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QUANTITATIVE ANALYSIS OF DRUG DISTRIBUTION USING COMPARTMENTAL MODELS

Fatima Hauwa Bello
Published 21 March 2026
Vol. 14, No. 2 (2026)
pp. 28-46
CC BY 4.0
  1. 1
    Fatima Hauwa Bello
    Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State, Nigeria
    NG

Alcohol addiction poses serious health and social challenges, making the study of ethanol metabolism in the human body an important area of research. Most existing studies on ethanol kinetics focus on single-dose experiments designed to determine the rate of alcohol elimination or to assess impairment, particularly in relation to highway safety. However, real-life drinking patterns often involve repeated alcohol consumption, which introduces additional complexity in modeling ethanol absorption, distribution, and elimination.
Repeated dosing scenarios require more sophisticated analytical approaches, and as a result, many studies have avoided such investigations. Mathematical modeling, particularly compartmental models, provides an effective framework for analyzing these complex dynamics. Compartmental models describe the movement of substances between different physiological compartments and help capture the processes involved in ethanol metabolism.
This study applies a compartmental mathematical approach to analyze the kinetics of ethanol metabolism in the human body. The model represents the body as interconnected compartments through which ethanol is absorbed, distributed, and eliminated over time. The approach provides insight into the behavior of alcohol concentration under different intake conditions and highlights the usefulness of mathematical models in understanding alcohol metabolism

JournalInternational Journal of Data Science and Statistics
ISSN3065-0577
Volume / IssueVol. 14, No. 2 (2026)
Pages28-46
Published21 March 2026
DOI10.5281/zenodo.19663638
Access Open Access
LicenseCC BY 4.0 — reuse with attribution
PublisherKeith Publications
Bello, F. (2026). QUANTITATIVE ANALYSIS OF DRUG DISTRIBUTION USING COMPARTMENTAL MODELS. International Journal of Data Science and Statistics, Vol. 14 No. 2, pp. 28-46. DOI: https://doi.org/10.5281/zenodo.19663638

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