Collaborative Research Team Project: 2018-2021
Identifying disease clusters and spatial patterns of disease (from human/animal/plant) are important to inform policy makers, programs and interventions at both local and global scales. For instance, in the context of population health, Canadian health authorities depend on alerts provided by front-line clinicians or by members of the public when there is an increase in disease or illness (disease cluster). Health authorities need to respond to cluster inquiries to inform the public that: a) no clustering exists; or b) to warn the public and investigate the cause of the cluster. The recent emergence of the Zika virus as a global pandemic is one example of a critical public health threat that challenged management systems. The rapid spread of Zika across much of the Americas is not well understood. Space-time patterns of spread span multiple scales due to complex disease ecological processes and biases from surveillance data generated from multi-jurisdictions with varying sampling protocols are real challenges. These issues, which are also common to high priority diseases in Canada (e.g., Lyme disease), can be difficult to accommodate in quantitative frameworks, and hamper the ability to use data and modeling products to accurately monitor disease and identify vulnerable populations either spatially or over space-time. This project will spearhead innovation in disease modeling by addressing several practical problems related to infectious diseases in environment and health by advancing statistical modeling techniques. The team’s research goal is to better integrate population and environmental data for infectious diseases using spatial modeling techniques. In particular, their study aims are to: 1) advance a spatial area-level statistical model (ALM) to address measurement errors in covariates that are related to an infectious disease outcome; 2) develop an area-level spatial model to relax the assumption of having the same distribution for all the areas of the population study by introducing a mixture model approach for an infectious disease outcome; 3) simultaneously study multiple infectious disease outcomes (e.g., influenza and meningococcal disease) by introducing multivariate area-level spatial models; 4) extend individual-level statistical models (ILMs) to a new class of geo-dependent ILMs to also account for the spatial location of the individuals in addition to the distance between susceptible and infectious individuals; and 5) develop a joint spatial survival model for modeling successive times to multiple events through the stages (susceptible, infectious, removed) of an infectious disease. Using their proposed models, which offer a better reflection of the true infectious disease dynamics and imperfect data, researchers and authorities will have improved models for understanding disease ecology and advising population health management to ultimately improve the health of Canadians.
The team leader is Mahmoud Torabi (University of Manitoba). Collaborators include Charmaine Dean (University of Waterloo), Mike Pickles (University of Manitoba), Cindy Feng (University of Saskatchewan), Rob Deardon (University of Calgary), Subhash Lele and Rhonda Rosychuk (University of Alberta), and Erin Rees (Public Health Agency of Canada and University of Montreal).