Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 8 , Issue 2 , PP: 27-36, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ehsan khodadadi 1 *
Doi: https://doi.org/10.54216/JAIM.080204
Influenza is often associated with millions of cases and hundreds of thousands of deaths each year, thus constituting a serious threat to public health. Traditional surveillance techniques employed in epidemiology are limited in forecasting impending outbreaks as caused by delays in receiving the relevant information and the dynamic nature of political environments. This review focuses on the available literature on the use of machine learning (ML) techniques in understanding and controlling influenza with an accent on all the sources of information available, including clinical papers, social networking sites and others. Applicable practices in classifying predictive modeling techniques, including deep learning and others, ensemble techniques, time series analysis, etc., have increased the speed and precision of the earlier results. Even so, the achievements made so far have not come on a silver platter as there are challenges, but not limited to data issues, model explain ability and strict validation processes. Some research areas are enhancing the present models to accommodate diverse virulent strains of the viruses and advancing extensive data analysis methods. It is noted in this review that machine learning strategies are essential in combating health issues and, thus, why such technologies can be deployed within a concise duration in the context of influenza epidemics for effective forecasting and resource management to salvage lives.
Influenza prediction , Machine learning , Outbreak detection , public health surveillance , Deep Learning
[1] Q. Chen et al., “Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models,” BMC Public Health, vol. 24, no. 1, pp. 1–12, Dec. 2024, doi: 10.1186/S12889-024-18583-X/FIGURES/5.
[2] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 2013.
[3] S. A. Ajagbe and M. O. Adigun, “Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review,” Multimed Tools Appl, vol. 83, no. 2, pp. 5893–5927, Jan. 2024, doi: 10.1007/S11042-023-15805-Z/TABLES/3.
[4] S. Wei et al., “The prediction of influenza-like illness using national influenza surveillance data and Baidu query data,” BMC Public Health, vol. 24, no. 1, pp. 1–12, Dec. 2024, doi: 10.1186/S12889-024-17978-0/FIGURES/3.
[5] T. J. Kieran, X. Sun, T. R. Maines, and J. A. Belser, “Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data Check for updates”, doi: 10.1038/s42003-024-06629-0.
[6] P. Mahajan, S. Uddin, F. Hajati, M. A. Moni, and E. Gide, “A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets,” Health Technol (Berl), vol. 14, no. 3, pp. 597–613, May 2024, doi: 10.1007/S12553-024-00835-W/TABLES/16.
[7] A. Hassan Zadeh, H. M. Zolbanin, R. Sharda, and D. Delen, “Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis,” Information Systems Frontiers, vol. 21, no. 4, pp. 743–760, Aug. 2019, doi: 10.1007/S10796-018-9893-0/TABLES/9.
[8] F. Liang, P. Guan, W. Wu, and D. Huang, “Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015,” PeerJ, vol. 2018, no. 6, p. e5134, Jun. 2018, doi: 10.7717/PEERJ.5134/SUPP-4.
[9] H. Bandi and D. Bertsimas, “Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions,” Sep. 18, 2020, PMLR. Accessed: Oct. 15, 2024. [Online]. Available: https://proceedings.mlr.press/v126/bandi20a.html
[10] X. Li, Y. Li, X. Shang, and H. Kong, “A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus,” Front Microbiol, vol. 15, p. 1345794, Jan. 2024, doi: 10.3389/FMICB.2024.1345794/BIBTEX.
[11] J. Henriques, T. Rocha, P. de Carvalho, C. Silva, and S. Paredes, “Interpretability and Explainability of Machine Learning Models: Achievements and Challenges,” IFMBE Proc, vol. 108, pp. 81–94, 2024, doi: 10.1007/978-3-031-59216-4_9/TABLES/1.
[12] J. Li et al., “Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions,” Mol Biol Evol, vol. 37, no. 4, pp. 1224–1236, Apr. 2020, doi: 10.1093/MOLBEV/MSZ276.
[13] R. Agarwal et al., “Neural Additive Models: Interpretable Machine Learning with Neural Nets,” Adv Neural Inf Process Syst, vol. 6, pp. 4699–4711, 2021.
[14] A. Zan et al., “DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression,” Comput Methods Programs Biomed, vol. 213, p. 106495, Jan. 2022, doi: 10.1016/J.CMPB.2021.106495.
[15] L. Du and Y. Pang, “A novel data-driven methodology for influenza outbreak detection and prediction,” Scientific Reports 2021 11:1, vol. 11, no. 1, pp. 1–16, Jun. 2021, doi: 10.1038/s41598-021-92484-6.
