Date of Award
5-28-2024
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Machine Learning;Time Series Analysis
Supervisor
Mohammad Hassanzadeh
Supervisor
Majid Ahmadi
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This research examines the impact of COVID-19 policies on death rates across Canadian provinces, considering geographical, cultural, economic, and healthcare disparities. It analyzes the effectiveness of these policies over time, hypothesizing that their impact varies and that only certain measures are effective. The study uses both traditional methods, like Vector Autoregression (VAR) and Granger Causality, and modern techniques, like eXtreme Gradient Boosting (XG- Boost), to assess policy effectiveness. This approach goes beyond binary evaluations by quantifying the strength of policy impacts. By comparing these methods, the research identifies the most effective strategies for evidence-based decision-making. Focusing on provincial-level data, the study aims to provide insights that are crucial for immediate policy decisions and future pandemic preparedness, contributing to a comprehensive understanding of effective pandemic response strategies and enhancing Canada’s resilience to health crises.
Recommended Citation
Khosravi, Sara, "Analysis on Covid-19 Public Restrictions Using Granger Causality and Machine Learning" (2024). Electronic Theses and Dissertations. 9494.
https://scholar.uwindsor.ca/etd/9494