Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues
COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main...
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MDPI AG
2022-02-01
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Online Access: | https://www.mdpi.com/2227-9032/10/2/324 |
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author | Abrar Almalki Balakrishna Gokaraju Yaa Acquaah Anish Turlapaty |
author_facet | Abrar Almalki Balakrishna Gokaraju Yaa Acquaah Anish Turlapaty |
author_sort | Abrar Almalki |
collection | DOAJ |
description | COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R<sup>2</sup>-score = 0.44% for COVID-19 cases and R<sup>2</sup> = 60% for COVID-19 deaths. The regression model with an R<sup>2</sup>-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors. |
first_indexed | 2024-03-09T21:48:52Z |
format | Article |
id | doaj.art-576f2e22acf04c238e60dcb0fe3ee97c |
institution | Directory Open Access Journal |
issn | 2227-9032 |
language | English |
last_indexed | 2024-03-09T21:48:52Z |
publishDate | 2022-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Healthcare |
spelling | doaj.art-576f2e22acf04c238e60dcb0fe3ee97c2023-11-23T20:10:03ZengMDPI AGHealthcare2227-90322022-02-0110232410.3390/healthcare10020324Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health IssuesAbrar Almalki0Balakrishna Gokaraju1Yaa Acquaah2Anish Turlapaty3Computational Science and Engineering, North Carolina A&T University, Greensboro, NC 27411, USAComputational Science and Engineering, North Carolina A&T University, Greensboro, NC 27411, USAComputational Science and Engineering, North Carolina A&T University, Greensboro, NC 27411, USADepartment of Electronics and Communication Engineering, Indian Institute of Information Technology, Sri City 517 646, IndiaCOVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R<sup>2</sup>-score = 0.44% for COVID-19 cases and R<sup>2</sup> = 60% for COVID-19 deaths. The regression model with an R<sup>2</sup>-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.https://www.mdpi.com/2227-9032/10/2/324COVID-19GISmachine learningregressionNorth CarolinaGilford County |
spellingShingle | Abrar Almalki Balakrishna Gokaraju Yaa Acquaah Anish Turlapaty Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues Healthcare COVID-19 GIS machine learning regression North Carolina Gilford County |
title | Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues |
title_full | Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues |
title_fullStr | Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues |
title_full_unstemmed | Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues |
title_short | Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues |
title_sort | regression analysis for covid 19 infections and deaths based on food access and health issues |
topic | COVID-19 GIS machine learning regression North Carolina Gilford County |
url | https://www.mdpi.com/2227-9032/10/2/324 |
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