Introduction:
In an increasingly interconnected world, the need for effective pandemic and epidemic preparedness and response has become paramount. AI4PEP, an innovative initiative, aims to bridge existing knowledge gaps and leverage responsible AI solutions to enhance public health preparedness and response in the Global South. By establishing a multi-regional network and promoting collaboration among diverse stakeholders, AI4PEP seeks to deepen our understanding of how AI can contribute to early detection, response, mitigation, and control of infectious disease outbreaks. In this article, we will delve into the framework, expected outcomes, and collaborative approach of AI4PEP, shedding light on its potential to revolutionize clinical public health in LMICs.
Addressing Knowledge Gaps through Multi-Regional Collaboration:
AI4PEP addresses the existing knowledge and practice gaps in the Global South by establishing a multi-regional network. This network brings together interdisciplinary researchers, policymakers, and experts across Africa, Asia, Latin America and the Caribbean, and the Middle East and North Africa. By fostering collaboration and knowledge exchange, AI4PEP aims to enhance the capacity of these regions to support early detection, response, and control of infectious disease outbreaks.
The Foundation of AI4PEP: Integrating SDGs and One Health:
The AI4PEP network is built upon the foundation of two Sustainable Development Goals (SDGs): SDG3 (Good Health and Well-being) and SDG5 (Gender Equality). It centers around four key research themes: early detection, early warning systems, early response, and mitigation and control of developing epidemics. These themes are supported by three pillars: timely and reliable data for public health decision-making, resilient and fair health systems, and inclusion and equity for vulnerable groups. The unifying approach of One Health integrates and combines these domains, breaking down silos and fostering collaboration for a comprehensive approach to disease control.
The AI4PEP Framework for Responsible AI Solutions:
AI4PEP adopts a framework that encompasses three shells to guide responsible AI solutions in the Global South. The inner shell focuses on ethical and legal rules, incorporating policy and regulations that are locally relevant, responsible, and explainable to society. The medium shell outlines iterative processes, including data collection, design and development, deployment, performance, and monitoring. By following this framework, AI4PEP ensures responsible and accountable use of AI solutions throughout the disease management cycle.
Expected Outcomes and Deliverables:
AI4PEP aims to achieve a range of impactful outcomes, including the upgrade of existing methods and the development of new techniques for better clinical public health outcomes. It strives to inform epidemic and pandemic prevention, preparedness, and response in LMICs, and to bridge gaps between policy needs and solutions. Through sustainable collaborations, AI4PEP seeks to strengthen local expertise, improve data collection, enhance capacity, and establish trust among key decision-makers and stakeholders. The initiative also plans to deliver various outputs, such as data dashboards, online repositories, applications, scholarly articles, and policy reports.
Collaborative Approach and Biweekly Meetings:
AI4PEP operates through a collaborative approach that brings together diverse expertise and perspectives. Biweekly meetings and regular workshops provide a platform for teams to share ideas, insights, and best practices. By amplifying the voices and agency of marginalized and highly impacted communities, AI4PEP ensures equity priorities are addressed, and risk minimization strategies are inclusive and effective.
Conclusion:
AI4PEP represents a groundbreaking initiative that harnesses the power of responsible AI solutions to advance global health, particularly in LMICs. By establishing a multi-regional network, promoting collaboration, and embracing the principles of One Health, AI4PEP aims to enhance disease surveillance, early detection, and response.