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Article

Attention-Driven Deep Learning for News-Based Prediction of Disease Outbreaks

by
Avneet Singh Gautam
1,
Zahid Raza
1,
Maria Lapina
2,3 and
Mikhail Babenko
2,3,*
1
School of Computer and Systems Sciences, Jawaharlal Nehru University, 110067 New Delhi, India
2
Research Center for Trusted Artificial Intelligence, Ivannikov Institute for System Programming of the Russian Academy of Science, 109004 Moscow, Russia
3
Department of Computational Mathematics and Cybernetics, North-Caucasus Federal University, 355017 Stavropol, Russia
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2025, 9(11), 291; https://doi.org/10.3390/bdcc9110291
Submission received: 25 September 2025 / Revised: 6 November 2025 / Accepted: 10 November 2025 / Published: 14 November 2025

Abstract

Natural Language Processing is being used for Disease Outbreak Prediction using news data. However, the available research focuses on predicting outbreaks for only specific diseases using disease-specific data such as COVID-19, Zika, SARS, MERS, and Ebola, etc. To address the challenge of disease outbreak prediction without relying on prior knowledge or introducing bias, this research proposes a model that leverages a news dataset devoid of specific disease names. This approach ensures generalizability and domain independence in identifying potential outbreaks. To facilitate supervised learning, spaCy was employed to annotate the dataset, enabling the classification of articles as either related or unrelated to disease outbreaks. LSTM, Bi-LSTM, and Bi-LSTM with a Multi-Head Attention mechanism, and transformer have been used and compared for the purpose of classification. Experimental results exhibit good prediction accuracy with Bi-LSTM with Multi-Head Attention and transformer on the test dataset. The work serves as a pro-active and unbiased approach to predict any disease outbreak without being specific to any disease.
Keywords: disease outbreak prediction; news data; LSTM; Bi-LSTM; attention mechanism; transformer disease outbreak prediction; news data; LSTM; Bi-LSTM; attention mechanism; transformer

Share and Cite

MDPI and ACS Style

Gautam, A.S.; Raza, Z.; Lapina, M.; Babenko, M. Attention-Driven Deep Learning for News-Based Prediction of Disease Outbreaks. Big Data Cogn. Comput. 2025, 9, 291. https://doi.org/10.3390/bdcc9110291

AMA Style

Gautam AS, Raza Z, Lapina M, Babenko M. Attention-Driven Deep Learning for News-Based Prediction of Disease Outbreaks. Big Data and Cognitive Computing. 2025; 9(11):291. https://doi.org/10.3390/bdcc9110291

Chicago/Turabian Style

Gautam, Avneet Singh, Zahid Raza, Maria Lapina, and Mikhail Babenko. 2025. "Attention-Driven Deep Learning for News-Based Prediction of Disease Outbreaks" Big Data and Cognitive Computing 9, no. 11: 291. https://doi.org/10.3390/bdcc9110291

APA Style

Gautam, A. S., Raza, Z., Lapina, M., & Babenko, M. (2025). Attention-Driven Deep Learning for News-Based Prediction of Disease Outbreaks. Big Data and Cognitive Computing, 9(11), 291. https://doi.org/10.3390/bdcc9110291

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