Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)

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Comparison on Machine Learning Methods for Infectious Diseases Prediction

Authors
Rongrong Chen1, *
1SWUFE-UD Institute of Data Science at SWUFE, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China
*Corresponding author. Email: mymyself@udel.edu
Corresponding Author
Rongrong Chen
Available Online 19 December 2024.
DOI
10.2991/978-94-6463-598-0_24How to use a DOI?
Keywords
Machine Learning; Infectious Prediction; Prediction Models
Abstract

The primary focus of this study is to explore the predictive effectiveness of various machine learning and time series models. By leveraging three years of pandemic data from China, the research aims to identify the optimal predictive models for different infectious disease patterns, thereby contributing significantly to future pandemic prognosis and providing rigorous validation for pandemic prevention and control measures. This comprehensive study reviews previous research and selects the most representative and validated predictive models. Most of these models have been used to predict infectious disease include the Seasonal Autoregressive Integrated Moving Average (SARIMA), Exponential Smoothing State Space Model (ETS), Long Short-Term Memory (LSTM), Hybrid Models, Trigonometric, Box-Cox transformation. By incorporating these advanced predictive models, the study aims to improve research efficiency and accuracy in forecasting infectious disease trends. The ultimate goal is to provide robust tools and methodologies that can be utilized for effective pandemic management, helping policymakers and health professionals to make informed decisions and implement timely interventions.

Copyright
© 2024 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.

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Volume Title
Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
19 December 2024
ISBN
978-94-6463-598-0
ISSN
2352-5428
DOI
10.2991/978-94-6463-598-0_24How to use a DOI?
Copyright
© 2024 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.

Cite this article

risenwbib
TY  - CONF
AU  - Rongrong Chen
PY  - 2024
DA  - 2024/12/19
TI  - Comparison on Machine Learning Methods for Infectious Diseases Prediction
BT  - Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)
PB  - Atlantis Press
SP  - 233
EP  - 248
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-598-0_24
DO  - 10.2991/978-94-6463-598-0_24
ID  - Chen2024
ER  -