York University a leading Canadian research institution, brings its extensive academic expertise and commitment to innovation to the AI4GH project.

With over 300 undergraduate and graduate programs spanning diverse disciplines, including arts and humanities, social sciences, natural sciences, engineering, and health sciences, York is well-positioned to make significant contributions to the initiative's goal of improving global public health preparedness and response through responsible AI solutions.

Through its dedication to academic excellence, social justice, and community engagement, York is playing a crucial role in shaping the future of Canada and the world.

Official website: https://www.yorku.ca

AI4PEP

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.

Specific objectives are to:
1. Deepen the understanding of how to use responsible AI solutions to improve public health preparedness and response to emerging and re-emerging infectious disease outbreaks.
2. Establish and support a multi-regional network across the Global South to address gaps in knowledge, capacities, and solutions.
3. Inform national, regional, and global policies and practices on the use of AI to improve health equity and strengthen public health systems.

Official Website: https://ai4pep.org 

Resources and events

The main objective is to develop an integrated and user-friendly platform called AutoAI-Pandemics, democratizing access to data science and machine learning techniques by allowing non-experts to use them well, e.g., biologists, physicians, and epidemiologists. AutoAI-Pandemics seeks to provide the following solutions: 1. Automated epidemiologic analysis to detect possible epidemic scenarios and corresponding optimal intervention policies 2. Automated bioinformatics analysis, e.g., drug discovery or pathogen genome mining 3. Fighting misinformation/disinformation to assist in the search for reliable sources.

Artificial Intelligence and Eco-Epidemiology-Based Early Warning Systems for the Improvement of Public Health Response to Aedes-Borne Viruses in the Dominican Republic

The goal is to design a predictive model of Aedes-borne viruses in the Dominican Republic that provides early warnings of outbreaks and streamlines Public Health responses

Household screening for contagious and transmissible respiratory infections using artificial intelligence-based cough monitor

The general objective is to evaluate the use of an AI-based cough tool for respiratory infectious disease screening and monitoring to improve prevention and preparedness for outbreaks in the Peruvian health system

Controlling re-emerging and emerging infectious diseases using a digital one-¬¬health approach in Cameroon

The main objective is to provide mobile and web-based AI solutions to help strengthen Cameroon’s public health system to improve prevention, preparedness, and response to emerging and re-emerging infectious disease outbreaks

Polio antenna: Responsible AI for improving polio pandemic surveillance sensitivity

The goal is to improve polio surveillance sensitivity through responsible AI in a decolonized approach using local capacity and local data focusing on empowering underserved groups.

Responsible AI for developing a Robust public health surveillance system: Early Detection and Prediction of Vector-borne Viral Zoonotic Pathogens

The main objective is to develop bio-acoustic sensors to conduct vector monitoring at unprecedented scale and low cost, replacing the current practice of light traps and manual counting with automated solutions based on responsible AI and Edge Computing. To identify novel viruses in vectors, and animal reservoirs with the potential to replicate in humans via NGS metagenomic sequencing. Thus, ensuring readiness for outbreaks. To develop climate and environmentally-driven, dynamical host–vector models to predict the risk of viral outbreaks in current and future climates. To monitor Health Inequalities to characterize current realities of infectious disease pandemics on women and children within vulnerable communities to recommend.

AI and Hybrid modeling for Community-based early detection of zoonotic disease in the context of climate change in Senegal

The general objective of this project is to enhance the epidemiological surveillance system in Senegal by developing and testing a community-based, gender-sensitive early detection and warning systems for zoonotic diseases using AI, data science and a One Health approach.

AI-powered early detection system for communicable respiratory diseases based on integrated data sets

Development of a cost-effective AI-powered IoT system for air-pollutants.With integration data from air pollutants, epidemiology, clinical, atmospheric and satellite data for the development of AI-powered early detection algorithms for pandemic preparedness and mitigation of infectious diseases

Wastewater-based Surveillance for Antimicrobial Resistance (AMR) for Early Warning and Engendering Stakeholder Response Through Artificial Intelligence (AI)

The main goal is to address the feasibility and value of wastewater based surveillance for antimicrobial resistance towards a warning and AMR mitigation approach.

Intelligent Early Warning and Response System Based on Health System Routin Data and Environment Data to Improve National Health Resilience.

The goal is to develop artificial intelligence model to predict spread of infectious disease outbreak by combining health system routine data and environment data.

Blockchain-Enabled AI Architecture for Trustworthy Digital Health .

To leverage advanced technologies such as AI, Blockchain, and IoT to provide a secure, accurate, and privacy-preserving solution enabling better pandemic and epidemic preparedness and response

Telehealth data, predictions, pandemic prevention, and preparation (TDP4): early resources mobilization and long-term mental health response in highly vulnerable Indigenous communities.

