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

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
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...
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:
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

Qualitative and quantitative screening of the β-glucosidase activity in Saccharomyces cerevisiae and Saccharomyces uvarum strains isolated from refrigerated must.

Letters in applied microbiology·2018
Same author

Fast method for identifying inter- and intra-species Saccharomyces hybrids in extensive genetic improvement programs based on yeast breeding.

Journal of applied microbiology·2015
Same author

A probabilistic method for computing quantitative risk indexes from medical injuries compensation claims.

Methods of information in medicine·2013
Same author

Evolution-based strategy to generate non-genetically modified organisms Saccharomyces cerevisiae strains impaired in sulfate assimilation pathway.

Letters in applied microbiology·2011
Same author

Intrinsic gap and exciton condensation in the nu{T}=1 bilayer system.

Physical review letters·2010
Same author

Characterization and technological properties of Oenococcus oeni strains from wine spontaneous malolactic fermentations: a framework for selection of new starter cultures.

Journal of applied microbiology·2009

Related Experiment Video

Updated: Jul 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Modelling categorical covariates in Bayesian disease mapping by partition structures.

P Giudici1, L Knorr-Held, G Rasser

  • 1Dipartimento di Economia Politica e Metodi Quantitativi, University of Pavia, Via San Felice 5, I-27100 Pavia, Italy. guidici@unipv.it

Statistics in Medicine
|August 29, 2000
PubMed
Summary

This study introduces a Bayesian spatial model to map disease risk using regional case data and risk factors. The enhanced model identifies disease clusters linked to unobserved factors, improving risk assessment accuracy.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

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

Related Experiment Videos

Last Updated: Jul 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

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

Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Mapping disease risk requires analyzing regional case counts and risk factors.
  • Existing spatial models may not fully capture complex risk factor interactions.
  • Identifying disease clusters is crucial for public health interventions.

Purpose of the Study:

  • To develop and implement an advanced Bayesian spatial partition model for disease risk mapping.
  • To incorporate categorical covariate information into spatial models for enhanced risk factor analysis.
  • To detect residual spatial clusters indicating unobserved risk factors.

Main Methods:

  • Bayesian statistical analysis.
  • Extension of spatial partition models with categorical covariates.
  • Reversible jump Markov chain Monte Carlo (RJMCMC) sampling for model implementation.
  • Application to lip cancer incidence data in Scotland.

Main Results:

  • The proposed methodology successfully identified disease clusters.
  • The extended model provided a more nuanced understanding of risk factor contributions compared to purely spatial models.
  • Detection of residual variation highlighted potential unobserved risk factors influencing disease distribution.

Conclusions:

  • The enhanced Bayesian spatial model offers a robust framework for disease risk mapping.
  • Incorporating covariates improves the detection of localized disease patterns.
  • This approach aids in identifying areas needing targeted public health strategies.