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

507
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:
507
Introduction to Epidemiology01:26

Introduction to Epidemiology

945
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
945
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

176
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:
176
Causality in Epidemiology01:21

Causality in Epidemiology

726
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
726
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

173
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...
173
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

373
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
373

You might also read

Related Articles

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

Sort by
Same author

Visualizing epidemiological models for policy: design principles for effective communication.

Frontiers in public health·2026
Same author

Visual analytics framework for survival analysis and biomarker discovery from gene expression data.

PloS one·2026
Same author

Infectious disease outbreak controllability: biological, social and public health factors.

Proceedings. Biological sciences·2026
Same author

Bundling-Aware Graph Drawing Revisited.

IEEE transactions on visualization and computer graphics·2025
Same author

Embarrassingly Agile-Data Visualization Methodology in Emergency Responses.

IEEE computer graphics and applications·2025
Same author

Information-Theoretic Cost-Benefit Analysis of Hybrid Decision Workflows in Finance.

Entropy (Basel, Switzerland)·2025
Same journal

Correction to: 'Stokes settling and particle-laden plumes: implications for deep-sea mining and volcanic eruption plumes' (2020), by Mingotti et al.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

A stable hothouse triggered by a tipping mechanism.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Beyond distance: quantifying point cloud dynamics with persistent homology and dynamic optimal transport.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Global stability of the Atlantic overturning circulation: edge state, long transients and boundary crisis under CO2 forcing.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Morse index classification and landscape of Kuramoto system for Hebbian-based binary pattern recognition.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Interpretable and equation-free response theory for complex systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
See all related articles

Related Experiment Video

Updated: Sep 1, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

Jason Dykes1, Alfie Abdul-Rahman2, Daniel Archambault3

  • 1City, University of London, London, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

This study details a collaboration between epidemiological modellers and visualization researchers to enhance COVID-19 pandemic modeling. It highlights visualization

Keywords:
computational notebooksepidemiological modellingvisual analyticsvisual designvisualization

More Related Videos

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

13.6K
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

10.8K

Related Experiment Videos

Last Updated: Sep 1, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K
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

13.6K
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

10.8K

Area of Science:

  • Epidemiology
  • Data Visualization
  • Scientific Collaboration

Background:

  • The COVID-19 pandemic necessitated rapid advancements in epidemiological modeling.
  • Effective communication and analysis of complex data are crucial for public health.
  • Existing visualization research and practice offered potential solutions for modeling challenges.

Purpose of the Study:

  • To document and reflect upon knowledge constructs used in a collaboration between epidemiological modellers and visualization researchers.
  • To evaluate the effectiveness and value of visualization in supporting COVID-19 pandemic modeling.
  • To identify open problems, provide guidance for future collaborations, and safeguard achievements.

Main Methods:

  • Structured independent commentary on deployed visualization approaches.
  • Iterative reflection and synthesis of insights from multiple projects.
  • Documentation of knowledge constructs, including ideas, approaches, and methods.

Main Results:

  • Evidence of visualization's effectiveness and value in epidemiological modeling.
  • Identification of open problems for future research in visualization and epidemiology.
  • Development of guidance and recommendations for future interdisciplinary collaborations.

Conclusions:

  • Interdisciplinary collaboration between visualization researchers and epidemiological modellers is highly beneficial.
  • Visualization tools and methods can significantly enhance the observation, analysis, and dissemination of epidemiological data.
  • Continued engagement between these fields can address emerging data challenges in public health and beyond.