title: 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.
authors: Physics Department, University of California Santa Barbara, Santa Barbara, California, United States of America; Applied Physics Department, Stanford University, Stanford, California, United States of America.
authors: Edwin C
authors: EC
authors: Yuan
authors: Operations Research Department, Naval Postgraduate School, Monterey, California, United States of America.
authors: David L
authors: DL
authors: Alderson
authors: Physics Department, University of California Santa Barbara, Santa Barbara, California, United States of America.
authors: Sean
authors: S
authors: Stromberg
authors: Physics Department, University of California Santa Barbara, Santa Barbara, California, United States of America.
authors: Jean M
authors: JM
authors: Carlson
mesh_terms: D000465:Algorithms; D003140:Communicable Disease Control; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D008962:Models, Theoretical; D014611:Vaccination