Meet postdoctoral fellow, Dr. Amin Abed
Dr. Amin Abed is a postdoctoral fellow working with Dr. Cindy Feng in the Department of Community Health and Epidemiology in Dalhousie Faculty of Medicine. With a strong foundation in statistical theory and a deep commitment to applied public health research, his work bridges advanced biostatistical methodology with real-world health challenges. Trained across multiple institutions in Iran and Canada, Dr. Abed brings extensive expertise in infectious disease modeling, spatiotemporal analysis, disease mapping, and disease cluster detection, with a growing focus on applying these tools to non-communicable diseases such as cancer.
Dr. Abed’s academic journey began with a B.Sc. in Applied Statistics from the University of Kashan, followed by an M.Sc. in Mathematical Statistics at the University of Isfahan, where his thesis focused on bootstrap variance estimators for complex survey designs. He later completed a second M.Sc. in Statistics at the University of Sherbrooke, concentrating on Bayesian inference under uncertainty, and earned a micro diploma in mathematical science communication. He went on to complete a Ph.D. in Community Health Sciences (Biostatistics) at the University of Manitoba, where his doctoral research advanced individual-level spatiotemporal models of infectious disease transmission, including work on tuberculosis, influenza, gonorrhea and COVID‑19.
Through his research, teaching, and software development—including the creation of multiple R packages—Dr. Abed aims to support reproducible, data-driven public health decision-making and to contribute evidence that can guide targeted, equitable interventions.
Q: Can you describe your academic journey and what led you to community health and epidemiology?
My academic journey has been shaped by an interest in combining statistical theory with real-world public health applications. Starting with applied and mathematical statistics, I became increasingly interested in how advanced statistical models could be used to address pressing public health challenges. This led me to pursue doctoral training in community health sciences, where I focused on infectious disease modeling and developed tools to better understand how diseases spread across space and time.
Q: How has your educational background shaped your approach to research?
My training has given me a strong methodological foundation while keeping my work grounded in practical application. I aim to develop models that are statistically rigorous but also directly useful for public health practice. This approach now guides my work as I expand from communicable disease modeling into non-communicable diseases, including cancer epidemiology.
Q: Can you share an example of research that addressed a community-identified need or structural determinant of health?
During my Ph.D., I focused on advancing spatiotemporal models of infectious disease transmission and applying these new methods to high-resolution, individual-level data from Manitoba. By integrating data across health authorities and geographic levels, I was able to examine how factors such as population density and socioeconomic disparities influenced disease risk. The findings helped identify priority communities, time periods, and regions for interventions like vaccination, screening, and outreach programs.
Q: Have you received any awards or recognitions, and what do they mean to you?
I’ve been fortunate to receive several recognitions, including the Roos Prize for Best Publication in Population Health and the Evelyn Shapiro Award for Health Services Research from the Manitoba Centre for Health Policy, as well as the University of Manitoba Graduate Fellowship. These awards are meaningful because they reflect the relevance and impact of my research and motivate me to continue working on data-driven solutions to public health challenges.
Q: Which publications best represent your contribution to the field?
Two publications stand out for me. The first, “Individual-Level Modeling of Infectious Disease Transmission with Reinfection Dynamics: Tuberculosis in Manitoba, Canada,” published in Spatial and Spatio-temporal Epidemiology, focuses on individual-level modeling of tuberculosis transmission with reinfection dynamics. The second, “Spatial Individual-Level Models for Transmission Dynamics of Seasonal Infectious Diseases,” published in Statistics in Medicine, extends these methods to seasonal infectious diseases such as influenza. Together, they highlight my work on developing spatiotemporal models that can identify high-risk populations, periods, and areas, and support targeted disease prevention strategies.
Q: What are your long-term research goals?
My long-term goal is to continue advancing statistical modeling methods that improve our understanding of disease dynamics and translate those innovations into practical public health tools. I hope to pursue a career in academia, contribute to teaching and mentorship, and foster interdisciplinary collaborations that support evidence-based public health and address structural determinants of health.
Q: How do you see your work influencing public health policy or practice in the Maritimes and beyond?
My research aims to provide tools that help identify high-risk communities and forecast outbreaks, as well as evaluate the potential impact of interventions. In the Maritimes and elsewhere, this work can support public health authorities in making informed decisions, allocating resources effectively, and designing strategies that reduce health disparities.