AI-Powered Surveillance Systems for Outbreak Detection of Emerging Infectious Diseases
1Umar Tipu, 2Dr. Saima Javed, 3Dr Mahtab Akhtar, 4Dr Fazeelat Samad, 5Nazneen Tabassum, 6Qasim Raza
Submission: 31 January 2026 | Acceptance: 27 February 2026 | Publication: 07 April 2026
1Assistant Professor, Shifa International Hospital, Islamabad
2Microbiologist, Karachi Metropolitan Corporation Hospital
3Niazi Medical and Dental College
4Gomal medical college DI Khan KPK
5Hope Family Clinic Faisalabad
6Assistant Professor, PIMS Islamabad
ABSTRACT:
Background: The rapid spread of emerging infectious diseases (EIDs) posed a significant threat to global public health. Traditional surveillance systems often encountered delays in outbreak detection, limiting the effectiveness of timely responses. With advancements in artificial intelligence (AI), there was a growing interest in leveraging AI-powered surveillance systems to enhance the early detection and monitoring of outbreaks.
Aim: This study aimed to evaluate the effectiveness of AI-powered surveillance systems in detecting outbreaks of emerging infectious diseases in comparison to conventional surveillance methods.
Methods: This study was conducted at Shifa International Hospital, Islamabad, from June 2024 to May 2025. A total of 100 participants, including public health professionals, epidemiologists, and data scientists, were enrolled. Real-time health data, social media trends, environmental signals, and clinical records were integrated into an AI surveillance model developed for the study. The system’s performance was evaluated based on its sensitivity, specificity, detection speed, and predictive accuracy for EID outbreaks, and compared to conventional disease monitoring frameworks.
Results: The AI-powered surveillance system demonstrated a sensitivity of 92% and specificity of 88% in outbreak detection, significantly outperforming the traditional system, which showed 75% sensitivity and 68% specificity. Additionally, the AI model detected potential outbreaks an average of 4.2 days earlier than conventional systems. The system also identified novel patterns and correlations that were previously undetected through standard epidemiological methods.
Conclusion: AI-powered surveillance systems significantly enhanced the early detection of emerging infectious disease outbreaks compared to traditional methods. Their ability to integrate diverse data sources and detect patterns in real-time improved responsiveness and preparedness in public health responses. These findings supported the integration of AI technologies into national and global disease surveillance infrastructures.
Keywords: Artificial Intelligence, Infectious Disease Surveillance, Outbreak Detection, Emerging Diseases, Epidemiology, Public Health Technology, Machine Learning.