This package provides 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 underlying disease parameters through to the decision process via Bayesian posterior inference. 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.
|Author||Daniel Merl <[email protected]>, Leah R. Johnson <[email protected]>, Robert B. Gramacy <[email protected]>, and Marc S. Mangel <[email protected]>|
|Maintainer||Robert B. Gramacy <[email protected]>|
|Package repository||View on CRAN|
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