Jove
Visualize
Contact Us

Related Experiment Videos

Predicting epidemics on directed contact networks.

Lauren Ancel Meyers1, M E J Newman, Babak Pourbohloul

  • 1Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, 1 University Station C0930, Austin, TX 78712, USA. laurenmeyers@mail.utexas.edu

Journal of Theoretical Biology
|November 23, 2005
PubMed
Summary

This study introduces a new mathematical framework for disease spread modeling in semi-directed contact networks. It reveals that epidemic probabilities and infection rates can differ in these networks, unlike previous assumptions.

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

Adaptive multi-model ensembles for improved epidemic projections and decision support.

medRxiv : the preprint server for health sciences·2026
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

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
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

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Network Science

Background:

  • Contact network epidemiology models infectious disease spread based on community contact patterns.
  • Disease transmission can be asymmetric, with differing probabilities between individuals.
  • Existing models often assume symmetric transmission, which may not reflect real-world scenarios like sexually transmitted diseases or healthcare-associated infections.

Purpose of the Study:

  • To develop a mathematical framework for predicting disease transmission in semi-directed contact networks.
  • To investigate how directed and undirected contacts influence epidemic dynamics.
  • To assess the accuracy of these methods in predicting healthcare worker (HCW) vulnerability and containment strategies.

Main Methods:

Related Experiment Videos

  • Utilized methods from percolation theory.
  • Developed a framework for analyzing semi-directed contact networks, incorporating both symmetric and asymmetric transmission.
  • Modeled disease spread considering directed and undirected contact probabilities.

    • Main Results:

    • Demonstrated that epidemic probability and expected infected fraction can differ in semi-directed networks, challenging the assumption of equality.
    • Showcased the framework's ability to accurately predict HCW vulnerability during outbreaks.
    • Validated the efficacy of various hospital-based containment strategies using the developed model.

    Conclusions:

    • Semi-directed networks exhibit distinct epidemic dynamics compared to networks with assumed symmetric transmission.
    • The developed percolation theory-based framework provides a more accurate approach to modeling infectious disease spread.
    • This approach enhances the prediction of disease impact on vulnerable populations like HCWs and the effectiveness of interventions.