Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

Modeling with Differential Equations

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...

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

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

Sort by

Same author

Adaptive social distancing under variant-specific transmission dynamics in a spatial SEIIR model with reinforcement learning.

Journal of theoretical biology·2026

Same author

Predictors of Functional Improvement in Patients with Chronic Stroke Participating in Group-Based Telerehabilitation: A Post-hoc Analysis of a Randomized Controlled Trial.

NeuroRehabilitation·2026

Same author

Evaluating the impact of South-North Joint Malaria Control Project on spatiotemporal transmission dynamics in South Korea using dynamic mode decomposition.

Malaria journal·2026

Same author

Multi-target bioactive M(II) pyridine-diamine complexes: synthesis, characterization, in vitro and in silico evaluation.

Bioorganic chemistry·2026

Same author

Integrating viral kinetics and population spread in a one health framework to explain variant-specific epidemic dynamics.

One health (Amsterdam, Netherlands)·2026

Same author

Adiponectin as a Predictor of Metabolic Dysfunction-Associated Steatotic Liver Disease and Non-Alcoholic Fatty Liver Disease: A 17-Year Korean Cohort Study (Diabetes Metab J 2026;50:331-42).

Diabetes & metabolism journal·2026

Search research articles

Related Experiment Video

Updated: Jan 13, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model

Byul Nim Kim¹, Minchan Choi¹, Hyosun Lee¹

¹Department of Applied Mathematics, Kyung Hee University, Yongin, 17104, Republic of Korea.

|January 7, 2026

Summary

Understanding COVID-19 spread requires analyzing mobility and interventions. Densely connected regions like Seoul and Gyeonggi significantly drive transmission, necessitating adaptive, phase-dependent public health strategies.

Keywords:

COVID-19 Effective reproduction number Mobility restrictions Multi-patch model Regional heterogeneity

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

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

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Jan 13, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

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

Published on: December 9, 2015

Area of Science:

Computational epidemiology
Infectious disease dynamics
Public health modeling

Background:

COVID-19 transmission is complex, influenced by mobility, connectivity, and interventions, making region-specific risk assessment challenging.
Effective epidemic preparedness demands understanding spatial and temporal disease spread dynamics.

Purpose of the Study:

To develop a robust computational framework for assessing interregional COVID-19 transmission dynamics.
To quantify the impact of mobility and interventions on disease spread across different epidemic phases.
To identify key transmission hubs and evaluate targeted intervention strategies.

Main Methods:

Utilized a multi-patch model to estimate time-dependent regional effective reproduction numbers.
Integrated high-resolution mobility and COVID-19 incidence data from South Korea.
Distinguished locally transmitted infections from mobility-induced cases.

Main Results:

Seoul and Gyeonggi were identified as dominant sources of interregional COVID-19 spread, with influence varying by pandemic phase.
Mobility controls in identified transmission hubs significantly reduced infection spread during the Pre-Delta phase.
Densely connected regions disproportionately contribute to nationwide transmission.

Conclusions:

Adaptive, phase-dependent intervention strategies are more effective than uniform nationwide policies for controlling COVID-19.
Integrating real-world mobility data with epidemic modeling provides a scalable framework for data-driven public health responses.
Targeted interventions in key transmission hubs are crucial for mitigating widespread infectious disease transmission.