Closing the Gap: Artificial Intelligence Integration for Advancing Chikungunya Virus Studies in Africa

Abstract

This study addresses critical research gaps in Chikungunya virus (CHIKV) studies in Africa, proposing an AI-integrated approach. The study aims to leverage AI to enhance epidemiological surveillance, vector control, clinical management, community engagement, drug discovery, and data integration, within a One Health framework. The research gaps encompass inadequate real-time surveillance, limited vector knowledge, diagnostic challenges, low community awareness, slow drug development, and fragmented data. The study underscores AI's potential in early outbreak detection through data analysis and predictive modeling. It highlights AI's role in enhancing vector surveillance via image recognition and machine learning. AI-assisted diagnostics aid in accurate case identification. Moreover, AI-driven communication strategies can elevate community engagement. AI expedites drug discovery and vaccine development by predicting potential compounds. Data integration facilitated by AI harmonizes diverse datasets, encouraging interdisciplinary collaboration. The study advocates a One Health approach, recognizing the interdependence of human, animal, and environmental health. The study's insights provide a comprehensive roadmap to address CHIKV research gaps through AI, ultimately advancing public health outcomes in Africa.

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