Computational Epidemiological Analysis of the Impact of Artificial Intelligence on the Early Detection of Infectious Diseases

The integration of artificial intelligence (AI) into modern computational epidemiology has revolutionized early detection and surveillance of infectious diseases. This study employs a comparative computational framework combining neural networks, ensemble machine learning models, and regression-based predictive analytics to analyze multi-source datasets, including clinical records, genomic sequences, environmental indicators, and population mobility data. Our results demonstrate that AI-driven models can identify subtle transmission patterns and epidemic clusters up to two weeks earlier than traditional surveillance methods, enhancing diagnostic accuracy and enabling timely public health interventions. The application of infectious disease surveillance frameworks augmented by AI supports proactive containment strategies, optimized resource allocation, and data-driven policy decisions. Despite these advancements, challenges persist regarding data quality, model interpretability, and ethical governance of sensitive health data. Overall, AI-powered epidemiology offers a transformative approach to early diagnosis, bridging computational data science and evidence-based public health, and provides a scalable framework for global infectious disease preparedness.

کلیدواژه ها:

Computational Epidemiology ، Artificial Intelligence ، Machine Learning ، Early Diagnosis ، Infectious Disease Surveillance