Collaborative Research Team Project #11

Spatial Modeling of Infectious Diseases: Environment and Health

This project explores advanced statistical modeling techniques used for mapping infectious disease outcomes.

Research Category: Health & Biology

Why Study Infectious Disease Models?

Disease clusters and spatial patterns of disease are important for informing policies, programs and interventions, on local and global scales. 

Due to complex ecological processes, space-time patterns of disease spread can span multiple scales. Biases from surveillance data, generated from multiple jurisdictions with varying sampling protocols, pose significant challenges in interpretation. 

These issues can be difficult to accommodate in quantitative frameworks, and hamper the ability to use data and models to accurately monitor disease. This limits our ability to identify vulnerable populations either spatially or over time.

Areas of Exploration

Area-Level Model (ALM) Advancement

Includes addressing measurement errors in covariates that are related to infectious disease outcomes, through the advancement of ALMs.

Mixture Modeling

Includes developing an area-level spatial model to relax the assumption of having the same distribution for all the areas of a population study. This is done by introducing a mixture model approach for infectious disease outcomes.

Multivariate Spatial Models

Includes introducing multivariate area-level models to study multiple infectious disease outcomes simultaneously.

Individual-Level Models (ILMs)

Includes extending ILMs to a new class of geo-dependent ILMs, to account for the spatial location of the individuals and the distance between susceptible and infectious individuals.

Joint Spatial Survival Models

Includes developing a joint spatial survival model, for modeling successive times to multiple events through the stages of an infectious disease.

Solving Global Challenges

Research Team’s Goal

To use advanced spatial modeling to address practical problems related to mapping infectious disease outcomes. To better integrate population health and environmental data into these models. 

Area of Impact

Health authorities depend on alerts provided by front-line clinicians or members of the public when there is an increase in disease or illness (a disease cluster). These authorities need to respond to cluster inquiries to inform the public that either no clustering exists, or to warn the public and investigate the cause.

Advanced spatial models offer a truer reflection of infectious disease dynamics and imperfect data. They can help researchers and authorities better understand disease ecology and advise population health management.

People Behind the Project

Project Team

Mahmoud Torabi, Team Leader | ​​University of Manitoba


Charmaine Dean | University of Waterloo

Mike Pickles | University of Manitoba

Cindy Feng | University of Saskatchewan

Rob Deardon | University of Calgary

Subhash Lele | University of Alberta

Rhonda Rosychuk | University of Alberta

Erin Rees | Public Health Agency of Canada and University of Montreal


Spatial Modeling of Infectious Diseases: Environment and Health is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.

CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.