SPPH 401: Basic Epidemiology for Infection Control

SPPH 401 is designed to explore fundamental concepts in epidemiology, biostatistics for epidemiology, study design and analysis, outbreak investigation and critical appraisal as it applies to infections in health care facilities and the continuum to the community. The course is online in a problem-based learning format. 

For more information click here

SPPH 501: Analysis of Longitudinal & Correlated Data

Unlock the world of advanced data analysis with SPPH 501! Dive into methods for analyzing longitudinal and correlated data, with a special focus on statistical modeling techniques like generalized linear mixed models, Bayesian hierarchical models, and more. This course combines conceptual learning with hands-on practice in statistical computing, using R and WinBUGs. Ideal for those interested in understanding how health outcomes relate to their determinants through sophisticated modeling approaches.

By the end, you’ll be equipped to translate complex health-related concepts into statistical models and apply various analytical methods to real-world data. The course includes a flexible final project, providing the opportunity to apply what you learn to a topic that interests you.

Pre-requisites include SPPH 400 and 500 or their equivalents.

SPPH 520: Control of
Communicable Diseases

Join SPPH 520 to delve into the world of communicable diseases! This course explores the unique features of infectious disease epidemiology, outbreak investigation, and effective control measures. You’ll learn to design intervention programs, investigate outbreaks, and understand how social determinants impact disease spread.

With a focus on hands-on learning, the course features interactive workshops, assignments, and a mid-term exam. You’ll also have the chance to present on key topics related to disease control, allowing for an immersive and practical approach to learning.

Pre-requisites include introductory epidemiology and statistics courses.

SPPH 581C: Machine Learning for Health Research

Master the skills needed to analyze complex health data with SPPH 581C! This course introduces students to both traditional statistical methods and advanced machine learning techniques, such as cluster analysis, regression trees, and spline models. You’ll work hands-on with large clinical and administrative datasets using SAS, developing skills in data manipulation, cleaning, and analysis.

Ideal for students with a background in multivariate statistics and experience with programming languages like R, this course provides a practical, data-driven approach to health services research in Canada.

SPPH 303: Environmental Impacts on Health

Explore how the environment shapes human health with SPPH_V 303. From harmful exposures like air pollution and pesticides to the health benefits of urban green spaces, this course dives into both the risks and rewards our environment presents. You’ll learn about topics such as climate change, environmental justice, and how tools like epidemiology and risk assessment inform public health policies. Whether you are interested in the effects of water quality, air quality, or urban design on well-being, this course offers a broad and insightful introduction to environmental health issues.

Engage deeply with a topic of your choosing and see firsthand how the intersection of health and the environment impacts our lives both locally and globally. Ideal for students from any disciplinary background looking to understand and impact public health.

SPPH 512: The Design and Analysis of Clinical Trials

Ethical considerations, intention-to-treat versus efficacy trials, principles of sampling and exclusion, methods of allocation and techniques of randomization, parallel versus cross over design, monitoring treatment outcomes, adverse effects, stopping rules, analytic techniques and data interpretation, and logistical issues in the management of clinical trials.

Ethical considerations, intention-to-treat versus efficacy trials, principles of sampling and exclusion, methods of allocation and techniques of randomization, parallel versus cross over design, monitoring treatment outcomes, adverse effects, stopping rules, analytic techniques and data interpretation, and logistical issues in the management of clinical trials.