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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

84
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...
84
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

109
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.
109
Typical Model Studies01:30

Typical Model Studies

385
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
385
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

65
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...
65
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

3.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.5K
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

80
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
80

You might also read

Related Articles

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

Sort by
Same journal

Bayesian variable selection in sample selection models using spike-and-slab priors.

Computational statistics·2026
Same journal

A reduced basis decomposition approach to efficient data collection in pairwise comparison studies.

Computational statistics·2026
Same journal

A latent class pattern mixture model for nonignorable nonresponses in multivariate categorical data.

Computational statistics·2026
Same journal

A stochastic approach to k-nearest neighbors search using a fixed radius method.

Computational statistics·2026
Same journal

Sparse Bayesian multidimensional scaling(s).

Computational statistics·2025
Same journal

Misspecification-robust likelihood-free inference in high dimensions.

Computational statistics·2025
See all related articles

Related Experiment Video

Updated: Jul 25, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Policy evaluation using model over-fitting: the Nordic case.

Armando Tapia1, Silvestre L González1, Jose R Vergara1

  • 1School of Engineering, Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX, Mexico, CP 04510 Mexico.

Computational Statistics
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study applies the susceptible, infected, recovered (SIR) model to analyze COVID-19 pandemic policies. It identifies policy impacts on disease spread dynamics by adjusting model parameters based on real-world events and data.

Keywords:
COVID-19Public-policySIR-modelSimulation

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

3.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K

Related Experiment Videos

Last Updated: Jul 25, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
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

3.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K

Area of Science:

  • Epidemiology
  • Public Health Policy
  • Mathematical Modeling

Background:

  • The COVID-19 pandemic necessitated rapid evaluation of public health interventions.
  • Understanding the epidemiological impact of various policies is crucial for effective pandemic response.
  • Traditional econometric models may not fully capture the dynamic nature of disease spread.

Purpose of the Study:

  • To assess the effectiveness of different public policy alternatives in managing the COVID-19 pandemic.
  • To utilize the susceptible, infected, recovered (SIR) epidemiological model for policy impact analysis.
  • To identify specific policy interventions that influence the dynamics of disease transmission.

Main Methods:

  • Employing the susceptible, infected, recovered (SIR) model to simulate disease spread.
  • Over-fitting the SIR model to raw mortality data to identify critical time points for parameter adjustment.
  • Correlating parameter changes (daily contacts, contagion probability) with historical public policies and social events.

Main Results:

  • The study identifies specific time points where policy or social events significantly altered disease transmission parameters.
  • The SIR model, when adjusted with real-world data, provides a framework for evaluating the impact of interventions.
  • Insights into the effectiveness of various public health strategies were gained through this modeling approach.

Conclusions:

  • The SIR model, integrated with historical event analysis, offers a robust method for evaluating public policy effectiveness during pandemics.
  • Policy interventions and social dynamics demonstrably influence key epidemiological parameters like contact rates and contagion probability.
  • This approach provides a nuanced understanding of pandemic dynamics beyond standard statistical analyses.