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

Poisson Probability Distribution01:09

Poisson Probability Distribution

10.5K
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...
10.5K
Poisson's Ratio01:23

Poisson's Ratio

677
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
677
Distribution and Dispersion00:54

Distribution and Dispersion

23.2K
To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
23.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.5K
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...
4.5K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.7K
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.
3.7K
Probability Distributions01:32

Probability Distributions

10.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Global mobility flows and COVID-19 spread in Europe during the emergency phase: Insights from Meta-Facebook data.

Spatial and spatio-temporal epidemiology·2026
Same author

Informing quarantine policy for measles control in primary school and daycare settings: insights from a simulation study, Flanders, Belgium.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026
Same author

CD155 links tumor immunotype to epithelial-directed precision therapy beyond checkpoint inhibition in cervical cancer.

Journal for immunotherapy of cancer·2026
Same author

Diagnostic testing intensity for Legionnaires' disease: Spatio-temporal assessment and its effect on surveillance case reporting, Denmark, 2014-2022.

PloS one·2026
Same author

Exploring age and gender sensitivity to high-temperature days: impacts on respiratory, cardiovascular, and all-cause mortality in summer in Flanders.

Archives of public health = Archives belges de sante publique·2026
Same author

Assessing the impact of neighborhood structures in Bayesian disease mapping.

Journal of applied statistics·2026

Related Experiment Video

Updated: Oct 29, 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

Quantifying superspreading for COVID-19 using Poisson mixture distributions.

Cécile Kremer1, Andrea Torneri2, Sien Boesmans3

  • 1Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium. cecile.kremer@uhasselt.be.

Scientific Reports
|July 9, 2021
PubMed
Summary
This summary is machine-generated.

Understanding infectious disease transmission is key. This study explores different statistical models for secondary cases in COVID-19, finding the Poisson-lognormal distribution often better describes transmission heterogeneity than the negative binomial model.

More Related Videos

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
06:53

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Published on: June 8, 2019

8.9K
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

358

Related Experiment Videos

Last Updated: Oct 29, 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
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
06:53

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Published on: June 8, 2019

8.9K
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

358

Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Accurate estimation of secondary cases is crucial for infectious disease control.
  • Individual variation in transmission (heterogeneity) is significant in diseases like COVID-19.
  • The negative binomial distribution is commonly used but may not always fit transmission data well.

Purpose of the Study:

  • To evaluate alternative offspring distributions for modeling transmission heterogeneity.
  • To assess potential bias in estimating mean and variance when the assumed distribution differs from the true data-generating distribution.
  • To analyze COVID-19 transmission data from Hong Kong, India, and Rwanda.

Main Methods:

  • Proposed and compared three alternative offspring distributions beyond the negative binomial.
  • Conducted simulation studies to assess bias in parameter estimates.
  • Analyzed real-world COVID-19 transmission data from three countries.

Main Results:

  • Variance estimates can be significantly biased with substantial heterogeneity if the wrong distribution is used.
  • Selecting the most accurate distribution is critical for reliable inference.
  • The Poisson-lognormal distribution provided a better fit for COVID-19 transmission data from Hong Kong and Rwanda compared to the negative binomial.

Conclusions:

  • The choice of offspring distribution significantly impacts the accuracy of transmission parameter estimation.
  • The Poisson-lognormal distribution is a valuable alternative for modeling heterogeneous transmission, particularly for COVID-19.
  • Further research should focus on robust methods for selecting appropriate distributions in infectious disease modeling.