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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

889
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
889
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

449
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
449
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

481
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:
481
Manipulation and Analysis01:21

Manipulation and Analysis

282
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
282
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

231
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...
231

You might also read

Related Articles

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

Sort by
Same author

Traveler-derived importation risk underestimates regional disease activity: Evidence from Okinawa, Japan.

Journal of infection and public health·2026
Same author

Diabetes Mortality in the Post-Pandemic Era: What Recent Global Burden of Disease Data Reveals About COVID-19's Lasting Impact.

Epidemiologia (Basel, Switzerland)·2026
Same author

Counting everyone onboard is not enough: modelling lessons from the MV Hondius Andes virus outbreak.

Journal of travel medicine·2026
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

A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl.

Bulletin of mathematical biology·2026
Same author

A comparative study of simulation-based inference methods for epidemic models with identifiability considerations.

PLoS computational biology·2026

Related Experiment Video

Updated: Jan 12, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K

A mobility-adjusted framework for regional Rt estimation: enhancing spatial interpretation of transmission dynamics.

Young Kim1, Junwoo Jo1, Byul Nim Kim1

  • 1Department of Applied Mathematics, Kyung Hee University, 1732, Deogyeong-daero, Yongin, Gyeonggi-do, 17104, Republic of Korea.

BMC Infectious Diseases
|November 3, 2025
PubMed
Summary

This study introduces a new method to estimate infectious disease spread by including population movement, improving accuracy in low-incidence areas and identifying risks in connected regions.

Keywords:
COVID-19Effective reproduction numberInter-regional connectivityMobilitySpatial heterogeneity

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

11.0K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K

Related Experiment Videos

Last Updated: Jan 12, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K
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

11.0K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Estimating the effective reproduction number (R) is crucial for disease outbreak management.
  • Conventional methods often overlook spatial connectivity and inter-regional mobility.
  • There is a need for models that integrate spatial dynamics into transmission estimations.

Purpose of the Study:

  • To develop and validate a mobility-adjusted framework for regional R estimation.
  • To incorporate inter-regional population movement data into transmission dynamics.
  • To improve the accuracy of R estimates by accounting for external transmission sources.

Main Methods:

  • Proposed a novel framework for mobility-adjusted R estimation.
  • Utilized a daily mobility matrix derived from telecommunication records for COVID-19 data in South Korea.
  • Compared the proposed method with the conventional Wallinga-Teunis (WT) estimator.

Main Results:

  • The mobility-adjusted R estimates were more responsive in high-mobility regions during the early epidemic phase.
  • The framework mitigated inflated R estimates in low-incidence regions by accounting for inter-regional transmission.
  • Daegu, the outbreak epicenter, showed a sharp early peak in mobility-adjusted R, reflecting its role in onward transmission.

Conclusions:

  • The mobility-adjusted framework enhances R estimation by integrating spatial transmission dynamics.
  • The method offers context-aware insights, mitigating biases in low-incidence areas and highlighting risks in connected regions.
  • This approach provides a practical tool for geographically targeted interventions in diverse epidemiological settings.