Utilizing Socio-Economic Indicators and Artificial Neural Networks to Predict COVID-19 Spread in Canadian Health Regions

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Published Nov 29, 2024
DOI: https://doi.org/10.18103/mra.v12i11.6017

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Main Article Content

Lahiru Wickramasinghe
The University of Winnipeg, 515 Portage Avenue, Winnipeg, R3B 2E9, Canada
http://orcid.org/0000-0003-3851-5950 Aditya Jain
Indian Institute of Technology Bhubaneswar, Argul, Khordha, Odisha, 752050, India.

Abstract

This study explores how well Artificial Neural Networks (ANNs) can predict the spread of COVID-19 across Canadian health regions, focusing on the impact of socio-economic factors. By examining a wide range of demographic, economic, and social indicators, we identify which factors play the biggest role in accurately forecasting the pandemic’s spread. The trained ANN model underscores the critical role of urbanization, population density, and social behaviors in densely populated regions, such as Toronto and Montreal, where transmission rates were higher. Conversely, remote regions like the Keewatin Yatthé and Labrador-Grenfell Health Authorities saw lower transmission due to geographic isolation and community-based controls. Additionally, the study highlights disparities in healthcare infrastructure, especially in ICU bed availability, which were more pronounced in urban areas. Vaccination rates were also identified as key in controlling the spread, with proactive public health efforts leading to higher rates in regions like the Northwest Territories. Our findings show that these socio-economic factors vary in importance from one region to another, offering valuable insights for public health planning. These findings provide practical advice for improving how resources are allocated and how public health strategies are developed, emphasizing the need to consider socio-economic differences in pandemic forecasting. This approach aims to help policymakers and health officials respond more effectively to current and future public health challenges.

Keywords: SARS-Cov-2, COVID-19, Machine Learning, Artificial Neural Network, Canadian Health Regions

Article Details

How to Cite
WICKRAMASINGHE, Lahiru; JAIN, Aditya. Utilizing Socio-Economic Indicators and Artificial Neural Networks to Predict COVID-19 Spread in Canadian Health Regions. Medical Research Archives, [S.l.], v. 12, n. 11, nov. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/6017>. Date accessed: 12 dec. 2024. doi: https://doi.org/10.18103/mra.v12i11.6017.
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Issue
Vol 12 No 11 (2024): November Issue, Issue 11, VOl.12
Section
Research Articles

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