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

Research Article
Open access

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    
  • *corresponding author enhemaster@163.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

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.

En,H. (2023). Analysis of the practical application value and practice path of big data in epidemic warning. Applied and Computational Engineering,5,126-132.
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References

[1]. H. Wang, Z. Wang, Y. Dong et al., “Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China,” Cell Discovery, vol. 6, no. 1, pp. 1–8, 2020.

[2]. W. H. Organization, “Coronavirus,” 2020, https://www.who.int/health-topics/coronavirus#tab�tab_1.

[3]. Qi Xiaoliang. Big data epidemic warning mechanism:value analysis, realistic dilemma and practical path[J]. Administration and Law, 2020(4):24-33. doi:10.3969/j.issn.1007-8207.2020.04.004.

[4]. Li Lanjuan. Return peak should increase the use of information technology to prevent and control epidemics [ EB/ OL]. (2020 - 01 - 31) [2021 - 10 - 20]. http:/ / news. scien- cenet - . cn / htmlnews/ 2020 / 1 / 435284. shtm? id = 435284.

[5]. Wang B, Zhu YC. 2019 - nCoV epidemic: on the management of public health emergencies in China[J]. Science, 2020,38(7):1161 - 1166.

[6]. Fan Canghai, Shi Si. Big data governance in sudden public health events: realistic dilemmas and path optimization - taking the prevention and control of the new crown pneumonia epidemic as an example[J]. Health Soft Science,2022,36(8):81-85,96. doi:10.3969/j.issn.1003-2800.2022.08.016.

[7]. Liu Guangbo. Mouthpieces sent to Chongqing are requisitioned by Dali, Dali Health Bureau responds [N]. Xinjing News, 2020 - 02 - 05.

[8]. Wang Bo, Zhu Yuchun. 2019 - nCoV outbreak: on the management of public health emergencies in China[J]. Science, 2020,38(7):1161 - 1166.

[9]. Shan Jizhen, Cui Shuo, Zheng Pan. Design and implementation of an Internet medical services regulatory platform in Beijing[J]. China Digital Medicine,2021,16(4):22 - 25.

[10]. N. F. F. da Silva, L. F. S. Coletta, E. R. Hruschka, and E. R. Hruschka Jr., “Using unsupervised information to improve semi-supervised tweet sentiment classification,” Information Sciences, vol. 355-356, pp. 348–365, 2016.

[11]. N. Coletta, “An ensemble classification system for twitter sentiment analysis,” Procedia Computer Science, vol. 132, pp. 937–946, 2018.

[12]. Zhang Lanting. The social value and strategic choice of big data [D]. Beijing:Doctoral dissertation, Central Party School of the Communist Party of China, 2014.

Cite this article

En,H. (2023). Analysis of the practical application value and practice path of big data in epidemic warning. Applied and Computational Engineering,5,126-132.

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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About volume

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

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. H. Wang, Z. Wang, Y. Dong et al., “Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China,” Cell Discovery, vol. 6, no. 1, pp. 1–8, 2020.

[2]. W. H. Organization, “Coronavirus,” 2020, https://www.who.int/health-topics/coronavirus#tab�tab_1.

[3]. Qi Xiaoliang. Big data epidemic warning mechanism:value analysis, realistic dilemma and practical path[J]. Administration and Law, 2020(4):24-33. doi:10.3969/j.issn.1007-8207.2020.04.004.

[4]. Li Lanjuan. Return peak should increase the use of information technology to prevent and control epidemics [ EB/ OL]. (2020 - 01 - 31) [2021 - 10 - 20]. http:/ / news. scien- cenet - . cn / htmlnews/ 2020 / 1 / 435284. shtm? id = 435284.

[5]. Wang B, Zhu YC. 2019 - nCoV epidemic: on the management of public health emergencies in China[J]. Science, 2020,38(7):1161 - 1166.

[6]. Fan Canghai, Shi Si. Big data governance in sudden public health events: realistic dilemmas and path optimization - taking the prevention and control of the new crown pneumonia epidemic as an example[J]. Health Soft Science,2022,36(8):81-85,96. doi:10.3969/j.issn.1003-2800.2022.08.016.

[7]. Liu Guangbo. Mouthpieces sent to Chongqing are requisitioned by Dali, Dali Health Bureau responds [N]. Xinjing News, 2020 - 02 - 05.

[8]. Wang Bo, Zhu Yuchun. 2019 - nCoV outbreak: on the management of public health emergencies in China[J]. Science, 2020,38(7):1161 - 1166.

[9]. Shan Jizhen, Cui Shuo, Zheng Pan. Design and implementation of an Internet medical services regulatory platform in Beijing[J]. China Digital Medicine,2021,16(4):22 - 25.

[10]. N. F. F. da Silva, L. F. S. Coletta, E. R. Hruschka, and E. R. Hruschka Jr., “Using unsupervised information to improve semi-supervised tweet sentiment classification,” Information Sciences, vol. 355-356, pp. 348–365, 2016.

[11]. N. Coletta, “An ensemble classification system for twitter sentiment analysis,” Procedia Computer Science, vol. 132, pp. 937–946, 2018.

[12]. Zhang Lanting. The social value and strategic choice of big data [D]. Beijing:Doctoral dissertation, Central Party School of the Communist Party of China, 2014.

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