Jove
Visualize
Contact Us

Related Concept Videos

Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
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:
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...
Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.However, realistic environmental conditions limit the number of...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Local Influenza Forecasts Outperform State-Level Forecasts in the United States.

medRxiv : the preprint server for health sciences·2026
Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

medRxiv : the preprint server for health sciences·2026
Same author

DiagnoDating: diagnostics for dated phylogenies in microbial population genetics.

Molecular biology and evolution·2026
Same author

Estimated impact of 2022-2023 influenza vaccines on annual hospital burden in the United States.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Improving outbreak forecasts through model augmentation.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Modeling Influenza Antiviral Strategies: Reducing Burden and Preventing Resistance.

The Journal of infectious diseases·2025
Same journal

RNA-ligand complexes and the attenuation of neutral confinement in the evolution of RNA secondary structures.

Journal of the Royal Society, Interface·2026
Same journal

Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.

Journal of the Royal Society, Interface·2026
Same journal

Thermal stress disrupts symbiotic fluid dynamics in bobtail squid.

Journal of the Royal Society, Interface·2026
Same journal

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same journal

Slow modulation of the contraction patterns in Physarum polycephalum.

Journal of the Royal Society, Interface·2026
Same journal

Moo-ving mountains: grazing agents drive terracette formation on steep hillslopes.

Journal of the Royal Society, Interface·2026
See all related articles
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 Experiment Video

Updated: Jul 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Epidemic thresholds in dynamic contact networks.

Erik Volz1, Lauren Ancel Meyers

  • 1Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA. erik.volz@gmail.com

Journal of the Royal Society, Interface
|July 31, 2008
PubMed
Summary
This summary is machine-generated.

The basic reproduction number (R0) for infectious diseases depends on social factors like contact patterns and network dynamics, not just transmission and recovery rates. Understanding these social parameters is crucial for accurate epidemic threshold calculations in dynamic networks.

More Related Videos

Contact-Free Co-Culture Model for the Study of Innate Immune Cell Activation During Respiratory Virus Infection
07:36

Contact-Free Co-Culture Model for the Study of Innate Immune Cell Activation During Respiratory Virus Infection

Published on: February 28, 2021

Related Experiment Videos

Last Updated: Jul 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Contact-Free Co-Culture Model for the Study of Innate Immune Cell Activation During Respiratory Virus Infection
07:36

Contact-Free Co-Culture Model for the Study of Innate Immune Cell Activation During Respiratory Virus Infection

Published on: February 28, 2021

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • The basic reproduction number (R0) is critical for understanding infectious disease spread.
  • Calculating epidemic thresholds in complex models is challenging.
  • Previous models often used static network approximations.

Purpose of the Study:

  • To derive the reproductive ratio and epidemic thresholds for SIR epidemics in dynamic random networks.
  • To investigate the influence of social parameters on R0 and epidemic thresholds.
  • To highlight the limitations of static network approximations.

Main Methods:

  • Derivation of R0 and epidemic thresholds for SIR models on dynamic random networks.
  • Analysis of the impact of degree distribution (contact heterogeneity) and mixing parameter (contact rates).

Main Results:

  • R0 is influenced by transmission rate, recovery rate, degree distribution, and mixing parameter.
  • Social mixing parameters significantly alter the epidemiological landscape.
  • Static network approximations can be inadequate for dynamic networks.

Conclusions:

  • Epidemic thresholds are not solely determined by transmission and recovery rates.
  • Social contact patterns and network dynamics are essential components for accurate epidemic modeling.
  • Dynamic network models provide a more realistic approach to understanding disease spread.