Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Infection dynamics on scale-free networks.

R M May¹, A L Lloyd

¹Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|December 12, 2001

Summary

Infection dynamics on scale-free networks lack epidemic thresholds due to varied connectivity. Finite populations, however, do show thresholds, preventing spread with low transmission rates.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

How population control of pests is modulated by density dependence: The perspective of genetic biocontrol.

bioRxiv : the preprint server for biology·2024

Same author

Seven challenges for model-driven data collection in experimental and observational studies.

Epidemics·2015

Same author

The wMel Wolbachia strain blocks dengue and invades caged Aedes aegypti populations.

Nature·2011

Same author

How much do we know about the current extinction rate?

Trends in ecology & evolution·2011

Same author

Chaos and forecasting.

Trends in ecology & evolution·2011

Same author

Palaeoecology and ecology.

Trends in ecology & evolution·2011

Area of Science:

Epidemiology
Network Science
Statistical Physics

Background:

Scale-free networks exhibit heterogeneous node connectivity.
Traditional epidemiological models often assume homogeneous mixing and exhibit threshold behavior.

Purpose of the Study:

To analyze infection processes on scale-free networks.
To derive analytic expressions for epidemic final size and infection probability.
To investigate the impact of network structure on epidemic dynamics.

Main Methods:

Derivation of analytic expressions for epidemic final size in infinite populations.
Analysis of infection probability dependence on node connectivity.
Investigation of finite population size effects on epidemic thresholds.

Main Results:

Epidemic processes on infinite scale-free networks do not exhibit threshold behavior, irrespective of immunity.
Heterogeneous connectivity distribution in scale-free networks is responsible for the absence of thresholds.
Finite population sizes introduce threshold effects, limiting disease spread at low transmission probabilities.

Conclusions:

The structure of scale-free networks significantly alters epidemic dynamics compared to traditional models.
Understanding network connectivity is crucial for predicting epidemic behavior.
Finite population size is a critical factor in the emergence of epidemic thresholds.

Related Experiment Videos