data-resources

[4]. Microsoft Power BI. COVID-19 Tracking Report. [Internet]. 2020 [cited 2024 May 30]. Available from: https://powerbi.microsoft.com/en-us/blog/covid-19-tracking-report/

[5]. McKinsey & Company. How data visualization supports rapid crisis decision-making. [Internet]. 2020 [cited 2024 May 30]. Available from: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-data-visualization-supports-rapid-crisis-decision-making

[6]. Ahmed I, Ahmad M, Jeon G, Piccialli F. A framework for pandemic prediction using big data analytics. Big Data Research. 2021 Jul 15;25:100190.

[7]. Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS. Applications of big data analytics to control COVID-19 pandemic. Sensors. 2021 Mar 24;21(7):2282.

[8]. Sengupta S, Mugde S, Sharma G. Covid-19 pandemic data analysis and forecasting using machine learning algorithms. MedRxiv. 2020 Jun 26:2020-06.

[9]. Clement F, Kaur A, Sedghi M, Krishnaswamy D, Punithakumar K. Interactive data-driven visualization for COVID-19 with trends, analytics and forecasting. In2020 24th International Conference Information Visualisation (IV) 2020 Sep 7 (pp. 593-598). IEEE.

[10]. Pan Z, Nguyen HL, Abu-Gellban H, Zhang Y. Google trends analysis of covid-19 pandemic. In2020 IEEE International Conference on Big Data (Big Data) 2020 Dec 10 (pp. 3438-3446). IEEE.

Cite this article

Lu,X. (2024). Analysis and trend prediction of COVID-19 pandemic data based on big data visualization. Applied and Computational Engineering,69,179-187.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-459-0(Print) / 978-1-83558-460-6(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.69

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).