skip to main content
10.1145/3644116.3644197acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisaimsConference Proceedingsconference-collections
research-article

Predicting the trend and outbreak of influenza A using machine learning algorithms in Shenzhen, China

Published: 05 April 2024 Publication History

Abstract

Influenza is a major public health concern, causing significant morbidity and mortality each year. Early and accurate prediction of influenza outbreaks is essential for effective public health intervention. The proposed algorithm will use historical influenza data to identify patterns and trends in influenza outbreaks. The algorithm will then use these patterns and trends to predict future influenza outbreaks. The proposed algorithm will be evaluated using a variety of metrics, including accuracy, precision, recall and F1 score. The results of this research will provide valuable insight into the potential of machine learning algorithms for influenza prediction. This research presents a machine learning algorithm for influenza in Shenzhen, China, using data from the Shenzhen Center of Disease Control and Prevention. The algorithm uses a combination of supervised and unsupervised learning techniques to identify patterns of the data and to make predictions about future influenza outbreaks. The results of the algorithm were compared to the actual influenza cases reported by the Shenzhen Center of Disease Control, and the accuracy of the predictions was evaluated.

References

[1]
Lee SS, Viboud C, Petersen E. Understanding the rebound of influenza in the post COVID-19 pandemic period holds important clues for epidemiology and control. International Journal of Infectious Diseases. 2022 Sep 1;122:1002-4.
[2]
Wang X, Cheng XW, Ma HW, He JF, Xie X, Fang SS, et. Influenza surveillance in Shenzhen, the largest migratory metropolitan city of China, 2006–2009. Epidemiology & Infection. 2011 Oct;139(10):1551-9.
[3]
Chen S, Xu J, Wu Y, Wang X, Fang S, Cheng J, Predicting temporal propagation of seasonal influenza using improved gaussian process model. J Biomed Inform. 2019 May;93:103144.
[4]
Fuhrmann C. The effects of weather and climate on the seasonality of influenza: what we know and what we need to know. Geography Compass. 2010 Jul;4(7):718-30.
[5]
Wang G, Wei W, Jiang J, Ning C, Chen H, Huang J, Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China. Epidemiol Infect. 2019; 147:e194.
[6]
Ke GL, Meng Qi, Finley T, Wang TF, Chen W, Ma WD, 2017. LightGBM: a highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 3149–3157.
[7]
Lu JH, Zhang SX, Gu LN, Cheng XW, He JF. Design and application of the reference lines of influenza surveillance in Shenzhen City[J]. Disease Surveillance, 2007, 22(12): 799-801.

Index Terms

  1. Predicting the trend and outbreak of influenza A using machine learning algorithms in Shenzhen, China

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
    October 2023
    1394 pages
    ISBN:9798400708138
    DOI:10.1145/3644116
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 April 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ISAIMS 2023

    Acceptance Rates

    Overall Acceptance Rate 53 of 112 submissions, 47%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 17
      Total Downloads
    • Downloads (Last 12 months)17
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media