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.

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R² = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.

Food security is a condition where people at all times have physical, economic, and social access to sufficient, safe, and nutritious food. Even though the world produces enough food for all humans, we have people with low access to food due to several socioeconomic variables, and this lack of food access affects their lives and health in return. On regular days, physical access to food outlets is limited by several variables and that creates categories in food access areas. In extreme weather events, the accessibility becomes more limited due to the impact on mobility, and interruption to electricity or transportation systems. These weather events increase due to climate change, and the future is highly unpredictable. The cumulative effect of climate change and pandemics increases the risk of complicated circumstances for food security. COVID-19 pandemic is considered the largest epidemic, and the onset of these large-scale future epidemics is predicted to be inevitable. This chapter will describe the extent of food access categories in Guilford county of North Carolina pre-COVID-19, and the influence of climate change and the COVID-19 pandemic on the food-access distribution.

Higher education institutions face a dual imperative: expanding access while reducing their environmental and operational footprint. Grounded in Triple Bottom Line theory and the Sustainable Development Goal (SDG) interlinkages framework, this study examines faculty and postgraduate students’ perceived associations between online learning—across environmental, economic, educational, and health dimensions—and five SDGs (SDG3, SDG4, SDG8, SDG12, and SDG13), and how these perceived dimensions interrelate to form a coherent structural model. Data were collected from 306 faculty members and postgraduate students via a five-point Likert-scale questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). All nine direct and four specific indirect path relationships were statistically significant at p < 0.001. Perceived economic rationalization exerted the strongest direct path coefficient on perceived responsible consumption (β = 0.648) and the strongest specific indirect effect on perceived climate action (β = 0.371), while perceived health and well-being served as the central pathway statistically linking perceived online learning benefits to perceived quality education and decent work outcomes (β = 0.557 and 0.608, respectively). The structural model statistically accounted for 42–54% of the variance in these perceived SDG-related outcomes. These findings suggest that online learning may be perceived by faculty and postgraduate students as an integrated structural lever—rather than a source of isolated benefits—for advancing interconnected dimensions of the 2030 Agenda in higher education; objective verification of these perceived associations (e.g., using institutional records or environmental metrics) remains an important direction for future research. Theoretical and practical implications for university leaders and policymakers are discussed.