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Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
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The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Related Experiment Video

Updated: Apr 18, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Back to the real world: connecting models with data.

Rebecca M Mitchell1, Robert H Whitlock2, Yrjö T Gröhn3

  • 1Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA; Centers for Disease Control and Prevention, Division of Parasitology and Malaria, GA, USA.

Preventive Veterinary Medicine
|January 14, 2015
PubMed
Summary

Mathematical modeling of Mycobacterium avium subspecies paratuberculosis (MAP) infection dynamics improves understanding and intervention strategies. Precise parameter estimation using real-world data enhances model accuracy and predictive power for infectious diseases.

Keywords:
Mathematical modelingMycobacterium avium subspecies paratuberculosisObservational studies

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Dynamics

Background:

  • Mathematical models are crucial for understanding infectious disease biology and evaluating interventions.
  • Mycobacterium avium subspecies paratuberculosis (MAP) infection dynamics require robust modeling approaches.

Purpose of the Study:

  • To develop a mathematical model for MAP infection dynamics.
  • To demonstrate methods for parameter estimation in state transition models.
  • To integrate simulation models with real-world data for improved predictions.

Main Methods:

  • Developed a mathematical model incorporating known and hypothetical MAP infection biology.
  • Utilized longitudinal field data from an observational study for parameter estimation.
  • Employed molecular diagnostics on MAP strains to refine parameter estimates.

Main Results:

  • Precise parameter estimates were achieved using detailed, molecularly characterized MAP strain data.
  • The integration of real-world data enhanced the realism and predictive capability of the model.
  • Model quality is contingent upon biological accuracy in structure and data quality for parameterization and validation.

Conclusions:

  • Mathematical modeling of infectious disease dynamics is valuable for understanding pathophysiology, epidemiology, and control.
  • High-quality biological data and comprehensive real-world data are essential for valid and predictive modeling outcomes.
  • Enhanced model development through detailed data integration leads to more reliable insights into infectious disease dynamics.