AI-BASED EARLY DETECTION AND SURVEILLANCE OF INFECTIOUS DISEASES IN PAKISTAN: A PUBLIC HEALTH DATA ANALYTICS APPROACH

Abstract

The prevalence of infectious diseases in Pakistan is increasing underlines the need for proactive surveillance systems proficient of early detection and timely intervention. This study investigates the application of (AI) Artificial Intelligence and public health data analytics to boost infectious disease surveillance in Pakistan. Employing multi-source datasets, including digital media feeds, electronic health records, laboratory reports, environmental indicators, this study effected machine learning, deep learning, and predictive analytics models to identify outbreak forms and high-risk localities. Results proven that AI techniques, particularly Long Short-Term Memory (LSTM) networks, outbreak prediction accuracy, significantly improved early detection and the timeliness of public health responses. The integration of unstructured and structured data additional enhanced surveillance efficiency, while infrastructural factors, institutional and technical moderated system effectiveness. The findings highlight the potential of AI-driven surveillance that inform evidence-based policy decision and transform Pakistan’s public health infrastructure.Health, Pakistan.