[16] L. Du and Y. Pang, “A novel data-driven methodology for influenza outbreak detection and prediction,” Scientific Reports 2021 11:1, vol. 11, no. 1, pp. 1–16, Jun. 2021, doi: 10.1038/s41598-021-92484-6.
[17] S. Yousefinaghani, R. A. Dara, Z. Poljak, and S. Sharif, “A decision support framework for prediction of avian influenza,” Scientific Reports 2020 10:1, vol. 10, no. 1, pp. 1–14, Nov. 2020, doi: 10.1038/s41598-020-75889-7.
[18] B. Jang, I. Kim, and J. W. Kim, “Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study,” JMIR Med Inform 2021;9(5):e23305 https://medinform.jmir.org/2021/5/e23305, vol. 9, no. 5, p. e23305, May 2021, doi: 10.2196/23305.
[19] X. Guo, N. N. Xiong, H. Wang, and J. Ren, “Design and Analysis of a Prediction System about Influenza-Like Illness from the Latent Temporal and Spatial Information,” IEEE Trans Syst Man Cybern Syst, vol. 52, no. 1, pp. 66–77, Jan. 2022, doi: 10.1109/TSMC.2020.3048946.
[20] M. Gulyaeva et al., “Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider pacific rim using big data sets,” Scientific Reports 2020 10:1, vol. 10, no. 1, pp. 1–11, Oct. 2020, doi: 10.1038/s41598-020-73664-2.
[21] E. K. Lee, H. Tian, and H. I. Nakaya, “Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks,” Hum Vaccin Immunother, vol. 16, no. 11, pp. 2690–2708, Nov. 2020, doi: 10.1080/21645515.2020.1734397.
[22] R. Yin, Z. Luo, P. Zhuang, Z. Lin, and C. K. Kwoh, “VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments,” Bioinformatics, vol. 37, no. 6, pp. 737–743, Mar. 2021, doi: 10.1093/BIOINFORMATICS/BTAA901.
[23] L. Zhao et al., “Online flu epidemiological deep modeling on disease contact network,” Geoinformatics, vol. 24, no. 2, pp. 443–475, Apr. 2020, doi: 10.1007/S10707-019-00376-9/FIGURES/16.
[24] A. Ella Hassanien, V. Snasel, S. M. Elghamrawy, A. Ella Hassnien, and C. Author, “Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19”, doi: 10.32604/cmc.2021.014767.
[25] J. Lee et al., “End-to-end Convolutional Neural Network Design for Automatic Detection of Influenza Virus,” IEEE Transactions on Smart Processing & Computing, vol. 10, no. 1, pp. 31–36, Feb. 2021, doi: 10.5573/IEIESPC.2021.10.1.031.
[26] J. Meng et al., “PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network,” Brief Bioinform, vol. 25, no. 2, pp. 1–12, Jan. 2024, doi: 10.1093/BIB/BBAE033.
[27] B. Jang, I. Kim, and J. W. Kim, “Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data,” IEEE Trans Neural Netw Learn Syst, vol. 34, no. 5, pp. 2400–2412, May 2023, doi: 10.1109/TNNLS.2021.3106637.
[28] D. S. Yoo, B. C. Chun, K. Hong, and J. Kim, “Risk Prediction of Three Different Subtypes of Highly Pathogenic Avian Influenza Outbreaks in Poultry Farms: Based on Spatial Characteristics of Infected Premises in South Korea,” Front Vet Sci, vol. 9, p. 897763, May 2022, doi: 10.3389/FVETS.2022.897763/BIBTEX.
[29] I. Mezić et al., “A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–13, Mar. 2024, doi: 10.1038/s41598-024-55798-9.
[30] D. Martín-Corral, M. García-Herranz, M. Cebrian, and E. Moro, "Social media sensors as early signals of influenza outbreaks at scale," doi: 10.1140/epjds/s13688-024-00474-1.
[31] Q. Jia, Y. Xia, F. Dong, and W. Li, “MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses,” Brief Bioinform, vol. 25, no. 5, p. 395, Jul. 2024, doi: 10.1093/BIB/BBAE395.
[32] J. Zhang, P. Zhou, Y. Zheng, and H. Wu, “Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks,” Comput Biol Med, vol. 158, p. 106807, May 2023, doi: 10.1016/J.COMPBIOMED.2023.106807.
[33] C. Cai, J. Li, Y. Xia, and W. Li, “FluPMT: Prediction of Predominant Strains of Influenza A Viruses Via Multi-Task Learning,” IEEE/ACM Trans Comput Biol Bioinform, 2024, doi: 10.1109/TCBB.2024.3378468.
