Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues
Abrar Almalki1, Balakrishna Gokaraju1, Yaa Acquaah1
1Computational Science and Engineering, North Carolina A&T University, Greensboro, NC 27411, USA.
Healthcare (Basel, Switzerland)
|February 25, 2022
Summary
This study reveals a significant spatial correlation between food access points and COVID-19 spread. Understanding these links can help prepare for future pandemics by improving food distribution strategies.
Area of Science:
- Epidemiology
- Public Health
- Geographic Information Systems (GIS)
Background:
- The COVID-19 pandemic (SARS-CoV-2) profoundly impacted global health, economies, and societies.
- Understanding the drivers of virus spread, including socio-economic factors, is crucial for pandemic preparedness.
- Food access and distribution are identified as potential high-risk areas for virus transmission due to population density.
Purpose of the Study:
- To investigate the spatial correlation and statistical association between health, food access factors, and COVID-19 spread.
- To explore regression models for examining COVID-19 spread in relation to socio-economic and food access variables.
- To identify interrelations between socio-economic factors to enhance future pandemic preparedness.
Main Methods:
- Utilized Geographic Information Systems (GIS) for spatial analysis, mapping COVID-19 cases alongside food outlets.
- Employed clustering techniques and overlay analysis to map and analyze spatial relationships between food outlets and infected case clusters.
- Applied machine learning regression techniques to quantitatively analyze health issues and food access areas against COVID-19 infections and deaths.
Main Results:
- A correlation was found between independent variables (health, food access) and dependent variables (COVID-19 cases and deaths).
- Pearson correlation R²-scores indicated a 44% association for COVID-19 cases and 60% for COVID-19 deaths.
- A regression model achieved an R²-score of 0.60, demonstrating good fit for predicting COVID-19 deaths based on health and food access factors.
Conclusions:
- Spatial analysis highlights the link between food access points and COVID-19 transmission patterns.
- Machine learning models show significant predictive power for COVID-19 deaths concerning health and food access.
- Findings underscore the importance of integrated socio-economic and health strategies for effective pandemic response and preparedness.
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