The long-term objective is development and implementation of a sustained sentinel epidemic-surveillance and resource-planning system for underserved and marginalized Indigenous communities of Western Visayas (Region 6, Philippines) using telehealth and community-inspired health innovations. Within this proposal, the chronic lack of access to healthcare among Indigenous minorities is also addressed as a prelude to universal healthcare. The ultimate goal of this system is to manage and greatly reduce the impacts of public health emergencies and their long-term mental health repercussions which are greatly compounded by the lack of access to healthcare in these communities. Improved performance goals include anticipation of crises to enable roll-out of the appropriate co-created health, social and economic programs for highly vulnerable Indigenous populations.

Strengthening Lebanon's pandemic surveillance system through AI-driven automation of laboratory data.

The project’s objective is to improve the performance of the current pandemic surveillance system in Lebanon on multiple levels: 1. Systematize the detection of infectious pathogens (and pathologies) subject to national or regional surveillance, through extracting data directly from the healthcare institutions information systems and laboratory information systems. 2. Optimize the identification of significant trends in data that prompt expert attention through automated rules and alerts validated by experts. 3. Improve the consolidation of information in one central real-time dashboard for visualization of important epidemiological surveillance data and trends, for better decision-aid. After a pilot in one university healthcare institution in the inception phase where a prototype of the system will be elaborated, tested and compared to the existing system, a roadmap will be developed for implementation on a national scale in collaboration with the MoPH

Applications of AI for early diagnosis of tuberculosis and prediction of drug resistant Mycobacterium tuberculosis strains

The primary objective of this research is to leverage the potential of AI techniques to improve the early detection, prediction of drug resistance, and monitoring of MTB strains circulating in countries like Morocco, South Africa, Burkina Faso, and the Democratic Republic of Congo.

A Responsible Artificial Intelligence-driven Initiative for Tackling Waterborne Pathogen (re)Emergence in Tunisia (INTERACT)

Find ways to transform an academic research project into a toolkit for epidemic response: Lay the foundation for a functional system that incorporating AI models along with wastewater-based epidemiology—surveillance data into early detection and warning systems of waterborne outbreaks in Tunisia

AI-powered mHealth system for infectious disease early detection and early warning systems (AIMED)

This aims to investigate the potential of Mobile cloud AI and Big Data Analytics technology in improving early detection and warning systems for emerging and re-emerging infectious diseases. 2. To investigate the potential of Mobile cloud AI and Big Data Analytics technology in improving early response and the mitigation of the effect of infectious diseases. 3. To generate models that simulate the spread of infectious diseases which can be utilized to help in the containment of these diseases. Phase 2: 4- To develop and validate a mobile cloud system that utilizes AI and Big Data Analytics to enable early response of emerging and re-emerging infectious diseases. 5. To investigate the potential of AI-powered mobile cloud technology and Big Data Analytics in controlling and mitigating the effects of infectious diseases among at-risk populations.

Prof. Jude Kong

Professor in the Mathematics & Statistics Department at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). Additionally, he is the Executive Director of the Global South Artificial Intelligence for Pandemic and Epidermic Preparedness and Response Network and the Regional Node Liaison to the steering committee of the Canadian Black Scientist Network (CBSN). He obtained his Ph.D. in Mathematics from the University of Alberta, his MSc. in Engineering mathematics from the University of Hamburg-Germany and the University of L’Aquila-Italy. His B.Sc. in Computer Science and Mathematics was acquired at the University of Buea-Cameroon and his Bachelor of Education (B.Ed) degree in Mathematics was earned at the University of Yaounde-Cameroon. Before joining York University, he did a postdoc at Princeton University. Dr. Kong is an expert in artificial intelligence, data science, mathematical modelling, infectious disease modelling and mathematics education. His principal research program focuses on the use of artificial intelligence, data science, mathematical models and other quantitative methods to improve decision-making for clinical public health. During the COVID-19 pandemic, he has been leading an interdisciplinary team of more than 52 researchers from key academic and government institutions in nine African countries that have been using Artificial intelligence to help government and local communities to contain and manage the spread of COVID-19. In 2020, he won a York Research Leader Award. In 2021 he was spotlighted among Canadian Innovation Research Leaders 2021 for his work with ACADIC. In 2022, he was spotlighted as a Change Maker by People of YU for his work in helping others learn mathematical concepts and encouraging them to find their passion and achieve more than they thought was possible. He is an Area Editor of the Data & Policy Journal where he focuses on Data Technologies and Analytics for Policy and Governance.

