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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

216
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
216
Censoring Survival Data01:09

Censoring Survival Data

494
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
494
Causality in Epidemiology01:21

Causality in Epidemiology

1.4K
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...
1.4K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

459
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:
459
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

219
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
219
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

854
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:
854

You might also read

Related Articles

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

Sort by
Same author

Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.

PLoS computational biology·2026
Same author

Model reduction and analysis: A case study of a malaria control model.

Journal of theoretical biology·2026
Same author

Spatio-temporal agent-based modelling of malaria.

Epidemics·2026
Same author

Border quarantine, vaccination and public health measures to mitigate the impact of COVID-19 importations in Australia: a modelling study.

Journal of the Royal Society, Interface·2026
Same author

Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.

PLoS computational biology·2025
Same author

Model reduction and analysis: A case study of a malaria control model.

medRxiv : the preprint server for health sciences·2025
Same journal

The stability and bifurcations of ecosystems within resource constraints - Dedicated to Professor Shigui Ruan on the occasion of his 60th birthday.

Mathematical biosciences·2026
Same journal

The hydra and hormetic effects in a single discrete-time overcompensation model.

Mathematical biosciences·2026
Same journal

Seasonal impacts on brucellosis transmission mediated by live sheep supply-demand dynamics.

Mathematical biosciences·2026
Same journal

Optimal controls and cost-effectiveness analysis on the transmission dynamics of early blight disease in tomatoes.

Mathematical biosciences·2026
Same journal

Temperature-dependent dynamics and allee effect thresholds mediate fourfold cusp stability in biological control of invasive vectors.

Mathematical biosciences·2026
Same journal

Dynamics of a stochastic tumor-immune interaction system with an Ornstein-Uhlenbeck process.

Mathematical biosciences·2026
See all related articles

Related Experiment Video

Updated: Jan 6, 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

10.9K

Bayesian model discrimination for partially-observed epidemic models.

Camelia R Walker1, Andrew J Black1, Joshua V Ross1

  • 1School of Mathematical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; ACEMS, School of Mathematical Sciences, University of Adelaide, Adelaide, SA 5005, Australia.

Mathematical Biosciences
|October 8, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient Bayesian method for selecting continuous-time Markov chain models, crucial for epidemiological analysis. The approach accurately identifies infectious period shapes and symptom timing using outbreak data.

Keywords:
Epidemic modellingImportance samplingMarkov chainModel selectionParticle filter

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Related Experiment Videos

Last Updated: Jan 6, 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

10.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Bayesian model selection is vital for complex systems.
  • Continuous-time Markov chain (CTMC) models are widely used in epidemiology.
  • Intractable likelihood functions pose challenges for model selection.

Purpose of the Study:

  • To develop an efficient Bayesian method for model selection in CTMC models.
  • To apply this method to identify infectious period distribution shapes.
  • To determine symptom onset timing relative to infectiousness.

Main Methods:

  • Utilized a particle filter with a novel importance sampling algorithm for partially-observed CTMCs.
  • Employed a second importance sampling method for unbiased model evidence estimation.
  • Incorporated precision estimates for stopping criteria.

Main Results:

  • Successfully identified the correct model for infectious period distribution shape in most cases.
  • Accurately determined symptom onset timing relative to infectiousness.
  • Demonstrated effectiveness on simulated data from multiple small population outbreaks.

Conclusions:

  • The proposed Bayesian method is efficient and generalizable for CTMC model selection.
  • The method accurately resolves key epidemiological questions using symptom onset data.
  • Applicable to various biological and epidemiological systems with challenging likelihoods.