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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Main Authors: Abrar Almalki, Balakrishna Gokaraju, Yaa Acquaah, Anish Turlapaty
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Healthcare
Subjects:
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.
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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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