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Jude Kong

York University

Jude Kong is an assistant professor at York University and the founding director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). He is also the leader of the One Health Modelling Network for Emerging Infections (OMNI) Early Warning Signals Network and a member of the Canadian Black Scientist Network, the Scientific Advisory Committee of the Mathematics for Public Health Network, the Canadian Centre for Disease Modelling, and the Canadian COVID-19 Modelling Rapid Response Task Force. He is an expert in data science, artificial intelligence, and infectious disease modelling. His principal research interest is the impacts of climate and environmental changes on disease transmission and identifying populations at risk. 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 helping others learn mathematical concepts and encouraging them to find their passion and achieve more than they thought was possible.

How Mathematics Can Save Lives: Mathematical Modeling to Support Infectious Disease-based Decision-making

Monday, June 5 2023 | 11am - 12pm

As posited by Galileo, an Italian astronomer, physicist, and polymath, the “book of nature” is written in the language of mathematics. Rather than being abstract, this language represents the foundation of our world, in terms of time and space dimensions as well as of uncertainties. Being generally perceived as a niche discipline, mathematical modeling has become extremely popular during the COVID-19 pandemic, being brought to the forefront of lay public attention and debate. Words such as ‘flattening the curve’ and ‘reproduction number’ have become a common part of the collective lexicon. In the era of evidence-based decision-making and evidence-based medicine, mathematical models are now considered as valuable and insightful tools as epidemiological surveys and randomized controlled clinical trials. Governmental institutions and public health authorities all over the world are relying more and more on mathematics, not only to forecast the epidemic in terms of trends and projections, but also to understand societal issues, like vaccine hesitancy and behavioral adherence to recommendations and mandates. Never as in this period, mathematicians and mathematical models are playing a key role in real-time delivery of reliable and comprehensive information to predict the spread of COVID-19 and its impact, and in guiding governmental policies and best practice. However, despite this increasing popularity, mathematical modeling still appears to be more an art rather than a science, with results sometimes highly conflicting, which are hard to reconcile. So, HOW do we design a mathematical model of an infectious disease outbreak? HOW can models be harnessed to inform public health measures at different stages of an outbreak? In this talk, I will try to provide answers to these questions.

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