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

Generation Time01:22

Generation Time

Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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:
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

You might also read

Related Articles

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

Sort by
Same author

Relationship between Dermatology Life Quality Index Scores and EQ-5D-5L Utility Values in Adults with Atopic Dermatitis: Development of a Swedish Mapping Model.

Acta dermato-venereologica·2026
Same author

Dupilumab-Induced Psoriasis in an Adolescent With Severe Atopic Dermatitis: Successful Management With Abrocitinib.

Pediatric dermatology·2026
Same author

Position Statement of the EADV Task Forces With External Experts on Quality of Life Measurement in Hidradenitis Suppurativa: An Update.

International journal of dermatology·2026
Same author

Life Events Preceding Alopecia Areata Onset-a Descriptive Retrospective Cohort Study Focusing on Strain.

Clinical, cosmetic and investigational dermatology·2026
Same author

Importance of alexithymia on anxiety and depression in alopecia areata: a cohort study.

Skin health and disease·2026
Same author

Modelling Impact of Different Varicella Immunisation Strategies Upon Introduction in the Swedish National Programme.

Acta paediatrica (Oslo, Norway : 1992)·2026

Related Experiment Video

Updated: Jun 19, 2026

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

Some model based considerations on observing generation times for communicable diseases.

Gianpaolo Scalia Tomba1, Ake Svensson, Tommi Asikainen

  • 1Dept. of Mathematics, University of Rome Tor Vergata, Italy. scaliato@mat.uniroma2.it

Mathematical Biosciences
|October 27, 2009
PubMed
Summary

Understanding infectious disease generation time is crucial for tracking spread. This study develops a mathematical model to accurately estimate generation time distributions, improving epidemic modeling and public health insights.

More Related Videos

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System
16:41

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System

Published on: November 5, 2013

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

Related Experiment Videos

Last Updated: Jun 19, 2026

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System
16:41

Detecting, Visualizing and Quantitating the Generation of Reactive Oxygen Species in an Amoeba Model System

Published on: November 5, 2013

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Biostatistics

Background:

  • The generation time of an infectious disease is defined as the interval between infection of a primary case and infection of a secondary case.
  • This concept, analogous to demographic generation gap, is vital for understanding disease transmission dynamics.
  • While traditionally applied to diseases like measles, its extension to influenza and other less discernible transmission patterns presents statistical challenges.

Purpose of the Study:

  • To develop a mathematical model for estimating the generation time distribution of infectious diseases.
  • To investigate how various transmission scenarios (isolation, households, outbreaks) affect the statistical properties of observed generation times.
  • To identify factors influencing sampling distributions that are often overlooked in current literature.

Main Methods:

  • Formulation of statistical questions within a basic mathematical model of infection spread.
  • Derivation of theoretical properties of observations under different epidemiological settings.
  • Analysis of sampling distributions and their dependence on various factors.

Main Results:

  • The sampling distribution of generation time observations is influenced by multiple factors not commonly considered.
  • These factors must be accounted for to ensure unbiased inference about the true generation time distribution.
  • The model provides a framework for analyzing generation time in diverse epidemiological contexts.

Conclusions:

  • Accurate estimation of infectious disease generation time requires consideration of factors beyond simple time intervals.
  • The developed mathematical framework offers improved methods for statistical inference in epidemic modeling.
  • Findings have significant implications for public health strategies and the analysis of infectious disease outbreaks.