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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Population Growth00:57

Population Growth

29.2K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
29.2K
Modeling with Differential Equations01:25

Modeling with Differential Equations

129
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
129
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

288
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
288
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

467
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
467
Survival Curves01:18

Survival Curves

790
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...
790

You might also read

Related Articles

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

Sort by
Same author

Development of a single-visit protocol for the management of pregnancy of unknown location following in vitro fertilization: a retrospective study.

Human reproduction (Oxford, England)·2024
Same author

Myonuclear alterations associated with exercise are independent of age in humans.

The Journal of physiology·2023
Same author

On the survival of the quantum depletion of a condensate after release from a magnetic trap.

Scientific reports·2022
Same author

Trap frequency measurement with a pulsed atom laser.

Optics express·2022
Same author

Measurement of a helium tune-out frequency: an independent test of quantum electrodynamics.

Science (New York, N.Y.)·2022
Same author

The significance of low first trimester serum progesterone in ongoing early pregnancies presenting as pregnancies of unknown location.

European journal of obstetrics, gynecology, and reproductive biology·2021
Same journal

Reconstructing birth histories using linked household data and the 1911 Census fertility survey.

Population studies·2026
Same journal

Perceptions of infertility: The roles of age, knowledge, and motivated reasoning.

Population studies·2026
Same journal

The impact of education misreporting on education-specific mortality and educational inequalities in mortality: A scenario-based study.

Population studies·2026
Same journal

Social parenting and childlessness in Norway: Associations by sex and economic uncertainty.

Population studies·2026
Same journal

A review and evaluation of internal migration forecasting models.

Population studies·2026
Same journal

The generational health drift: A systematic review of evidence from the British birth cohort studies.

Population studies·2026
See all related articles

Related Experiment Video

Updated: Mar 3, 2026

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

11.2K

A Gompertz model for birth interval analysis.

J A Ross1, Shantha MAdhavan2

  • 1a Center for Population and Family Health , Columbia University , Jakarta , Indonesia presently attached to the National Family Planning Coordinating Board, United Nations Development Programme.

Population Studies
|May 4, 2017
PubMed
Summary
This summary is machine-generated.

Analyzing birth intervals using life tables reveals distinct patterns across different birth orders. A Gompertz model effectively summarizes childbearing pace and progression, aiding future demographic projections.

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

3.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.9K

Related Experiment Videos

Last Updated: Mar 3, 2026

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

11.2K
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

3.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.9K

Area of Science:

  • Demography
  • Sociology
  • Biostatistics

Background:

  • Birth interval analysis is crucial for understanding fertility dynamics.
  • Traditional methods may not fully capture reproductive behaviors, including birth spacing.
  • Life table analysis offers a more comprehensive approach to studying birth intervals.

Purpose of the Study:

  • To analyze birth intervals using a life table approach.
  • To investigate variations in birth patterns across different birth orders.
  • To introduce and evaluate a Gompertz model for summarizing fertility data.

Main Methods:

  • Life table analysis applied to birth intervals.
  • Stratification of analysis by birth order.
  • Application and evaluation of a Gompertz model to cumulative birth proportions.

Main Results:

  • Life table analysis reveals distinct birth interval patterns by parity.
  • The Gompertz model accurately fits the cumulative proportion of births within intervals.
  • The model's parameters provide intuitive interpretations of childbearing pace and progression.

Conclusions:

  • Life table analysis effectively captures nuanced birth interval behaviors.
  • The Gompertz model offers an efficient and interpretable summary of fertility patterns.
  • This approach aids in demographic interpolation and projection.