Multivariate Analysis of Risk Factors of the COVID-19 Pandemic in the Community of Madrid, Spain

Abstract

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world’s most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.

Interplay of Demographic Influences, Clinical Manifestations, and Longitudinal Profile of Laboratory Parameters in the Progression of SARS-CoV-2 Infection: Insights from the Saudi Population

“…The multivariate logistic regression analysis highlighted the critical need to understand the interaction between patient characteristics and early biomarker detection in assessing SARS-CoV-2 infection severity and outcomes. These results build on and extend the findings of previous studies [69][70][71]. In our model, individual clinical and demographic factors did not demonstrate significant predictive power in the univariate analysis.…”

“…In our model, individual clinical and demographic factors did not demonstrate significant predictive power in the univariate analysis. However, they collectively exhibited a considerable ability to predict disease severity in the model, which aligns with earlier reported research in building models to assess severity in the early stage [69][70][71]. These findings reflect the accumulative effect of individual characteristics on disease presentation and severity level [69][70][71][72].…”

“…However, they collectively exhibited a considerable ability to predict disease severity in the model, which aligns with earlier reported research in building models to assess severity in the early stage [69][70][71]. These findings reflect the accumulative effect of individual characteristics on disease presentation and severity level [69][70][71][72]. For instance, age has consistently been identified as a significant predictor in various studies, underscoring its multifaceted impact on the immune system and organ function [27,[73][74][75][76].…”

Changing face of socio-economic vulnerability and COVID-19: An analysis of country wealth during the first two years of the pandemic

“…During these four years, an intense scientific production has been generated on the risk factors that catalyze COVID-19 disease. The risk factors identified come from different dimensions, such as environmental [1,2], biological [3,4], political [5] or social. Within this last dimension, several empirical studies have found that territorial wealth is a protective factor against the incidence of COVID-19, in countries such as the United States [6][7][8][9], United Kingdom [10,11], Peru [12], Brazil [13], or at the international level [14].…”

Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2

“…Therefore, multivariate analyses are a preferable approach when analysing complex datasets, providing a more realistic basis for robust and accurate clinical decisions [6]. Moreover, multivariate analysis enables the assessment of the contribution of multiple factors concerning one or more clinical factors to reflect reality, reveal relationships between the factors analysed, and reduce the bias of univariate patient characteristics across studies [6][7][8][9]. Nowadays, several multivariate approaches are available that consider complex, multidimensional relationships between factors.…”