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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

241
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:
241
Causality in Epidemiology01:21

Causality in Epidemiology

1.0K
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...
1.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

92
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...
92
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

136
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...
136
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

586
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:
586
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

278
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
278

You might also read

Related Articles

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

Sort by
Same author

Excess mortality in Europe estimated by EuroMOMO during the COVID-19 pandemic and previous influenza seasons.

Nature communications·2026
Same author

Modelling in-hospital length of stay: A comparison of linear and ensemble models for competing risk analysis.

PloS one·2025
Same author

Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm.

Statistics in medicine·2023
Same author

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques.

PloS one·2023
Same author

Analysis of Prior Aspirin Treatment on in-Hospital Outcome of Geriatric COVID-19 Infected Patients.

Medicina (Kaunas, Lithuania)·2022
Same author

Multidimensional adaptive P-splines with application to neurons' activity studies.

Biometrics·2022

Related Experiment Video

Updated: Sep 30, 2025

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

10.8K

Modeling latent spatio-temporal disease incidence using penalized composite link models.

Dae-Jin Lee1, María Durbán2, Diego Ayma3

  • 1BCAM - Basque Center for Applied Mathematics, Bilbao, Bizkaia, Spain.

Plos One
|March 10, 2022
PubMed
Summary

This study introduces a new statistical model to reveal detailed patterns in aggregated epidemiological data. The penalized composite link model helps uncover hidden trends in spatio-temporal health information.

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
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

15.8K

Related Experiment Videos

Last Updated: Sep 30, 2025

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

10.8K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
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

15.8K

Area of Science:

  • Epidemiology
  • Statistical Modeling
  • Public Health

Background:

  • Epidemiological data are often aggregated to protect privacy or for summarization, obscuring underlying detailed patterns.
  • Researchers and public health officials may miss crucial insights due to coarse spatio-temporal resolutions.

Purpose of the Study:

  • To develop and apply a novel statistical approach for estimating underlying trends in data aggregated in both space and time.
  • To recover fine-grained spatio-temporal patterns from coarse-resolution epidemiological data.

Main Methods:

  • Utilized the penalized composite link model with spatio-temporal P-splines methodology.
  • Employed a generalized linear mixed model framework for model estimation.
  • Implemented advanced algorithms to manage computationally intensive calculations.

Main Results:

  • Successfully estimated underlying trends in spatio-temporally aggregated data.
  • The model effectively revealed detailed patterns previously obscured by data aggregation.
  • Applied the methodology to analyze data from a major Q-fever outbreak in the Netherlands.

Conclusions:

  • The proposed penalized composite link model combined with spatio-temporal P-splines is effective for uncovering hidden trends in aggregated epidemiological data.
  • This approach enhances the understanding of disease dynamics by revealing fine-scale spatio-temporal patterns.
  • The methodology offers valuable tools for researchers and public health officials investigating disease outbreaks.