Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Analysis of the practical application value and practice path of big data in epidemic warning

He En * 1
1 Maple Leaf School, Toronto, Ontario, Canada, M1S 4R5

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 126-132
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation He En. Analysis of the practical application value and practice path of big data in epidemic warning. ACE (2023) Vol. 5: 126-132. DOI: 10.54254/2755-2721/5/20230546.

Abstract

Late in 2019, the unique viral disease coronavirus disease, or COVID-19, initially appeared. On March 11, 2020, the World Health Organization (WHO) proclaimed the COVID-19 outbreak a pandemic. It rapidly spread to every corner of the globe. This paper examines the use and actual application of big data in epidemic early warning. Based on the analysis of the value of big data epidemic early warning mechanisms, this paper divides the current big data epidemic early warning systems into three main categories according to the various channels of data acquisition: early warning systems based on the Internet and communication systems, early warning systems based on electronic medical information, and early warning mechanisms based on the Internet of Things information collection.

Keywords

public health event, big data, artificial intelligence, early warning mechanism, COVID-19.

References

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Data Availability

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

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230546
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated