Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs a Monte Carlo simulation of epidemic trajectories under a static vaccination strategy. Statistics are collected 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 
init 
a 
params 
a 
vac 
a 
costs 
a 
T 
the maximum number of time steps during which the epidemic is allowed to evolve 
MCreps 
the number of times to repeat the Monte Carlo experiment,
each time starting with the state in 
quant 
a 2vector of quantiles to use in order to capture the spread in the density of characteristics of the epidemic trajectory and costs 
midepi 
a debugging 
start 
at what time, after time 1 where the state is given
by 
... 
additional arguments passed to a userdefined

This function simulates many (MCreps
) trajectories of an
epidemic starting out in a particular state (init
) and
evolving according to a particular (true
) parameterization
under a fixed vaccination strategy (vac
). 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")
MCepi
returns an object of class "MCepi"
, which is a
list
containing the components listed below.
Q1 
a 
Mean 
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), 132. 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 36 37 38  ## true epidemic parameters, initial values, and
## vaccination costs
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)
## trivial vaccination strategy  dont vaccinate
vac < list(frac=0, stop=0)
## simulate the resulting trajectories
init.MCepi < MCepi(init, truth, vac, costs)
## plot the distribution of trajectories and costs
## under no vaccination
plot(init.MCepi, main="no vaccination")
plot(init.MCepi, type="costs")
## Now try the optimal strategy.
## See the optvac function for more info
vac.opt < list(frac=0.7, stop=502)
opt.MCepi < MCepi(init, truth, vac.opt, costs)
## plot the distribution of trajectories and costs
## under the optimal (static) vaccination
plot(opt.MCepi, main="optimal static vaccination")
plot(opt.MCepi, type="costs")
## show the total number of vaccinations
## median and quantiles
getvac(opt.MCepi)
## compare the median costs of the the initial
## (no vaccination) strategy versus the optimal
## (static) policy
T < length(opt.MCepi$Median$C)
optC < getcost(opt.MCepi)
initC < getcost(init.MCepi)
rbind(initC,optC)

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