Abstract: This talk focuses on how using better statistical methods and different perspectives for analyses can help gain insight into biomedical and public health data. The first part of the talk focuses on ongoing work regarding COVID-19 vaccination in the province of Ontario, and how there are intra-provincial differences that deserve further exploration. The second part of the talk focuses on the use of generalized additive models (GAMs) to analyze longitudinal biomedical data in order to gain insight into metabolic and molecular changes that might be significant, and that are ignored in traditional regression models.

Bio: Ariel Mundo Ortiz received his PhD in Biomedical Engineering from the University of Arkansas in 2022, and is currently a postdoctoral fellow at l’Universite de Montreal, co-sponsored by the Centre de recherches mathématiques and the Fields Institute under the MfPH initiative. His work lies at the intersection of infectious diseases, longitudinal data, and reproducible research. He has authored and co-authored papers within the fields of biomedical research (cancer biology, optics) and public health (mpox, and upcoming work on COVID-19).