Predictive Analysis of COVID-19 Positive Rate Using SAP Analytics Cloud: A Case Study

DOI

10.2991/978-2-38476-132-6_87How to use a DOI?

Keywords

COVID-19; SAP Analytics Cloud; data analysis; predictive analytics; positive rate; public health

Abstract

This paper explores the utilization of SAP Analytics Cloud for analyzing COVID-19 data and predicting the positive rate. Given the significant impact of the COVID-19 pandemic on global health and economies, accurate data analysis and forecasting have become vital for effective decision-making and resource allocation. The objective of this study is to forecast the positive rate by leveraging SAP Analytics Cloud’s advanced analytics capabilities and its integration with multiple data sources. The research methodology involves collecting COVID-19 data from reputable sources, including Kaggle, and integrating it into SAP Analytics Cloud for data processing and visualization. Predictive analytics techniques are employed to analyze the positive rate, detect patterns, identify trends, and anticipate future events. In accordance with best practices, the implementation procedure includes data preparation such as collecting data, cleaning and modeling data, data calculations, and predictive analytics. The findings of this study offer valuable insights into the trends of the COVID-19 positive rate, empowering stakeholders to make informed decisions concerning pandemic management. Additionally, the study highlights the potential of SAP Analytics Cloud as a potent instrument for data analysis and prediction within the healthcare industry. The outcomes underscore the utility of employing advanced analytics to extract actionable insights from COVID-19 data. This study contributes to the growing body of knowledge on the application of advanced analytics in public health emergencies, underscoring the effectiveness of SAP Analytics Cloud for large-scale data analysis. The methodologies and insights derived from this research can be applied to future disease outbreaks or public health crises, expanding the practicality of the findings beyond the current pandemic. The study encompasses a comprehensive analysis of data from 117 countries, representing a significant dataset for robust analysis and forecasting.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Wahyu Nofiantoro
AU  - Nisa Ismundari Wildan
PY  - 2023
DA  - 2023/10/31
TI  - Predictive Analysis of COVID-19 Positive Rate Using SAP Analytics Cloud: A Case Study
BT  - Proceedings of the 6th International Conference on Vocational Education Applied Science and Technology (ICVEAST 2023)
PB  - Atlantis Press
SP  - 1022
EP  - 1035
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-132-6_87
DO  - 10.2991/978-2-38476-132-6_87
ID  - Nofiantoro2023
ER  -