ABSTRACT
We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*) Subject(s)
Disease Outbreaks/prevention & control
, Models, Biological
, Stochastic Processes
, Vaccination/methods
, Algorithms
, Basic Reproduction Number
, Communicable Disease Control/economics
, Communicable Disease Control/methods
, Communicable Diseases/epidemiology
, Communicable Diseases/transmission
, Cost-Benefit Analysis
, Humans
, Vaccination/economics
, Vaccines/economics
, Vaccines/supply & distribution
