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

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

192
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...
192
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

221
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:
221
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.0K
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,...
1.0K
Causality in Epidemiology01:21

Causality in Epidemiology

934
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...
934
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

566
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:
566
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

1.7K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Timely Availability and Accessibility of Health Data: Meeting the Challenge of Pan-Canadian Health Charter Principle 6.

Healthcare management forum·2026
Same author

An Exploration of Machine Learning Methods in Human Biomonitoring.

International journal of environmental research and public health·2026
Same author

Frequency-Based Prioritization of ICD-10-CA/CCI to OMOP Mapping in a Canadian Hospital Data Warehouse: Coverage and Usagi Performance.

Studies in health technology and informatics·2026
Same author

Early Detection Intervals for Evaluating Event-Based Surveillance System: Reference Dataset Development Study.

JMIR public health and surveillance·2026
Same author

ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.

bioRxiv : the preprint server for biology·2026
Same author

Automated Chest X-ray Report Generation Remains Unsolved.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026

Related Experiment Video

Updated: Sep 22, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.4K

A Conceptual Framework for Representing Events Under Public Health Surveillance.

Anya Okhmatovskaia1, Yannan Shen1, Iris Ganser1,2

  • 1School of Population and Global Health, McGill University, Canada.

Studies in Health Technology and Informatics
|May 25, 2022
PubMed
Summary

Integrating multiple event-based surveillance (EBS) systems can enhance global disease monitoring. This study proposes a common conceptual framework to enable data integration across these public health surveillance systems.

Keywords:
Event-based surveillanceconceptual modellingoutbreak detection

More Related Videos

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

8.9K
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

13.7K

Related Experiment Videos

Last Updated: Sep 22, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.4K
Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

8.9K
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

13.7K

Area of Science:

  • Public Health
  • Infectious Disease Surveillance
  • Information Systems

Background:

  • Global disease surveillance relies on multiple event-based surveillance (EBS) systems.
  • Lack of a common data framework hinders information integration across diverse EBS systems.
  • Previous experimental settings demonstrated the benefits of integrated EBS.

Purpose of the Study:

  • To propose a conceptual framework for representing data in public health surveillance.
  • To address the gap preventing integration of multiple event-based surveillance systems.

Main Methods:

  • Development of a candidate conceptual framework for public health surveillance data.
  • Focus on representing events and related concepts within EBS.

Main Results:

  • A proposed conceptual framework designed for event-based surveillance data.
  • Facilitates standardized data representation for improved integration.

Conclusions:

  • A common conceptual framework is essential for integrating event-based surveillance systems.
  • The proposed framework aims to overcome practical barriers to data integration.
  • Enables enhanced global disease surveillance through improved information sharing.