Optimal Vaccination in a Stochastic Epidemic Model of Two Non-Interacting Populations

Abstract

Developing robust, quantitative methods to optimize resource allocations in response to epidemics has the potential to save lives and minimize health care costs. In this paper, we develop and apply a computationally efficient algorithm that enables us to calculate the complete probability distribution for the final epidemic size in a stochastic Susceptible-Infected-Recovered (SIR) model. Based on these results, we determine the optimal allocations of a limited quantity of vaccine between two non-interacting populations. We compare the stochastic solution to results obtained for the traditional, deterministic SIR model. For intermediate quantities of vaccine, the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case.

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Document Details

Document Type
Technical Report
Publication Date
Feb 17, 2015
Accession Number
ADA617603

Entities

People

  • David L. Alderson
  • Edwin Yuan
  • Jean M. Carlson
  • Sean Stromberg

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Algorithms
  • California
  • Differential Equations
  • Diseases And Disorders
  • Distribution Curves
  • Epidemics
  • Equations
  • Immune System Phenomena
  • Immunity
  • Immunization
  • Immunomodulation
  • Military Research
  • Operations Research
  • Probability
  • Probability Distributions
  • United States
  • Vaccination

Fields of Study

  • Biology
  • Mathematics

Readers

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  • Computational Modeling and Simulation
  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.

Technology Areas

  • Biotechnology