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

Binomial Probability Distribution01:15

Binomial Probability Distribution

10.8K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.8K
McNemar's Test01:23

McNemar's Test

249
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
249
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.1K
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...
8.1K
Probability Histograms01:17

Probability Histograms

11.6K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.6K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
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...
4.1K

You might also read

Related Articles

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

Sort by
Same author

A spatial scan statistic for group testing data.

Spatial and spatio-temporal epidemiology·2026
Same author

Deforestation's impact on Brazilian Culex mosquitoes and arboviral spillover risk.

Acta tropica·2026
Same author

Assessing Simultaneous Infection with Multiple Pathogens via Group Testing with Imperfect Multiplex Assays.

Journal of agricultural, biological, and environmental statistics·2026
Same author

Granular insights: A wastewater-based machine learning approach for localized COVID-19 hospitalization forecasting.

Epidemics·2026
Same author

Impact of Medicaid Enrollment Timing on Tumor Stage at Diagnosis and Survival in Breast, Colorectal, and Lung Cancer.

Healthcare (Basel, Switzerland)·2026
Same author

Tick to table: a scoping review on the global impact of alpha-gal syndrome.

Journal of medical entomology·2026
Same journal

Bandwidth of gamma-distribution-shaped functions via Lambert W function.

Statistics & probability letters·2026
Same journal

Directional replicability: When can the factor of two be omitted.

Statistics & probability letters·2026
Same journal

Approximating win-loss probabilities based on the overall and event-free survival functions.

Statistics & probability letters·2025
Same journal

On exact Bayesian credible sets for discrete parameters.

Statistics & probability letters·2025
Same journal

On critical points of Gaussian random fields under diffeomorphic transformations.

Statistics & probability letters·2024
Same journal

Universally Consistent K-Sample Tests via Dependence Measures.

Statistics & probability letters·2024
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

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

2.5K

A Bayesian Spatial Scan Statistic for Multinomial Data.

Stella Self1, Melissa Nolan1

  • 1Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, South Carolina, United States of America.

Statistics & Probability Letters
|January 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian spatial scan statistic for analyzing multinomial data. The method effectively detects clusters of SARS-CoV-2 infection and immunity in South Carolina.

More Related Videos

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

15.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Related Experiment Videos

Last Updated: Jul 4, 2025

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

2.5K
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

15.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Spatial scan statistics are essential for identifying disease clusters.
  • Existing methods may have limitations with multinomial disease data.
  • Detecting geographic patterns of infectious diseases is crucial for public health.

Purpose of the Study:

  • To develop and validate a novel Bayesian spatial scan statistic for multinomial data.
  • To apply the new method for identifying clusters of SARS-CoV-2 infection and immunity.
  • To enhance spatial epidemiological surveillance capabilities.

Main Methods:

  • Development of a Bayesian spatial scan statistic tailored for multinomial distributions.
  • Validation through a comprehensive simulation study.
  • Application to real-world SARS-CoV-2 infection/immunity data from South Carolina.

Main Results:

  • The Bayesian spatial scan statistic demonstrated robust performance in simulation studies.
  • Significant spatial clusters of SARS-CoV-2 infection/immunity were identified in South Carolina.
  • The method provides a powerful tool for disease cluster detection.

Conclusions:

  • The proposed Bayesian spatial scan statistic is a valuable advancement for analyzing multinomial spatial data.
  • This approach can improve the detection and understanding of disease clustering.
  • Effective for public health surveillance of infectious diseases like SARS-CoV-2.