Towards A Global Federated System for Biosurveillance and Pandemic Early Warning Using Genomic...

Presented By: David C. Danko, PhD

Speaker Biography: Mission motivated and impact oriented Ph.D. scientist with a strong publication and business record working on infectious disease. Developed in depth knowledge of bioinformatics and machine learning with a proven ability to communicate data, apply cutting edge research to business problems, and lead a team. Dr. Danko completed his Ph.D. at Weill Cornell Medicine where he researched the impact of environmental microbiomes on human health. Prior to this, he earned his undergraduate degree in Computer Science from Massachusetts Institute of Technology, and he also did research at the Oxford Kennedy Institute of Rheumatology. Currently Dr. Danko is the CTO of Biotia, a NYC-based startup focusing on precision infectious disease diagnostics, surveillance, and prevention of hospital acquired infections powered by AI.

Webinar: Towards A Global Federated System for Biosurveillance and Pandemic Early Warning Using Genomic, Epidemiological, Climate and Environmental Data

Webinar Abstract: Our health, social, and economic systems are becoming increasingly interconnected across the globe. Though substantial benefits have emerged global interconnection has created novel biological risks. Invasive species, habitat destruction, and the spread of human infectious disease have all been consequences of globalized systems. The COVID-19 pandemic showed that these risks not only pose risk to people and the environment but can create domino effects in systems themselves. Recent studies have shown that more than half of all infectious diseases could be made worse by climate change, further exacerbating these risks.In spite of this there are few effective systems to model biological risk at a global scale. This is partly because modeling biological systems is difficult. Such systems are inherently complex and cannot be understood using only one type of data. Any framework for modeling data must necessarily incorporate data from genomics, earth systems, public health, and sociology. It must be able to incorporate local knowledge and empower experts to share their approaches and expertise.

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