This initiative will address existing knowledge and practice gaps in the Global South by establishing a multi-regional network to deepen the understanding of how responsible AI solutions can improve public health preparedness and response. It will strengthen the capacity of interdisciplinary researchers and policy makers across Africa, Asia, Latin America and the Caribbean, and the Middle East and North Africa, to support early detection, response, mitigation and control of developing infectious disease outbreaks. Projects within the initiative will work closely with governments, public health agencies, civil society and other actors to generate new knowledge and collaborations to inform practice and policies at subnational, national, regional and global levels.
The Foundation of the A4PEP Network is a combination of SDG3 (“Good Health and Well-being”) and SDG5 (“Gender Equality”). It is built around four research themes: early detection, early warning systems, early response, and mitigation and control of developing epidemics with AI being the entry point. These four areas are supported by three pillars: i) timely and reliable data for public health decision-making, ii) resilient, strong, and fair health systems and iii) inclusion and equity for vulnerable groups. One Health is the unifying approach that integrates and combines all these domains (themes and objectives to achieve), that are usually siloed.
Our adopted Framework for clinical public health needs in the Global South, contains three shells. The inner shell (“how”) contains the set of ethical and legal rules and codes that should be designed in such a way that they are responsible (incorporating policy and regulations), locally relevant for communities, and explainable to society at large. Moreover, they should be applied and embedded along all the processes of AI solutions in the Global South. The medium shell (“what”) describes the processes that should be implemented in an iterative fashion (step 1: data collection; step 2: design and development; step 3: deployment; step 4: performance; and step 5: monitoring).
In addition to advancing the responsible development and deployment of AI-based tools in One Health approaches to epidemic and pandemic prevention, preparedness, and response in Afric, it is anticipated that this network will achieve the following outcomes:
● Upgrade existing methods and develop new techniques and tools for bettering Africa clinical public health outcomes
● Devise comprehensive and complementary models to inform epidemic and pandemic prevention, preparedness, and response in LMICs
● Map drivers and key national priorities to be considered for alignment and linkages
● Inform the development of new policies and approaches to stimulate innovation and technology adoption
● Bridge gaps between clinical public healthcare policy needs and solutions, and contribute to policy and disaster relief innovation
● Establish sustainable collaborations among local AI experts, government, civil society organizations, and community leaders
● Improve identification, collection, and cataloging of relevant data required for the research projects in clinical public health and global health
● Strengthen and enhance capacity and prepare the next generation of leaders in responsible AI in clinical public health policy through a unique training program in an interactive environment mentored by basic, applied, and policy researchers
● Build trust and knowledge of emerging and re-emerging infectious diseases models among key decision makers to enable rapid-repose in emergencies through close engagement with government, public health agencies, and other stakeholders
Expected deliverable outputs include:
● Data dashboard and portals;
● An online searchable repository comprehensively compiling resources containing locally relevant data;
● Smartphone/web-based applications and other eHealth/mHealth tools, including AI-powered Digital tools to collect anonymized data, provide personalized health-related advice to patients, and follow them up to ensure adherence and compliance with treatment;
● Monthly blogs;
● Scholarly, peer-reviewed articles in impacted, open-access journals;
● Special issue in impacted journals;
● Policy reports.
How we work
We are addressing pandemic and epidemic preparedness and response by strengthening more equitable and effective public health preparedness and response to infectious disease outbreaks (epidemics and pandemics) in LMICs through Southern-led responsible AI solutions that adopt an OH approach. To this end, we fund projects from teams made of a diverse blend of research and implementation experts from across disciplines and sectors, community health practitioners and program managers, policy and decision-makers from across levels of government, and other key stakeholders from LMICs in Africa, Asia, Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA) that contribute to achieving the program's vision. Once funded, the funded grantees become members of AI4PEP. AI4PEP uses AI to strengthen public health systems throughout the disease management cycle using innovative approaches and techniques that are locally relevant and championed. We follow the virus/parasite/bacteria from its place of origin through its host-transmission dynamics, lifecycle, and evolution. The transnational partnership’s added value for all partners in our network is grounded in our sharing across contexts and specific situations regarding effective digital data generation, management, dissemination, and ways to address equity priorities for risk minimization by amplifying the voices and agency of marginalized and highly impacted communities.
We hold biweekly meetings and regular workshops. Our teams share ideas across contexts and specific situations regarding effective digital data generation, management, dissemination, and ways to address equity priorities for risk minimization by amplifying the voices and agency of marginalized and highly impacted communities.
Activities in each partner institution is led by a Node Director with a strong individual track record in undertaking interdisciplinary research in AI and at least one of the following: data sciences; disease modeling; global health; environmental science; veterinary health care; citizen science; community engagement; participatory research; policy; anthropology; social works; sex, gender intersectionality, and decolonization, etc. We factor the levels of clinical public health and AI activities at the Institutions into our capacity-building strategy and support. Node Directors work with teams of research staff to undertake and supervise joint interdisciplinary network projects. They are also involved in joint training and promotional activities of the network. Each node is guided by administrative support to develop good research governance structures within the broader institution.
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