Data science approaches to advance equitable population health strategies

Speaker: Hiroshi Mamiya, McGill University

Location and Time: July 11th 1:30 at SCIE 1504 or Zoom Link below

Data science approaches to advance equitable population health strategies

Widening inequalities of health across urban areas and socio-economic and racial status call for the transformation of healthcare and public health strategies. Increasingly digitalized and inter-linked health, social, and environmental data, and the advancement of machine learning and statistical modeling adapted to geospatial data provide opportunities to unmask these inequalities. In this talk, I will demonstrate the combination of data science approaches and epidemiologic principles to achieve such equitable population health strategies, with a focus on my research work advancing the public health surveillance of healthy eating, food insecurity, and chronic disease burdens across neighborhoods in Montreal. I will also examine ethical issues relevant to the applications of big data to population research and policy making.

Topic: Teaching (9:30) and Research (1:30) Talks - Hiroshi Mamiya

Time: This is a recurring meeting Meet anytime

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