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

Using network models to approximate spatial point-process models.

Chris T Bauch1, Alison P Galvani

  • 1Department of Mathematics and Statistics, McMaster University, Hamilton, Ont., Canada L8S 4K1. bauch@math.mcmaster.ca

Mathematical Biosciences
|June 6, 2003
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

Implications of regional variations in climate change vulnerability and mitigation behaviour for social-climate dynamics.

Nature communications·2026
Same author

When simple is enough: Binary models capture social complexity in coupled human-environment systems.

Mathematical biosciences·2026
Same author

The health and economic repercussions of declining MMR coverage in the United States.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Prejudice and objectivity, not social influence, determine long-term outcomes in coupled opinion-environment dynamics in polarized populations.

Journal of theoretical biology·2026
Same author

Homophily in a multi-group vaccination game and its impact on disease dynamics.

Proceedings. Biological sciences·2026
Same author

Estimating US savings on outpatient prescription pharmaceuticals from international reference pricing.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Intelligent machine learning solutions with Bayesian regularization backpropagation adaptive networks for differential systems of Maize streak virus diseases.

Mathematical biosciences·2026
Same journal

The stability and bifurcations of ecosystems within resource constraints - Dedicated to Professor Shigui Ruan on the occasion of his 60th birthday.

Mathematical biosciences·2026
Same journal

The hydra and hormetic effects in a single discrete-time overcompensation model.

Mathematical biosciences·2026
Same journal

Seasonal impacts on brucellosis transmission mediated by live sheep supply-demand dynamics.

Mathematical biosciences·2026
Same journal

Optimal controls and cost-effectiveness analysis on the transmission dynamics of early blight disease in tomatoes.

Mathematical biosciences·2026
Same journal

Temperature-dependent dynamics and allee effect thresholds mediate fourfold cusp stability in biological control of invasive vectors.

Mathematical biosciences·2026
See all related articles

This study introduces a moment closure technique to create realistic network models for spatial epidemics. This method accurately parameterizes network models using continuous population data, improving epidemic spread predictions.

Area of Science:

  • Ecology
  • Epidemiology
  • Mathematical Modeling

Background:

  • Spatial effects are crucial in ecological and epidemiological systems.
  • Fixed-edge network models simplify spatial processes but have limitations in scale, structure, and parameterization.
  • Spatial point-process models offer greater realism but are analytically challenging.

Purpose of the Study:

  • To develop a moment closure technique for network models.
  • To predict epidemic spread using a continuous spatial point-process model.
  • To systematically parameterize network models with empirical data from continuous populations.

Main Methods:

  • Developed a moment closure technique.
  • Defined a fixed-edge network model from a continuous spatial point-process epidemic model.

Related Experiment Videos

  • Utilized data from dispersal kernels for parameterization.

    • Main Results:

    • The moment closure technique successfully links network and point-process models.
    • The developed network model accurately predicts epidemic prevalence and spread rate.
    • The approach enables systematic parameterization of network models.

    Conclusions:

    • Network models can be realistic representations of spatial processes.
    • The moment closure technique provides a bridge between complex point-process models and practical network models.
    • This method enhances the utility of network models in ecological and epidemiological studies.