Dr. Nicola Luigi Bragazzi

He got his Medical Degree (MD) in general medicine and surgery from Genoa University (Genoa, Italy) in 2011, his doctorate (PhD) in biophysics from Philipps University of Marburg (Marburg, Germany) in 2014, and his specialization in Public Health from Genoa University (Genoa, Italy) in 2017. He is a member of the Cochrane Association (Cochrane Reviewer) for the Cochrane Epilepsy Group. He is also a member of the Global Burden of Disease (GBD) Initiative (Institute for Health Metrics and Evaluation, IHME, University of Washington, USA), which aims at quantitatively measuring the burden of disease generated by 369 diseases and injuries and 87 risk factors from 1990 onwards in 204 countries and territories. He has been awarded the “Young Knight of the Italian Republic” (“Alfiere della Repubblica Italiana”) by President Carlo Azeglio Ciampi in 2005. Recently, in 2019, he has been nominated as one of the top five biomedical researchers worldwide aged less than 40 years in terms of the number of publications, articles in Q1 biomedical journals, total impact factor, and h-index (USERN Prize). He also received other prizes, including the Guidoniani Prize in 2018, for his contributions to evidence-based medicine, the MAI Prize 2020 for his contributions toward a better understanding of the epidemiological basis of autoimmunity and autoinflammation, and the 2020 HSE Open Access Research Award for his contributions to evidence-based medicine and the fight against COVID-19. According to a scholarly study by Professor Ioannidis, Stanford University, USA, he has highly contributed to COVID-19 research, being among the most productive researchers from across the world and the only one from Canada in the list of highly cited and prolific researchers on COVID-19. According to a list of researchers compiled by Professor Ioannidis, he belongs to the 2% of top scientists worldwide. The list can be accessed here: https://data.mendeley.com/datasets/btchxktzyw/1. He is working on advanced mathematical modeling (infectious disease and vaccination modeling) and big data mining and artificial intelligence in biomedicine at York University, Toronto, ON, Canada. He is passionate about equity, diversity, and inclusion (EDI), and he is utilizing big data and artificial intelligence to empower underserved, marginalized communities (the 2SLGBTQIAP+ community, of which he is an out-and-proud member, people with disabilities, and in particular disabled athletes and para-athletes, visible racialized communities, and Aboriginals). Specifically concerning digital health, informatics, analytics, and machine learning, he has been a pioneer in the field of “infodemiology and infoveillance” and in the use of the so-called nonconventional or novel data streams, such as the number of accesses to Wikipedia pages, and web searches/web queries. He has published his research in the prestigious “Journal of Medical Internet Research” (JMIR), the number one and leading journal in the field of digital health. He has also published in other relevant journals, like “Health Informatics Journal”, “JMIR mHealth and uHealth”, “JMIR Formative Research”, “JMIR Public Health and Surveillance”, and “JMIR Mental Health”, among others. Of note, he has been deploying artificial intelligence for exploring topics of great relevance in the field of social justice, and EDI. Finally, he has been working with Statistics Canada, analyzing data relevant to the Canadian healthcare system and informing local policies.

Jean-Jacques Rousseau

Philosopher of science, with a practice in inclusive innovation at the intersection of tech, entrepreneurship and big ideas. A member of the Task Force on the Future of Pedagogy at York University, he advises the Office of the Dean at the Schulich School of Business in Decolonization, Equity, Diversity and Inclusion (DEDI). As Managing Director at Rousseau Ventures, he helps clients Innovate for Impact ™. He spent 10 years in the Ontario Public Service, starting as an Economist in the Ontario Ministry of Finance, and ending as Investment Attraction Lead and Senior Manager of Life Sciences Programs at the Ontario Ministry of Research and Innovation. After four years of varied client work, he was recruited for the role of Inaugural Technical Advisor in Innovation, Science and Competitiveness to the President of the Republic of Haïti. Today, Rousseau Ventures has a client list that includes postsecondary institutions, a cultural centre, entrepreneur incubator spaces, and tech startups. He holds a B.A. in Law and Philosophy from Carleton University, MBA from the Schulich School of Business, and PhD in Philosophy of Science from the Institute for the History and Philosophy of Science and Technology at the University of Toronto. He completed a postdoctoral fellowship in Explainability & Trust in AI Systems at the Lassonde School of Engineering, an intensive in Philosophy of Physics at the University of Geneva, and the EinsteinPlus workshop in modern physics at the Perimeter Institute for Theoretical Physics. He also completed a rigorous introduction to Machine Learning at the Vector Institute for Artificial Intelligence. Jean-Jacques regularly speaks and teaches on inclusive innovation, public policy and governance, digital and scientific literacy, and on Haiti as the light of the Enlightenment. He is an Instructor at the Schulich School of Business, Network Manager at the AI for Pandemic and Epidemic Preparedness (AI4PEP) project, Research Fellow at Dahdaleh Institute for Global Health Research, and Past Chair of CARE for Internationally Educated Nurses. He is a former Board Member at Groupe Média TFO and the World Summit AI Board of International Government and UN Advisors, Mentor at NextAI (Montréal) and former Chair of the Board of the Obsidian Theatre Company.

Ms. Liswa Luhlanga

Graduated from York University with a Master of Political Science and a post Graduate Diploma in Democratic Administration. She holds a double major Honours Degree in Communications Studies and Human Rights and Equity Studies. Previously, she earned a Diploma in Law Studies at the University of Swaziland. She is also the Founder and inagural National Coordinator of the Swaziland Young Women’s Network (SYWON) a feminist organization committed to building leadership skills and advocating for Sexual Reproductive Health Rights of Young Women. She also has a proven history of feminist organizing, lobbying and advocacy at national and regional level. During the period 0f 2012 to 2014, she served the Swazi civil society as the Chairperson of the Gender Consortium at national level. Her highlights as a CSO leader was championing the first ever Swaziland Alternative Shadow report to the UN-CEDAW Committee in Geneva.