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Multivariate Statistical Interpretation of Airborne Diseases using Principal Components Analysis

Paul Boye

2025enairborne diseasescommunicable diseasesprincipal component analysismultivariate analysisepidemiologypublic health

Abstract

Language:

Accurate and timely understanding of relationships among communicable diseases is essential for designing effective control measures and treatment policies. This study applies Principal Components Analysis (PCA) to explore and interpret the relationships among three airborne communicable diseases—tuberculosis (TB), chickenpox, and measles—using case data from Bibiani Government Hospital in Ghana (2013–2018). The Kaiser criterion and scree plot are used to determine the appropriate number of principal components (PCs), and projections of diseases on PC planes support the interpretation of inter-disease relationships. The first three principal components (PC1, PC2, and PC3) explain 46.71%, 30.62%, and 22.67% of the total variance respectively, capturing almost all useful information in the dataset. Results show marked strong correlations among the diseases with respect to these PCs, highlighting PCA’s effectiveness in revealing underlying patterns. Given the limited number of diseases considered, the work is positioned as a preliminary investigation, demonstrating PCA as a versatile and promising multivariate statistical tool to support public health experts and policymakers in interpreting disease relationships and making informed decisions.

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Cite This Work

@article{cb10008f-f034-484d-a076-0b5d19b8b615,
  title={Multivariate Statistical Interpretation of Airborne Diseases using Principal  Components Analysis  },
  author={Paul Boye},
  year={2025},
  language={en}
}
TY  - JOUR
TI  - Multivariate Statistical Interpretation of Airborne Diseases using Principal  Components Analysis  
AU  - Paul Boye
PY  - 2025
LA  - en
ER  -

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