A Novel Multi-Stage Rule-Based Information Extraction Framework for Disease Outbreak Detection with Enhanced Geographical Granularity
Received: 19 September 2025 | Revised: 7 October 2025, 19 October 2025, and 21 October 2025 | Accepted: 22 October 2025 | Online: 19 November 2025
Corresponding author: Manju Joy
Abstract
A critical limitation of modern event-based bio-surveillance systems is their reliance on headline-level data rather than a systematic analysis of the underlying news narratives. Although they provide general awareness, they often lack the geographical granularity needed to generate actionable insights and support location-specific public health interventions. To address this limitation, this study presents a method for extracting epidemic-related information from unstructured text corpora on digital platforms. By transforming unstructured text into a structured format using advanced NLP techniques, this approach facilitates the visualization of outbreak information on Google Maps with enhanced spatial detail. Kerala is recognized as the "Alarm bell of India" for consistently reporting the first instance of many outbreaks that have emerged throughout the country, including the first Nipah outbreak in 2018, the first COVID-19 case in 2020, and the index Monkeypox case in 2022. This pattern highlights Kerala's pivotal role in the early detection of emerging infectious diseases, highlighting the need for a more advanced and efficient bio-surveillance system to enhance public health preparedness. This study presents a novel approach to identify entities related to outbreaks using an optimized DistilBERT model, combined with a multi-stage rule-based method for automated information extraction from unstructured text corpora, achieving an overall precision of 94.86%. Experimental results demonstrate that the proposed framework is highly effective in tracking diseases that severely impact public health and disrupt socio-economic stability.
Keywords:
information extraction, named entity recognition, co-reference resolution, outbreak detection, epidemic surveillanceDownloads
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