Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues

Abstract

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 R2-score = 0.44% for COVID-19 cases and R2 = 60% for COVID-19 deaths. The regression model with an R2-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.

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

“…Another perspective would consist in introducing, thanks to Usher’s model [ 41 , 42 ], an accelerated aging due to infection, as well as an influence of exogenous determinants such as geo-climatic, socio-economic, and health-related factors [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ], which weight differently on the different age groups, therefore, changing the growth dynamics specific to each of the sub-populations studied in this article. These perspectives will be examined in future works.…”

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

“…Liu et al [33] used the machine learning method to predict the online taxi-hailing demand, and use RMSE, MAPE, and R square to evaluate the performance. Almalki et al [34] used machine learning technology for research on correlations between the COVID-19 pandemic with socioeconomic, food access, and health problem variables by applying linear and nonlinear regression models. The GIS analysis was also for comparison.…”

COVID-19 spread prediction using socio-demographic and mobility-related data

“…Apart from highlighting which factors are related to the spread of COVID-19, researchers have also explored various modeling methods to uncover the relationships between these factors, such as cross-sectional analysis ( Khazanchi et al, 2020 ), structural equation model ( Kim & Bostwick, 2020 ), regression analysis ( Almalki, Gokaraju, Acquaah, & Turlapaty, 2022 ), correlation analysis ( Sangkham, Thongtip, & Vongruang, 2021 ) and so on. However, most of these studies based on such models fail to account for the spatial heterogeneity and temporal non-stationarity, meaning that the influence of factors on the spread of COVID-19 will vary by space and time.…”