The Pandemic Nature of Reemerging Tuberculosis and the Role of Population Migration in its Spread

Popejoy, Michael W.

doi:10.15406/mojph.2017.06.00180

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The statistical analyses were carried out using the R V4.0.1 language [21] (R Foundation for Statistical Computing, Vienna, Austria) and several libraries, such as "sf" [22], "tidy-verse" [23], "psych" [24], "extrafont" [25], "cluster" [26], "sandwich" [27], "lmtest" [28], "reshape2" [29], "factoextra" [30], "agricolae" [31], "ggspatial" [32], and nortest [33]. The open source code and data required to replicate all analyses in this article are available at Pérez-Segura et al [34].…”

Section: Softwarementioning

confidence: 99%

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

Perez-Segura

Carretero

Rua

2021

IJERPH

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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.

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