Description Usage Arguments Details Value Author(s) References See Also Examples

A Monte Carlo method is used to calculate the expected costs of a range of static vaccination policies for an epidemic with a known parameterization and initialization

1 2 |

`init` |
a |

`params` |
a |

`vacgrid` |
a |

`costs` |
a |

`T` |
the maximum number of time steps during which the epidemic is allowed to evolve |

`MCvits` |
the number of Monte Carlo iterations of forward epidemic evolution used to determine the optimal vaccination policy |

`midepi` |
a debugging |

`start` |
at what time, after time 1 where the state is given
by |

This function use a Monte Carlo experiment to calculate the expected
costs over a range of vaccination policies specified by permissible
fractions of individuals to be vaccinated and stopping thresholds.
These policies are constructed by simulating SIR-modeled epidemics
that evolve according to `params`

starting in the
`init`

ial configuration provided.
The output is an object of class `"optvac"`

, so the cost grid,
or matrix, can be visualized with the `plot.optvac`

generic method.
The `getpolicy`

function can be used to select out the best
(and worst) one(s).

For more details on the parameterization and simulation of the
SIR model, etc., see `vignette("amei")`

`optvac`

returns an object of class `"optvac"`

, which is a
`list`

containing the following components.

`vacgrid ` |
a copy of the input |

`C ` |
a matrix of expected costs estimated for each combination
of |

Daniel Merl <danmerl@gmail.com>

Leah R. Johnson <lrjohnson@uchicago.edu>

Robert B. Gramacy <rbgramacy@chicagobooth.edu>

and Mark S. Mangel <msmangl@ams.ucsc.edu>

D. Merl, L.R. Johnson, R.B. Gramacy, and M.S. Mangel (2010).
“amei: An **R** Package for the Adaptive Management of
Epidemiological Interventions”. *Journal of Statistical Software*
**36**(6), 1-32. http://www.jstatsoft.org/v36/i06/

D. Merl, L.R. Johnson, R.B. Gramacy, M.S. Mangel (2009). “A
Statistical Framework for the Adaptive Management of Epidemiological
Interventions”. *PLoS ONE*, **4**(6), e5807.
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005807

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## same inputs as in the MCepi example
truth <- list(b=0.00218, k=10, nu=0.4, mu=0)
init <- list(S0=762, I0=1, R0=0, D0=0)
costs <- list(vac=2, death=4, infect=1)
## construct a grid of valid vaccination strategies
vacgrid <- list(fracs=seq(0,1.0,0.1), stops=seq(2,init$S0-50,50))
## calculate the cost surface of all combinations in vacgrid
out.optvac <- optvac(init, truth, vacgrid, costs)
## extract the best and worst (static) policy
best <- getpolicy(out.optvac)
worst <- getpolicy(out.optvac, which="worst")
rbind(best, worst)
## plot the cost surface along with the best and worst policy
plot(out.optvac)
## now return to MCepi for a cost comparison to no vaccination
## using these values
vac.opt <- best[3:4]
vac.opt
``` |

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