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

1.2K
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:
1.2K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

720
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:
720
Introduction to Epidemiology01:26

Introduction to Epidemiology

2.5K
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,...
2.5K
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

56
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...
56
Causality in Epidemiology01:21

Causality in Epidemiology

2.1K
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...
2.1K
Test for Homogeneity01:23

Test for Homogeneity

2.6K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.6K

You might also read

Related Articles

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

Sort by
Same author

High-Throughput Geocoding to Assess Short-Term Air Pollution in Correlation With Mortality in a French Cancer Patient Cohort.

International journal of cancer·2026
Same author

Socioeconomic inequalities and the COVID-19 pandemic in France: Territorial analyzes based on epidemic wave and metropolitan area.

PloS one·2026
Same author

Antibiotic resistance prediction with an attention-based bi-LSTM clinical decision support system.

Computer methods and programs in biomedicine·2026
Same author

Clove Essential Oil Enhances Antioxidant Defenses and Reduces DNA Damage in a Cellular Model of Parkinson's Disease.

Parkinson's disease·2026
Same author

Boolean Networks with Classic and New Updating Modes Applied to Genetic Regulation in Some Familial Diseases.

International journal of molecular sciences·2025
Same author

Dynamics of social inequalities in severe COVID-19 outcomes in metropolitan France from 2020 to 2022.

Communications medicine·2025

Related Experiment Video

Updated: Apr 9, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment.

Aline Guttmann1, Xinran Li2, Fabien Feschet3

  • 1Department of Biostatistics, Clermont University Hospital, Clermont-Ferrand, France; UMR CNRS UDA 6284 ISIT, Auvergne University, Clermont-Ferrand, France.

Plos One
|June 19, 2015
PubMed
Summary

New metrics, averaged and cumulated Tanimoto coefficients (TC), offer a global view of disease cluster detection test performance, improving spatial accuracy and power assessments for comparable studies.

More Related Videos

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.2K
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

14.3K

Related Experiment Videos

Last Updated: Apr 9, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.2K
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

14.3K

Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Biostatistics

Background:

  • Current disease cluster detection tests (CDTs) assess significance but lack standardized performance evaluation, hindering comparability.
  • Assessing location accuracy is crucial for CDTs, but existing methods focus on statistical errors, not spatial precision.
  • The Tanimoto coefficient (TC) measures similarity but is limited to single cluster assessments.

Purpose of the Study:

  • To propose novel global performance indicators for CDTs that integrate spatial accuracy and statistical power.
  • To introduce the averaged Tanimoto coefficient (TC) and cumulated TC as comprehensive performance metrics.
  • To facilitate standardized comparisons of CDT performance across different studies.

Main Methods:

  • A simulation study was conducted to evaluate multiple CDTs.
  • The Tanimoto coefficient (TC) was adapted to derive the averaged TC and cumulated TC statistics.
  • Performance maps were generated using these new indicators for systematic spatial assessment.

Main Results:

  • The averaged TC and cumulated TC provide a global overview of CDT performance, encompassing both power and location accuracy.
  • The cumulated TC demonstrated superiority over the averaged TC in assessing overall CDT performance.
  • The proposed indicators enabled systematic spatial assessments and facilitated comparisons between different CDTs.

Conclusions:

  • The cumulated TC offers a robust and comprehensive metric for evaluating disease cluster detection tests.
  • Standardized performance assessment using these novel indicators can improve the reliability and comparability of epidemiological studies.
  • The developed methods and performance maps aid in selecting optimal CDTs for disease surveillance.