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

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:
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:
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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,...
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...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

You might also read

Related Articles

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

Sort by
Same author

Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review.

Epidemics·2026
Same author

Estimation of the Ebola outbreak size in the Democratic Republic of the Congo.

The Lancet. Infectious diseases·2026
Same author

A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.

Epidemics·2026
Same author

Enhancing epidemic forecast usability for policymakers: A global mixed-methods study.

PLOS global public health·2026
Same author

The <i>R</i> = 1 threshold can misclassify epidemic stability.

Communications physics·2026
Same author

Optimal deployment of gonorrhoea point-of-care tests: modelling the potential impact of diagnostic confirmation testing and screening strategies across five priority populations in Kenya.

The Journal of infectious diseases·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
Same journal

Delayed reward information is underweighted in reinforcement learning with dispersed feedback.

PLoS computational biology·2026
Same journal

GHF-ACL: A novel contrastive learning framework with multi-order graph structures for herb-disease association prediction.

PLoS computational biology·2026
Same journal

GATE: Adaptive learning with working memory by information gating in multi-lamellar hippocampal formation.

PLoS computational biology·2026
Same journal

Evaluating vectors for the design of a spillover-disrupting Lassa virus transmissible vaccine.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa
07:14

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa

Published on: August 30, 2018

A statistical framework for comparing epidemic forests.

Cyril Geismar1,2,3, Peter J White1,3, Anne Cori1,3

  • 1MRC Centre for Global Infectious Disease Analysis, Imperial College School of Public Health, London, United Kingdom.

Plos Computational Biology
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

Epidemiologists can now statistically compare different models of disease spread. A new framework using chi-square tests and PERMANOVA helps assess the significance of variations in epidemic forests, improving public health insights.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: Jun 3, 2026

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa
07:14

Evaluating Dryocosmus Kuriphilus-induced Damage on Castanea Sativa

Published on: August 30, 2018

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Epidemiology
  • Computational Biology
  • Biostatistics

Background:

  • Inferring transmission pathways during outbreaks is crucial for public health.
  • Limited data and complex interactions make transmission inference challenging, often resulting in multiple plausible epidemic forests.
  • Currently, no statistical methods exist to formally compare these different epidemic forests.

Purpose of the Study:

  • To develop and evaluate a statistical framework for comparing epidemic forests.
  • To determine if differences between epidemic forests generated by various methods are statistically significant.

Main Methods:

  • Proposed a framework utilizing chi-square tests and permutational multivariate analysis of variance (PERMANOVA).
  • Assessed the methods' ability to distinguish simulated epidemic forests based on different offspring distributions.
  • Implemented the framework in the R package mixtree.

Main Results:

  • Both chi-square tests and PERMANOVA demonstrated perfect specificity in distinguishing forests with 100+ trees.
  • PERMANOVA consistently showed higher sensitivity than the chi-square test across various epidemic and forest sizes.
  • The study provides the first robust statistical framework for comparing epidemic forests.

Conclusions:

  • The proposed framework offers a statistically rigorous approach to compare epidemic forests.
  • PERMANOVA is a powerful tool for assessing differences in transmission dynamics inference.
  • This work enhances the ability to characterize outbreak transmission and guide interventions.