This community innovation has been accepted at the 2026 DHIS2 Annual Conference as a digital poster.
AI-Driven Predictive Epidemiology for Malaria
Malaria remains a major public health problem in the Comoros, requiring more effective surveillance tools to anticipate outbreaks and optimize health interventions. This study proposes a predictive epidemiology approach based on artificial intelligence for monitoring and forecasting malaria using data from DHIS2. Routine health data, including confirmed cases and demographic and temporal indicators, are used to develop machine learning models capable of capturing the spatio-temporal dynamics of the disease. Several deep learning and machine learning algorithms are evaluated to estimate the future incidence of malaria at different geographical scales. Model performance is compared using standard statistical metrics, highlighting the ability of AI-based approaches to improve early detection of epidemic trends. The results demonstrate the potential of intelligent systems integrated into DHIS2 to strengthen decision-making, support malaria control strategies, and contribute to proactive and sustainable public health management in the Comoros.
Authors: Mohamed-Saiaf ALIAMINI, Nassur HASSANE, Zoulfika ALHADHUR and Dhoulhedji ABDOU
Keywords:
Malaria, AI-Driven, DHIS2, Comoros
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