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

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:
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
Introduction to Epidemiology01:26

Introduction to Epidemiology

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

Statistical Methods for Analyzing Epidemiological Data

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:
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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...
Causality in Epidemiology01:21

Causality in Epidemiology

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...

You might also read

Related Articles

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

Sort by
Same author

Missed opportunities for Hepatitis C Virus screening and care among persons hospitalized for injection-related infections.

Drug and alcohol dependence·2026
Same author

Retrospective analysis of emerging health threats by shoreland Travax®, 2019-2024.

Journal of travel medicine·2026
Same author

Training socially accountable clinician-citizens: integrating clinical public health education in a medical school curriculum.

Medical education online·2025
Same author

"SafetyNet": Evaluation of a Recovery Coach and Paramedic Intervention Following Naloxone Resuscitation From an Opioid Overdose.

Substance use & addiction journal·2024
Same author

Hospitalization is a missed opportunity for HIV screening, pre-exposure prophylaxis, and treatment.

Addiction science & clinical practice·2024
Same author

Health and Clinical Impacts of Air Pollution and Linkages with Climate Change.

NEJM evidence·2024

Related Experiment Video

Updated: May 18, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Creating a process for incorporating epidemiological modelling into outbreak management decisions.

Hana Akselrod1, Monica Mercon, Petter Kirkeby Risoe

  • 1Yale New Haven Center for Emergency Preparedness and Disaster Response, New Haven, CT, USA.

Journal of Business Continuity & Emergency Planning
|September 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an operational structure to integrate infectious disease modeling into outbreak management planning. It bridges the gap between model outputs and incident management needs for better public health preparedness.

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: May 18, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Epidemiology
  • Public Health
  • Computational Modeling

Background:

  • Predictive modeling of infectious diseases is crucial for understanding threats and interventions.
  • The CDC Framework emphasizes using epidemic emergence models for public health preparedness.
  • Current limitations exist in integrating modeling output with incident management requirements.

Purpose of the Study:

  • To propose an operational structure for integrating infectious disease modeling into outbreak management action planning.
  • To bridge the gap between modeling capabilities and information needs for incident management.
  • To enhance public health preparedness and response to infectious disease outbreaks.

Main Methods:

  • Utilizing the Incident Command System (ICS) and Synchronization Matrix framework.
  • Developing an adaptable and scalable structure for state and local planners under the National Response Framework (NRF) and Emergency Support Function #8 (ESF-8).
  • Describing specific epidemiological modeling requirements and integrating them with public health emergency decision support processes.

Main Results:

  • The proposed structure facilitates the integration of modeling into action planning for outbreak management.
  • Methods are provided in a checklist format to align modeling output with decision points.
  • The framework guides strategic situational assessments at the community level.

Conclusions:

  • Formalizing these processes will translate CDC policy guidance into practical application during public health emergencies.
  • The operational structure enhances the utility of predictive modeling in real-world outbreak scenarios.
  • Improved integration of modeling supports better decision-making for infectious disease control.