Jove
Visualize
Contact Us

Related Concept Videos

Survival Curves01:18

Survival Curves

259
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
259
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

548
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
548
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

693
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...
693
Life Histories01:29

Life Histories

18.4K
Overview
18.4K
Hazard Rate01:11

Hazard Rate

167
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
167

You might also read

Related Articles

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

Sort by
Same author

Environmental Inequality Embedded in Global Food Trade: Evidence from the Mismatch between Embodied Air Pollutant Emissions and Value Added.

Environmental science & technology·2026
Same author

Potential effects of suboptimal nighttime temperatures when evaluating heat-related labour and economic losses in the United Kingdom.

iScience·2026
Same author

Associations of early childhood caries and child intelligence quotient: evidence from the Shanghai Birth Cohort.

Frontiers in public health·2026
Same author

A Novel Therapy With a One-Month Ultrashort Regimen to Halt Progression From Latent Infection to Active Tuberculosis Among Close Contacts (The TB‑YOUTH Study): Protocol for a Cluster Randomized Controlled Trial.

JMIR research protocols·2026
Same author

A GAMLSS approach to digital dementia screening and profiling using a two-step cognitive self-assessment tool in China.

Age and ageing·2026
Same author

Genetic inference of the etiological crosstalk between primary glaucomas and retinal vascular occlusions.

Advances in ophthalmology practice and research·2026
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 Experiment Video

Updated: Aug 27, 2025

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

8.8K

Simplicial epidemic model with birth and death.

Hui Leng1, Yi Zhao1, Jianfeng Luo1

  • 1School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.

Chaos (Woodbury, N.Y.)
|October 1, 2022
PubMed
Summary

This study introduces a new epidemic model considering group interactions and vital dynamics. Birth and death rates significantly impact disease spread, influencing stable states and outbreak thresholds in networks.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.6K

Related Experiment Videos

Last Updated: Aug 27, 2025

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

8.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.6K

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Epidemic modeling is crucial for understanding disease dynamics.
  • Group interactions and vital dynamics (birth/death) are key factors in disease spread.
  • Existing models often simplify network structures or omit demographic effects.

Purpose of the Study:

  • To propose a novel simplicial susceptible-infected-susceptible (SIS) epidemic model incorporating group interactions and vital dynamics.
  • To analyze the influence of system parameters, particularly birth and death rates, on epidemic dynamics.
  • To investigate the emergence of bistable states and their impact on disease persistence.

Main Methods:

  • Formulation of site-based evolutions using quenched mean-field probability equations.
  • Dimensionality reduction via the mean-field method for theoretical analysis.
  • Extensive simulations on empirical and synthetic networks to validate model predictions.

Main Results:

  • Birth and death rates affect the existence and stability of disease-free and endemic equilibria.
  • The model exhibits bistability, including coexistence of stable disease-free and endemic states.
  • A novel bistable state emerges: coexistence of a stable periodic outbreak and a disease-free state.
  • Birth and death rates modify infected node density and outbreak thresholds.

Conclusions:

  • Vital dynamics play a critical role in shaping epidemic patterns on networks.
  • The proposed model provides a more comprehensive framework for studying epidemic spreading with demographic effects.
  • Understanding bistability is essential for predicting and controlling disease outbreaks.