Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Beyond the metropolises: the decentralization of chikungunya to non-metropolitan areas across Brazil.

Cadernos de saude publica·2026
Same author

On the simultaneous inference of susceptibility distributions and intervention effects from epidemic curves.

Epidemics·2026
Same author

Mortality trends for diabetes mellitus, hypertension and cardiovascular disease among people living with and without HIV in Brazil during the COVID-19 pandemic, 2020-2022.

HIV medicine·2026
Same author

Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Uncertainty and inconsistency of COVID-19 non-pharmaceutical intervention effects with multiple competitive statistical models.

Scientific reports·2026
Same author

Mosquito-disseminated diflubenzuron and spinosad as alternatives to mosquito-disseminated pyriproxyfen: a proof-of-concept, blind, controlled comparison in experimental cages.

Memorias do Instituto Oswaldo Cruz·2025
Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026
Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026
Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026
Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026
Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026
Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

A Bayesian framework for parameter estimation in dynamical models.

Flávio Codeço Coelho1, Cláudia Torres Codeço, M Gabriela M Gomes

  • 1Instituto Gulbenkian de Ciência, Oeiras, Portugal. fccoelho@fgv.br

Plos One
|June 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for uncertainty analysis in biological models. It successfully calibrates an influenza transmission model using real-world incidence data, improving predictive accuracy.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 1, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Mathematical Biology
  • Computational Epidemiology
  • Systems Biology

Background:

  • Mathematical models are crucial for understanding complex biological dynamics.
  • Integrating theoretical models with experimental data requires addressing inherent uncertainties.
  • Accurate uncertainty handling is vital for predictive modeling in biology and epidemiology.

Purpose of the Study:

  • To present a general framework for uncertainty analysis and parameter estimation in dynamic biological systems.
  • To develop a method that is independent of the specific model type used.
  • To apply the framework to a real-world epidemiological problem.

Main Methods:

  • Developed a general framework for uncertainty analysis and parameter estimation.
  • The framework is model-agnostic, applicable to various dynamic biological models.
  • Applied the framework to fit an SIR-like influenza transmission model.

Main Results:

  • Successfully fitted an SIR-like influenza transmission model to 7 years of incidence data.
  • The framework effectively handled uncertainties in modeling biological systems.
  • Demonstrated the framework's utility with data from Belgium, the Netherlands, and Portugal.

Conclusions:

  • The presented framework provides a robust approach for uncertainty analysis in biological modeling.
  • This method enhances the reliability of predictions from dynamic models.
  • The successful application to influenza data highlights its potential for epidemiological studies.