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

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...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
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...
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...
Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness, disability,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Impact of red blood cell transfusion and transfusion strategies on clinical outcomes in extremely low gestational age neonates.

Frontiers in pediatrics·2026
Same author

Time evolution of infection state correlation of nodes in an SIR model on a random contact network.

Mathematical biosciences·2026
Same author

Integrating Transcriptomics and Gut Microbiota Analysis Reveals Adaptive Mechanisms of Alkaline Stress on the Molting and Intestinal Immune Responses in Pacific White Shrimp, <i>Litopenaeus vannamei</i>.

Life (Basel, Switzerland)·2026
Same author

Mechanistic modelling of highly pathogenic avian influenza: A scoping review revealing critical gaps in cross-species transmission models.

PloS one·2026
Same author

Retrospective analysis of age-specific non-pharmaceutical interventions on wild-type SARS-CoV-2 in Canada.

BMC public health·2026
Same author

Unraveling the phototransformation of 2,4,6-tribromophenol: Aqueous mechanisms involving ·OH and the emergence of hydroxylated PBDEs.

Water research·2026
Same journal

A perception-memory PDE framework for seasonal migration dynamics.

Journal of mathematical biology·2026
Same journal

Dynamic resource allocation in eukaryotic Resource Balance Analysis.

Journal of mathematical biology·2026
Same journal

Discrete-time exploitative competition model of different stage-specific predators.

Journal of mathematical biology·2026
Same journal

Spatiotemporal SEIQR Epidemic Modeling with Optimal Control for Vaccination, Treatment, and Social Measures.

Journal of mathematical biology·2026
Same journal

Phenotypic plasticity trade-offs in an age-structured model of bacterial growth under stress.

Journal of mathematical biology·2026
Same journal

Intraspecific interactions facilitate mutualism across multilayer networks under weak selection.

Journal of mathematical biology·2026
See all related articles

Related Experiment Videos

Effective degree household network disease model.

Junling Ma1, P van den Driessche, Frederick H Willeboordse

  • 1Department of Mathematics and Statistics, University of Victoria, Victoria, BC, V8W 3R4, Canada. jma@math.uvic.ca

Journal of Mathematical Biology
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an ordinary differential equation (ODE) model for disease spread, incorporating network topology and household structures. The model reveals how household clustering can significantly complicate and alter disease dynamics.

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Network Science

Background:

  • Understanding disease transmission dynamics is crucial for public health interventions.
  • Existing models often simplify population structures, potentially missing key transmission factors.
  • Household structures represent significant contact networks within populations.

Purpose of the Study:

  • To develop and analyze an ordinary differential equation (ODE) epidemiological model.
  • To incorporate both network topology and household structures into disease spread modeling.
  • To investigate the impact of household clustering on epidemic dynamics.

Main Methods:

  • Formulation of an ODE model for disease spread with immunity.
  • Integration of network topology and household structures into the model's contact network.
  • Analytical derivation of disease threshold parameters.
  • Comparison of model-derived epidemic curves with stochastic simulations.

Main Results:

  • The ODE model demonstrates excellent agreement with stochastic simulations.
  • Analytical expressions for threshold parameters were derived and interpreted.
  • Inclusion of households can accelerate or decelerate disease spread.
  • The effect of households depends on the variance of the inter-household degree distribution.

Conclusions:

  • Household structures introduce complex dynamics into disease spread.
  • Network topology and household clustering are critical factors in epidemiological modeling.
  • The developed ODE model provides a valuable tool for understanding disease dynamics in structured populations.