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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

171
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:
171
Causality in Epidemiology01:21

Causality in Epidemiology

684
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...
684
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

169
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...
169
Pareto Chart00:52

Pareto Chart

6.9K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.9K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

484
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:
484
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.6K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.6K

You might also read

Related Articles

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

Sort by
Same author

Med-KAG: Preliminary Results of a Medical Knowledge-Augmented Generation Approach.

Studies in health technology and informatics·2026
Same author

Digital Health for Precision Prevention.

Yearbook of medical informatics·2025
Same author

Harnessing the Core Propagation Phenomenon Ontology to Develop a Knowledge Graph for Tracking Health-Related Phenomena.

Studies in health technology and informatics·2024
Same author

Merging Biomedical Ontologies with BioSTransformers.

Studies in health technology and informatics·2024
Same author

Informatics for One Health.

Yearbook of medical informatics·2024
Same author

Modeling and integrating interactions involving the CYP450 enzyme system in a multi-terminology server: Contribution to information extraction from a clinical data warehouse.

International journal of medical informatics·2023
Same journal

Data Integration for the Study of Outstanding Productivity in Biomedical Research.

Procedia computer science·2023
Same journal

A non-invasive method for prediction of neurodegenerative diseases using gait signal features.

Procedia computer science·2023
Same journal

Use of a custom testing center locator tool to improve STI and HIV testing rates in adolescent men who have sex with men as part of an online sexual health program.

Procedia computer science·2023
Same journal

Audio delivery of health information: An NLP study of information difficulty and bias in listeners.

Procedia computer science·2023
Same journal

Hourly forecasting of traffic flow rates using spatial temporal graph neural networks.

Procedia computer science·2023
Same journal

Cognitive Cameras on the Edge for Crowd Physical Distancing Monitoring in the Covid-19 Era.

Procedia computer science·2023
See all related articles

Related Experiment Video

Updated: Aug 24, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Tracing and analyzing COVID-19 dissemination using knowledge graphs.

Gabriel H A Medeiros1, Lina F Soualmia1, Cecilia Zanni-Merk1

  • 1Normandy University, LITIS EA 4108, Saint-Étienne-du-Rouvray, France.

Procedia Computer Science
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Implementing knowledge graphs to analyze flight data and COVID-19 spread could improve virus dissemination control. This approach offers a promising method for understanding and managing global pandemics.

Keywords:
COVID-19Data VisualizationKnowledge GraphsMassive Heterogeneous Data Management

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

521
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

371

Related Experiment Videos

Last Updated: Aug 24, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

521
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

371

Area of Science:

  • Epidemiology
  • Data Science
  • Network Analysis

Background:

  • The global spread of COVID-19 (SARS-CoV-2) highlighted a need for better tools to understand and control virus dissemination.
  • Inefficient data visualization and analysis hindered effective responses to the pandemic.
  • Understanding spatial and temporal virus spread requires analyzing complex, heterogeneous datasets.

Purpose of the Study:

  • To propose a methodology for managing and analyzing complex virus dissemination data.
  • To explore the utility of knowledge graph models in understanding virus spread patterns.
  • To demonstrate a promising approach for virus dissemination analysis and control.

Main Methods:

  • Utilizing knowledge graph models to manage and analyze complex, massive, and heterogeneous data.
  • Integrating flight connection data with COVID-19 epidemiological data.
  • Applying various analysis and visualization tools to the knowledge graph.

Main Results:

  • Knowledge graph models provide a structured way to represent and analyze virus spread.
  • The methodology facilitates understanding spatial and temporal aspects of virus dissemination.
  • The approach shows promise for studying the spread of various viruses.

Conclusions:

  • Knowledge graphs offer a powerful framework for studying virus dissemination.
  • This methodology can be enriched with additional data for future predictive analysis.
  • Effective pandemic management requires advanced data analysis tools like knowledge graphs.