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

82
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...
82
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.2K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.2K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

389
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:
389
Causality in Epidemiology01:21

Causality in Epidemiology

451
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...
451
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

139
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:
139
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.0K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
3.0K

You might also read

Related Articles

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

Sort by
Same author

LI-RADS v2018 versus KLCA-NCC v2022: comparison of probability-based HCC categories.

European radiology·2026
Same author

Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.

Journal of the Korean Society of Radiology·2026
Same author

Protective Effectiveness of Sars-Cov-2 Infection Risk Among Hybrid, Vaccine, and Infection-induced Immunity Against the Omicron Variant, K-Serosmart.

Open forum infectious diseases·2026
Same author

Multicentre prospective trial of abbreviated MRI using gadoxetic acid versus CT for detection of late recurrent HCC (AMRICT): study protocol.

BMJ open·2026
Same author

Improving prediction of ypT0-1N0 response in rectal cancer: the added value of gross tumor type to magnetic resonance tumor regression grade after chemoradiotherapy in a retrospective cohort study.

Annals of surgical treatment and research·2026
Same author

Ultrasound Imaging Features Associated With Neoplastic Gallbladder Polyps: A Systematic Review and Meta-Analysis.

Korean journal of radiology·2026

Related Experiment Video

Updated: Jul 14, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.2K

A spatio-temporal Dirichlet process mixture model for coronavirus disease-19.

Jaewoo Park1,2, Seorim Yi2, Won Chang3

  • 1Department of Applied Statistics, Yonsei University, Seoul, South Korea.

Statistics in Medicine
|October 9, 2023
PubMed
Summary

This study introduces a new model to track COVID-19 spread in cities using spatial data. It identifies disease clusters and landmark impacts to improve public health warnings and understand epidemic dynamics.

Keywords:
Bayesian hierarchical modelDirichlet process Gaussian mixtureInfectious diseasesMarkov chain Monte Carlospatio-temporal point patterns

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

206
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

10.7K

Related Experiment Videos

Last Updated: Jul 14, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.2K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

206
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

10.7K

Area of Science:

  • Epidemiology
  • Computational Statistics
  • Public Health

Background:

  • Understanding spatio-temporal patterns of COVID-19 is crucial for effective public health interventions.
  • Spatially referenced data offers deeper insights into disease spread mechanisms than aggregated counts.

Purpose of the Study:

  • To propose a spatio-temporal Dirichlet process mixture model for analyzing COVID-19 cases in urban settings.
  • To detect epidemic cluster centers, estimate their space-time range for warning systems, and assess landmark influences.

Main Methods:

  • Developed a spatio-temporal Dirichlet process mixture model.
  • Employed a sequential approach using posterior distributions for temporal dynamics.
  • Implemented model assessment through comparison with theoretical densities and goodness-of-fit analysis.

Main Results:

  • The model effectively detects unobserved epidemic cluster centers.
  • It estimates the space-time range of clusters, aiding in warning system development.
  • The model quantifies the impact of urban landmarks on disease spread from different time points.

Conclusions:

  • The proposed model provides a computationally efficient method to analyze spatio-temporal disease patterns.
  • It offers an intuitive explanation of disease spread sources influenced by urban landmarks.
  • The approach enhances understanding of COVID-19 dynamics for targeted public health strategies.