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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

70
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
70
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

161
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
161
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.3K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.3K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

115
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
115
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.7K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.7K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

130
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
130

You might also read

Related Articles

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

Sort by
Same author

Austrian mosquito species inventory (diptera: culicidae) with a detailed analysis of the Anopheles maculipennis species complex.

Scientific reports·2026
Same author

A modelling exploration of potential spatiotemporal risk of high pathogenicity avian influenza virus introduction to Danish dairy herds through the contaminated environment.

Research in veterinary science·2026
Same author

Potential for integrated monitoring of tick-borne diseases: Indices of tick activity, citizen science, and tick-borne Lyme neuroborreliosis in Denmark from 2017 to 2024.

Ticks and tick-borne diseases·2026
Same author

Can public-domain datasets be leveraged to identify factors associated with the occurrence of African swine fever in europe?

Acta veterinaria Scandinavica·2025
Same author

Northward expansion of the thermal limit for the tick Ixodes ricinus over the past 40 years.

Parasites & vectors·2025
Same author

Predator-Prey Trophic Interactions and Seasonality of Highly Pathogenic Avian Influenza Virus in Denmark, 2016-2023.

Zoonoses and public health·2025

Related Experiment Video

Updated: Aug 5, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K

Predicting Culex pipiens/restuans Population Dynamics Using a Weather-Driven Dynamic Compartmental Population Model.

Karin Bakran-Lebl1, Lene Jung Kjær2, Beate Conrady2,3

  • 1Institute for Medical Microbiology and Hygiene, AGES-Austrian Agency for Health and Food Safety, 1090 Vienna, Austria.

Insects
|March 28, 2023
PubMed
Summary

This study developed a model to predict mosquito population dynamics, crucial for understanding arbovirus spread. The model accurately forecasts mosquito abundance, aiding in disease control strategies.

Keywords:
Cx. pipiensCx. restuansmodelingmosquitopopulation dynamicsvectorweather

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Preparing and Injecting Embryos of Culex Mosquitoes to Generate Null Mutations using CRISPR/Cas9
07:45

Preparing and Injecting Embryos of Culex Mosquitoes to Generate Null Mutations using CRISPR/Cas9

Published on: September 10, 2020

7.1K

Related Experiment Videos

Last Updated: Aug 5, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Preparing and Injecting Embryos of Culex Mosquitoes to Generate Null Mutations using CRISPR/Cas9
07:45

Preparing and Injecting Embryos of Culex Mosquitoes to Generate Null Mutations using CRISPR/Cas9

Published on: September 10, 2020

7.1K

Area of Science:

  • Ecology
  • Epidemiology
  • Mathematical Biology

Background:

  • Mosquitoes in the genus *Culex* are significant vectors for arboviruses.
  • Understanding mosquito population dynamics is vital for disease ecology.
  • Mosquito vital rates are influenced by environmental factors like temperature and precipitation.

Purpose of the Study:

  • To develop a compartmental model for *Cx. pipiens/restuans* population dynamics.
  • To assess the impact of temperature, precipitation, and day length on mosquito populations.
  • To evaluate mosquito control strategies by targeting vital rates.

Main Methods:

  • A compartmental model was created for *Cx. pipiens/restuans* population dynamics.
  • The model incorporates environmental drivers: temperature, precipitation, and day length.
  • Long-term mosquito capture data from Cook County, Illinois, was used for model evaluation.

Main Results:

  • The model accurately reproduced between-year variations and seasonal trends in mosquito abundance.
  • The model demonstrated high accuracy in predicting weekly mean *Cx. pipiens/restuans* abundance over 20 years.
  • Model evaluation supported the effectiveness of targeting specific vital rates for mosquito control.

Conclusions:

  • The developed model provides a robust tool for understanding *Cx. pipiens/restuans* population dynamics.
  • Environmental factors significantly influence mosquito populations, impacting arbovirus transmission.
  • The model can inform and enhance the effectiveness of mosquito control interventions.