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 Experiment Videos

Spatial heterogeneity in epidemic models

A L Lloyd1, R M May

  • 1Department of Zoology, University of Oxford, U.K. ALUN.LLOYD@ZOO.OX.AC.UK

Journal of Theoretical Biology
|March 7, 1996
PubMed
Summary
This summary is machine-generated.

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
Same journal

The male-biased sex ratio in humans and its role in the transition from promiscuity to pair bonding.

Journal of theoretical biology·2026
Same journal

Quantifying the counter-intuitive effects of vaccination by coupling the transmission dynamics of COVID-19 and the evolution of human behaviors.

Journal of theoretical biology·2026
Same journal

An integrative model of FGF2-induced signaling and muscle cell proliferation.

Journal of theoretical biology·2026
Same journal

A hybrid reaction-diffusion and mechanical stimulus model for mandibular bone remodeling under chewing and vibratory loading.

Journal of theoretical biology·2026
Same journal

Integrated tick management strategies in fragmented peridomestic environments.

Journal of theoretical biology·2026
Same journal

Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and their selection coefficients in an evolving population.

Journal of theoretical biology·2026
See all related articles

Spatial heterogeneity in childhood disease epidemics can lead to synchronization, but seasonal forcing can maintain regional differences. This impacts disease persistence and dynamics, even with weak coupling between populations.

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Disease Dynamics

Background:

  • Spatial heterogeneity is crucial for epidemic persistence, allowing regional asynchrony to maintain diseases globally.
  • Childhood disease epidemics are influenced by metapopulation dynamics and regional interactions.

Purpose of the Study:

  • To analyze a multi-patch metapopulation model for spatial heterogeneity in childhood disease epidemics.
  • To examine the conditions under which different regional populations (patches) become synchronized or desynchronized.

Main Methods:

  • Analysis of a simple multi-patch (metapopulation) model for epidemic dynamics.
  • Investigation of both deterministic and stochastic models, with and without seasonal forcing.
  • Examination of coupling strength between patches and its effect on synchronization.

Related Experiment Videos

Main Results:

  • Non-seasonal deterministic models show synchronization for moderate to strong coupling.
  • Stochastic models also exhibit synchronization, requiring slightly stronger coupling to overcome random effects.
  • Seasonal forcing in deterministic models can maintain phase differences, leading to complex dynamics and chaotic solutions with realistic minimum infective numbers.

Conclusions:

  • Synchronization is common in spatially heterogeneous epidemic models, particularly without seasonal forcing.
  • Seasonal forcing can disrupt synchronization, promoting complex dynamics and potentially more realistic epidemic behavior.
  • Understanding spatial and seasonal factors is key to predicting and managing childhood disease epidemics.