INTRODUCTION:
digital transformation in public health has highlighted critical gaps in early detection systems for infectious diseases, especially in low resource settings. traditional surveillance methods often rely on delayed reporting, fragmented data sources, and limited analytic capacity, creating blind spots that undermine timely response. these weaknesses became more visible during recent global health emergencies, revealing the need for adaptive, data-driven systems that integrate environmental, behavioural, and clinical signals. this abstract presents a conceptual innovation, crejiene artificial intelligence, an emerging digital platform designed to enhance early prediction of infectious disease risks.

METHODS:
crejiene artificial intelligence is being developed as an integrated machine learning system that synthesises epidemiological trends, climatic indicators, mobility patterns, community level symptom signals, and digital health information streams. the conceptual method includes: feature engineering to identify early risk markers; machine learning models for anomaly detection and spatiotemporal forecasting; a digital reporting interface that captures community submitted signals; risk classification dashboards for subnational health authorities; cybersecurity and ethical protocols for responsible data management.

RESULTS:
as a work in progress, preliminary modelling suggests that integrating multisource data enhances sensitivity for detecting early shifts in disease patterns compared to single source surveillance. simulated outputs show improved capacity for identifying clusters of respiratory and climate sensitive infections. projected implementation indicates potential benefits for outbreak readiness, targeted alerts, and improved health information flow.

CONCLUSIONS:
crejiene artificial intelligence represents a promising digital approach for strengthening early detection, surveillance sensitivity, and public health intelligence in underserved settings. by integrating machine learning with real time community and environmental signals, the system aims to support faster decision making, improved risk anticipation, and more equitable digital health systems. further development will include prototyping, validation, and stakeholder engagement.