Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs a Monte Carlo simulation of epidemic
trajectories under an adaptive vaccination strategy as implemented
by the manage
function. Statistics are tallied for
a collection of characteristics including the state
of the epidemic (evolution of susceptibles (S), infecteds (I),
recovereds (R), and deads) and the cost of the vaccination
strategy employed
1 2 3 4 5 6 | MCmanage(init, epistep, vacgrid, costs,
pinit = list(b = 0.1, k = 0.02, nu = 0.2, mu = 0.1),
hyper = list(bh = c(1,3), kh = c(1,3), nuh = c(1,1), muh = c(1,1)),
vac0 = list(frac = 0, stop = 0), T = 40, MCreps = 30,
MCvits = 50, MCMCpits = 1000, bkrate = 1, vacsamps = 50,
quant = c(0.025, 0.975), start = 7, ...)
|
init |
a |
epistep |
a function which moves the epidemic ahead one
time-step; see |
vacgrid |
a |
costs |
a |
pinit |
a |
hyper |
a |
vac0 |
the initial (static) vaccination policy to be used
before estimation of parameters begins (at |
T |
the maximum number of time steps during which the epidemic is allowed to evolve |
MCreps |
number of times to repeat the Monte Carlo experiment,
each time starting with the state in |
MCvits |
scalar number of Monte Carlo iterations of forward epidemic
evolution used at each time step in |
MCMCpits |
scalar number of Markov chain Monte Carlo iterations used
at each step to estimate the SIR model parameters in
|
bkrate |
number of samples of |
vacsamps |
used to thin the MCMC samples of the parameters sampled
from the posterior that are used to calculate optimal vaccination policies;
this should be an integer scalar such that |
quant |
a 2-vector of quantiles to use in order to capture the spread in the density of characteristics of the epidemic trajectory and costs |
start |
at what time, after time 1 where the state is given
by |
... |
additional arguments passed to a user-defined
|
This function simulates many (MCreps
) trajectories of an
epidemic starting out in a particular state (init
) and
evolving according the dynamics encoded in epistep
(or some other user-defined function) under an adaptive
vaccination strategy as implemented by manage
.
Many of the arguments to this function are simply passed
to manage
.
It returns a summary of characteristics of the state trajector(y/ies)
and the associated cost
s. The output can be
visualized with the generic plot.MCepi
method
and costs can be extracted with getcost
.
For more details on the parameterization and simulation of the
SIR model, etc., and the calculation of the optimal vaccination
strategy, please see vignette("amei")
MCmanage
returns an object of class "MCepi"
, which is a
list
containing the following components.
Q1 |
a |
Mean |
same as |
Median |
same as |
Q3 |
same as |
These quantities can be visually inspected using the
plot.MCepi
method
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 24 25 26 27 28 29 30 31 32 33 34 35 | ## adaptively manage the epidemic with the following
## initial population
init <- list(S0=762, I0=1, R0=0, D0=0)
## construct a grid of valid vaccination strategies
## and specify costs
## using a smaller grid for faster check times; try the commented out
## setting for higher fidelity
vacgrid <- list(fracs=seq(0,1.0,0.25), stops=seq(2,init$S0-75,150))
## vacgrid <- list(fracs=seq(0,1.0,0.1), stops=seq(2,init$S0-75,75))
costs <- list(vac=2, death=4, infect=1)
## run the Monte Carlo management experiment, reducing MCMCpits
## for faster check times; try default (commented out) version
out.MCmanage <- MCmanage(init, epistep, vacgrid, costs,
MCMCpits=100, MCreps=5)
## out.MCmanage <- MCmanage(init, epistep, vacgrid, costs)
## plot the trajectories of SIR and the associated costs
plot(out.MCmanage, main="optimal adaptive vaccination")
plot(out.MCmanage, type="costs")
## extract the distribution of the number of
## cumulative vaccinations via median and quantiles
getvac(out.MCmanage)
## plot the distribution fractions vaccinated and
## stopping times
plot(out.MCmanage, type="fracs")
plot(out.MCmanage, type="stops")
## get the final median cost and quantiles --
## these can be compared with the static ones
## calculated by MCepi
getcost(out.MCmanage)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.