MCepi: Monte Carlo Epidemic Simulation with Static Vaccination In amei: Adaptive Management of Epidemiological Interventions

Description

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

Usage

 1 2 MCepi(init, params, vac, costs, T = 40, MCreps = 1000, quant = c(0.025, 0.975), midepi = FALSE, start = 7, ...)

 init a list containing scalar entries $S0,$I0, $R0,$D0 depicting the initial number of susceptibles, infecteds, recovereds, and deads in the outbreak params a list containing scalar entries $b,$k, $nu, and$mu depicting the true parameters of the SIR model representing the transmission probability, clumpiness parameter, the recovery probability, and the mortality probability, respectively; optionally params may be an epistep function vac a list containing scalar entries $frac and$stop depicting the fraction to be vaccinated at each time step, and the vaccination (stopping) threshold, respectively costs a list containing scalar entries $vac,$death, and $infect depicting the costs associated with a single vaccination, death, or daily cost of maintaining an infected individual, respectively 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 init and collecting characteristics of the epidemic trajectories and (vaccination/death) costs 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 midepi a debugging logical indicating whether to signal that a trajectory is unlikely to be an epidemic start at what time, after time 1 where the state is given by init, should vaccinations be allowed to start ... additional arguments passed to a user-defined epistep function that may be passed in via params Details 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 costs. 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") Value MCepi returns an object of class "MCepi", which is a list containing the components listed below.  Q1 a data.frame containing 6 columns (S, I, R, D, V, C) depicting the first quantile (quant) of the distribution of the evolution of the state of the epidemic (SIRD), the number of vaccinations (V), and the cost (C), at each time point Mean same as Q1 except the mean rather than a quantile Q3 same as Q1 except the third quantile (quant) These quantities can be visually inspected using the plot.MCepi method Author(s) 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> References 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 See Also Examples  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)

Example output    q0.025    mean median q0.975
1      0 352.756  417.5    515
q0.025     mean median   q0.975
1 2155.975 2295.795 2296.5 2433.000
2 1506.975 1847.815 1790.0 2377.125

amei documentation built on May 29, 2017, 5:33 p.m.