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

Deterministic epidemiological models at the individual level.

Kieran J Sharkey1

  • 1Department of Mathematical Sciences, The University of Liverpool, Liverpool L69 7ZL, UK. kieran.sharkey@manchester.ac.uk

Journal of Mathematical Biology
|February 15, 2008
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

Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks.

Infectious Disease Modelling·2024
Same author

Eco-evolutionary dynamics in finite network-structured populations with migration.

Journal of theoretical biology·2023
Same author

Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality.

BMJ open·2022
Same author

Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2.

BMC infectious diseases·2022
Same author

Approximating Quasi-Stationary Behaviour in Network-Based SIS Dynamics.

Bulletin of mathematical biology·2021
Same author

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Nature communications·2021
Same journal

Phenotypic plasticity trade-offs in an age-structured model of bacterial growth under stress.

Journal of mathematical biology·2026
Same journal

Intraspecific interactions facilitate mutualism across multilayer networks under weak selection.

Journal of mathematical biology·2026
Same journal

A two-species competition model on a compact metric graph for the invasion and competition of Aedes Aegypti and Aedes Albopictus mosquitoes in Florida.

Journal of mathematical biology·2026
Same journal

Superinfection and the hypnozoite reservoir for Plasmodium vivax: a multitype branching process approximation.

Journal of mathematical biology·2026
Same journal

Stoichiometric balance and sustained rhythms.

Journal of mathematical biology·2026
Same journal

A generalised framework for building complex networks of evolution equations.

Journal of mathematical biology·2026
See all related articles

This study introduces approximate deterministic models to simplify complex epidemiological systems. The method aids in linking stochastic simulations with traditional population-level models for disease dynamics.

Area of Science:

  • Epidemiology
  • Population Dynamics
  • Computational Biology

Background:

  • Vast state spaces in complex systems often prevent deterministic analysis.
  • Stochastic simulations are computationally intensive for large systems.
  • Bridging stochastic and deterministic models is crucial for understanding disease spread.

Purpose of the Study:

  • To develop a method for constructing approximate deterministic models for epidemiological systems.
  • To reduce complex state spaces to numerically feasible dimensions.
  • To clarify the relationship between stochastic and deterministic epidemiological models.

Main Methods:

  • Introduced approximate deterministic models reducing state space complexity.
  • Employed closure approximations to ensure a closed set of equations.

Related Experiment Videos

  • Applied the method to a susceptible-infectious-removed (SIR) model on various networks.

    • Main Results:

    • Successfully created a method for deterministic differential equation models.
    • Demonstrated applicability to dynamic, heterogeneous contact networks with time-dependent parameters.
    • Numerically evaluated the SIR model on diverse network structures.

    Conclusions:

    • The developed method offers a computationally feasible approach to epidemiological modeling.
    • Provides a clearer link between stochastic simulations and deterministic population-level models.
    • Has broad applications in understanding epidemic propagation on contact networks.