Found at: doi:10.1371/journal.pone.0005819.s001 (0.04 MB DOC)  A Statistical Framework for the Adaptive Management of Epidemiological Interventions BACKGROUND: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost. METHODOLOGY: We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. CONCLUSIONS: Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptive interventions over non-adaptive ones, in terms of cost and resource efficiency, and robustness to model misspecification. Epidemiological interventions generally remove susceptible individuals or apply some form of treatment to infected individuals in order to prevent further spread of a disease. The susceptible population may be culled, as in the case of foot-and-mouth disease [1, 2] , in which case the total population size is permanently reduced. The infected population may be quarantined, as in the case of SARS [3] , in which case total population size is unchanged but the fraction of infecteds that may be in contact with susceptibles is reduced. Most commonly, susceptibles are vaccinated (cf influenza or smallpox [4, 5] ), in which case the total number of susceptibles, but not the total population size, is reduced.