DLSPH hosts Society for Epidemiologic Research (SER) Mid-Year Meeting
April 4/2024
On March 8th, DLSPH faculty Dr. Laura Rosella and Dr. Hailey Banack co-chaired the Society for Epidemiologic Research (SER) 2024 Mid-Year Meeting in Toronto. The mission of SER is to keep epidemiologists at the forefront of scientific developments. The theme of the mid-year meeting was Innovative Data Science Applications inEpidemiology, attracting 150 attendees and over 60 virtual participants. The event was also co-sponsored by McGill University Epidemiology and Biostatistics and the Data Sciences Institute.
SER Mid-Year Meeting Co-Chairs, Professors Laura Rosella and Hailey Banack
It was an exciting program with two expert international keynote speakers, thought provoking panel discussions and research talks about applying new data and novel methods. Professor Magdalena Cerda from the Department of Population Health at NYU Grossman School of Medicine discussed emerging data science methods in precision public health to inform the response to the US opioid crisis and leveraging large language models to automate the coding of municipal laws. Dr. Irene Y. Chen from UC Berkeley and UCSF’s Computational Precision Health Program discussed AI and health disparities and outlined critical considerations to reduce bias for ethical AI in medicine. A panel of Canadian experts including DLSPH’s Dean, Dr. Steini Brown, and faculty members, Dr. Zahra Shakeri, and Dr. Joe Kim, covered the tensions of advanced data science across policy, academic and clinical domains. They also discussed data science at the intersection of public health and academia, noting that the rapid availability of big data renews the focus on data governance with an increasing need for community engagement and transparency on how data are used.
Debates and discussions were a highlight, starting with the DLSPH panel. The meeting highlighted the latest advances in data science related to epidemiology. Research talks included using data from wearables, social media, sound data, and synthetic data for epidemiological insights. Artificial intelligence (AI) and machine learning applications were discussed in the context of public health surveillance, cancer AI, predictive epidemiology, and target trial emulation in nutrition. The final session with a stellar panel with Dr. Arjumand Siddiqi, Dr. Lisa Lix and Dr. Jay Kaufman and included audience polling to unpack the emerging ole of advanced data science and AI in modern epidemiology. The consensus? It’s complicated! AI is rapidly evolving, providing a range of opportunities and challenges for the field of epidemiology.
SER members can login to listen to the recordings here.
Session moderator Professor Robert Platt, with panelists Dean Steini Brown, and Professors Zahra Shaker and Joseph Kim