Artificial Intelligence in Disease Surveillance: Applications in Early Detection, Monitoring, and Control of Infectious and Emerging Diseases
- Dr Maria Saeed, Ajk mc
- Dr Tayyaba Manzoor, PMC Rawalakot
- Dr Maria khanum, AMC abbattobad
- Dr maryam javed, poonch medical college Rawalakot
- Dr Ibrar Nisar, Poonch medical college Rawalakot
- Dr Attia Mahmood, Ayub Medical College
ABSTRACT:
Background: Artificial intelligence (AI) has revolutionized disease surveillance by enhancing early detection, real-time monitoring, and predictive modeling of infectious and non-infectious diseases. AIdriven systems have improved accuracy and efficiency in tracking disease patterns, enabling timely interventions and public health responses.
Aim: This study aimed to evaluate the effectiveness of AI-based disease surveillance systems in identifying disease outbreaks, predicting trends, and improving public health decision-making.
Methods: A retrospective observational study was conducted at Services Hospital, Lahore, from February 2024 to January 2025. A total of 130 cases were analyzed to assess the impact of AI-driven surveillance tools on disease monitoring. Data were collected from hospital records, AI-integrated epidemiological tracking systems, and public health reports. The study compared AI-based predictions with traditional surveillance methods in terms of accuracy, response time, and outbreak detection efficiency. Results: AI-based surveillance significantly improved disease detection accuracy compared to conventional methods (p < 0.05). The average response time for identifying outbreaks was reduced by 35%, leading to faster public health interventions. Predictive modeling demonstrated high reliability, with a sensitivity of 92% and specificity of 89% in forecasting disease trends. AI tools effectively analyzed large datasets, identified emerging health threats, and optimized resource allocation for disease control. Conclusion: The study confirmed that AI played a crucial role in enhancing disease surveillance by improving detection accuracy, reducing response time, and enabling efficient outbreak management. Integrating AI with traditional surveillance strategies could strengthen public health preparedness and response capabilities. Further research is recommended to refine AI models for broader applications in epidemiology.
Keywords: Artificial intelligence, disease surveillance, outbreak detection, predictive modeling, public health, epidemiology, AI-driven monitoring.