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

Modeling the simple epidemic with deterministic differential equations and random initial conditions.

Bonnie Kegan1, R Webster West

  • 1US Census Bureau Washington, DC 20233, USA. bonnie.e.kegan@census.gov

Mathematical Biosciences
|May 24, 2005
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

Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment.

Environmetrics·2013
Same author

The Impact of Model Uncertainty on Benchmark Dose Estimation.

Environmetrics·2013
Same author

Confidence limits on one-stage model parameters in benchmark risk assessment.

Environmental and ecological statistics·2010
Same author

Bootstrap methods for simultaneous benchmark analysis with quantal response data.

Environmental and ecological statistics·2010
Same author

On use of the multistage dose-response model for assessing laboratory animal carcinogenicity.

Regulatory toxicology and pharmacology : RTP·2007
Same author

Benchmark analysis: shopping with proper confidence.

Risk analysis : an official publication of the Society for Risk Analysis·2005

This study explores how random starting conditions affect simple epidemic models. It develops a distribution to track susceptible individuals over time and estimate epidemic duration.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Probability Theory

Context:

  • Simple epidemic models are fundamental in understanding disease spread.
  • Deterministic models often assume fixed initial conditions, which may not reflect real-world scenarios.
  • Understanding the impact of initial conditions is crucial for accurate epidemic forecasting.

Purpose:

  • To investigate the influence of random initial conditions on a deterministic simple epidemic model.
  • To derive and analyze distributions describing the proportion of susceptible individuals over time.
  • To develop methods for estimating the time until a specific proportion of the population remains susceptible.

Summary:

  • This research introduces a probabilistic approach to the simple epidemic model by assuming a Beta distribution for initial susceptible proportions.

Related Experiment Videos

  • The study derives hypergeometric functions to represent the mean and variance of susceptible individuals over time.
  • A novel distribution and quantile-based method are presented for confidence statements on epidemic timing.

    • Impact:

    • Provides a more robust framework for epidemic modeling by incorporating initial condition variability.
    • Enhances the predictive power of epidemic models for public health interventions.
    • Offers tools for quantifying uncertainty in epidemic progression and duration estimates.