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

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:
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
Causality in Epidemiology01:21

Causality in Epidemiology

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...
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...

You might also read

Related Articles

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

Sort by
Same author

The impact of introducing meningococcal C/ACWY booster vaccination among adolescents in Germany: a dynamic transmission modelling study.

BMC infectious diseases·2026
Same author

[Reliability of peer review-like dialogue in the German statutory quality assurance program].

Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen·2024
Same author

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts.

BMJ global health·2023
Same author

Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.

PLoS computational biology·2022
Same author

Hospital profiling using Bayesian decision theory.

Biometrics·2022
Same author

Epitweetr: Early warning of public health threats using Twitter data.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2022

Related Experiment Video

Updated: Jun 17, 2026

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

Additive-multiplicative regression models for spatio-temporal epidemics.

Michael Höhle1

  • 1Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstr. 33, 80539 München, Germany. michael.hoehle@stat.uni-muenchen.de

Biometrical Journal. Biometrische Zeitschrift
|December 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an extended stochastic SIR model for infectious disease data analysis. The new model quantifies risk factors for both endemic and epidemic infectious diseases.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 17, 2026

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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Biology

Background:

  • Traditional SIR models are limited in analyzing endemic infectious diseases.
  • Existing models may not fully capture complex risk factors contributing to disease spread.

Purpose of the Study:

  • To extend the stochastic susceptible-infectious-recovered (SIR) model for regression analysis of infectious disease data.
  • To develop a model capable of analyzing both endemic and epidemic components of infectious diseases.
  • To quantify risk factors associated with external infection sources and direct contacts.

Main Methods:

  • Proposed a multivariate counting process with conditional intensities.
  • Incorporated additive epidemic and multiplicative endemic components.
  • Utilized full likelihood inference with parameter restrictions for non-negative intensities.
  • Employed Ogata's modified thinning algorithm for model simulation.

Main Results:

  • The extended SIR model successfully accommodates a regression context for infectious disease data.
  • The model allows for the quantification of risk factors for both endemic and epidemic infectious diseases.
  • Demonstrated the model's application using classical swine fever virus incidence data in Germany (1993-2004).

Conclusions:

  • The proposed extended SIR model provides a flexible framework for analyzing complex infectious disease dynamics.
  • This approach enhances the understanding of risk factors in endemic and epidemic scenarios.
  • The model is valuable for epidemiological research and public health policy.