Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181488,
        author = {Dr.Rajesh Saturi and Dr.Praveen Talari and Shaik Zabeen and VANNETI LAKSHMIKAR and PAVANIKA KANCHI},
        title = {AI-DRIVEN PREDICTIVE SURVEILLANCE SYSTEM FOR INFECTIOUS DISEASE OUTBREAKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4436-4442},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181488},
        abstract = {The swift transmission of infectious diseases requires sophisticated surveillance systems for timely outbreak detection and efficient public health response. Conventional models are plagued by latency and lack of flexibility. This study designs an AI-based predictive surveillance system with the combination of machine learning (ML), natural language processing (NLP), and geospatial analytics to improve outbreak prediction accuracy and response efficiency. The methodology consists of deep learning models for time-series prediction, NLP for social media and news processing, and geospatial analytics for visualization of disease spread. Multimodal data fusion supports real-time monitoring, and cloud-based architecture supports accessibility and collaboration with health authorities. The system overcomes difficulties in data integration, real-time flexibility, and interpretability, providing efficient infectious disease surveillance and decision-making. This study contributes to AI-based disease monitoring by formulating a scalable and accurate method of outbreak prediction. With the combination of heterogeneous data sources and sophisticated analytics, the system improves early detection capability and reduces the impact of outbreaks and aids active public health measures.},
        keywords = {AI-driven surveillance, infectious disease prediction, machine learning, natural language processing, geospatial analytics, public health monitoring, outbreak detection.},
        month = {June},
        }