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

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.