AI-driven strategies for enhancing Mpox surveillance and response in Africa

Abstract

Mpox, a zoonotic viral disease endemic to several African countries, has re-emerged as a significant public health concern, particularly in regions with limited healthcare resources. Current public health strategies in Africa fall short due to fragmented surveillance systems, delayed diagnostic capabilities, and inadequate resource distribution networks that cannot effectively respond to rapidly evolving outbreaks in remote and underserved areas. This narrative review explores the potential of Artificial Intelligence (AI) to enhance the management and control of Mpox in Africa. AI technologies, including machine learning and predictive analytics, can significantly improve early detection, surveillance, contact tracing, case management, public health communication, and resource allocation. AI-driven tools can analyze large datasets to identify outbreak patterns, automate contact tracing through mobile data, optimize treatment plans, and tailor public health messages to specific communities. However, the successful implementation of AI faces challenges, including limited digital infrastructure, data quality issues, ethical concerns, and the need for capacity building. Furthermore, ongoing research is essential to refine AI algorithms and develop culturally sensitive applications. This review emphasizes the need for investment in infrastructure, training, and ethical frameworks to fully integrate AI into public health systems in Africa. By addressing these challenges, AI can play a pivotal role in mitigating the impact of Mpox and enhancing the resilience of healthcare systems against future infectious disease outbreaks. This represents a novel comprehensive synthesis of AI applications specifically for African Mpox control, providing a critical framework for evidence-based implementation strategies in resource-limited settings.