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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