Grant: Alliance Grants

The recent emergence of COVID-19 as a global pandemic is one example of a critical public health threat that challenged management systems. The rapid spread of COVID-19 across much of the globe is not well understood yet. Patterns of spread span multiple scales due to complex disease etiological 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., COVID-19), can be difficult to accommodate in quantitative frameworks, and hamper the ability to use data and modeling products to accurately monitor the virus and identify vulnerable populations. We will spearhead innovation in disease modeling by addressing several practical problems related to the COVID-19 pandemic by advancing mathematical and statistical modeling techniques. Our research goal is to better understand the spread of the coronavirus in Canada using sophisticated modeling approaches to be able to predict the needs of the Canadian healthcare system and plan for interventions. In particular, our study goals are to 1) accurately predict the spread of coronavirus at the provincial and national level to improve our understanding of its behavior at the population level; 2) identify vulnerable populations who are most at risk due to COVID-19; 3) study the geographical variation of infected people at the provincial and national level to better understand viral persistence in the environment. By addressing the objectives proposed in this research proposal, we will provide new statistical techniques that solve prevalent problems due to the COVID-19 pandemic. An immediate outcome of this research proposal is helping our partner organizations (PHAC and Manitoba Health) to implement novel modeling products that will improve current technologies that are used to inform population health. Using our proposed models, which offer a better reflection of the true infectious disease dynamics and imperfect data, policymakers at the provincial and national levels will have improved models for understanding disease etiology and advising population health management.