Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Steps in Outbreak Investigation

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:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

You might also read

Related Articles

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

Sort by

Same author

Global mobility flows and COVID-19 spread in Europe during the emergency phase: Insights from Meta-Facebook data.

Spatial and spatio-temporal epidemiology·2026

Same author

Informing quarantine policy for measles control in primary school and daycare settings: insights from a simulation study, Flanders, Belgium.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026

Same author

CD155 links tumor immunotype to epithelial-directed precision therapy beyond checkpoint inhibition in cervical cancer.

Journal for immunotherapy of cancer·2026

Same author

Diagnostic testing intensity for Legionnaires' disease: Spatio-temporal assessment and its effect on surveillance case reporting, Denmark, 2014-2022.

PloS one·2026

Same author

Exploring age and gender sensitivity to high-temperature days: impacts on respiratory, cardiovascular, and all-cause mortality in summer in Flanders.

Archives of public health = Archives belges de sante publique·2026

Same author

Assessing the impact of neighborhood structures in Bayesian disease mapping.

Journal of applied statistics·2026

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

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

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

Published on: July 22, 2016

Flexible Bayesian estimation of incubation times.

Oswaldo Gressani¹, Andrea Torneri¹, Niel Hens^1,2

¹Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute Hasselt University, Hasselt BE-3500, Belgium.

American Journal of Epidemiology

|July 11, 2024

Summary

Estimating incubation periods for infectious diseases is crucial for public health. This study introduces a new Bayesian method using Laplacian-P-splines for more accurate incubation period distribution estimation, even with coarse data.

Keywords:

Bayesian P-splines Laplace approximation MCMC incubation period

More Related Videos

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

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

Published on: December 9, 2015

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Related Experiment Videos

Last Updated: Jun 21, 2025

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

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

Published on: July 22, 2016

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

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

Published on: December 9, 2015

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Area of Science:

Epidemiology
Biostatistics
Computational Biology

Background:

Accurate estimation of incubation periods is vital for controlling infectious disease outbreaks.
Current methods face challenges due to coarse data on exposure and symptom onset times.

Purpose of the Study:

To develop a novel Bayesian methodology for semiparametric estimation of incubation period distributions.
To address the challenges posed by coarse epidemiological data.

Main Methods:

Developed a Bayesian approach utilizing Laplacian-P-splines and a Langevinized Gibbs sampler.
Employed a finite mixture density smoother and moment matching for distribution selection.
Integrated the method into the extended EpiLPS package.

Main Results:

The new methodology demonstrated encouraging results across various simulation scenarios with different data coarseness levels.
Applied to real-world data for COVID-19, MERS, and Mpox, yielding results consistent with existing studies.

Conclusions:

The proposed flexible Bayesian approach offers a valuable alternative to traditional parametric methods for incubation period estimation.
This method enhances the ability to model incubation distributions accurately, aiding public health strategies.