inst/web/BasicICMs.R

In statnet/EpiModel: Mathematical Modeling of Infectious Disease Dynamics

## ----setup, include=FALSE------------------------------------------------
require(knitr)
require(EpiModel)
opts_chunk$set(comment = NA, message = FALSE, tidy = FALSE)

## ----icmSi, results = "hide"---------------------------------------------
param <- param.icm(inf.prob = 0.2, act.rate = 0.25)
init <- init.icm(s.num = 500, i.num = 1)
control <- control.icm(type = "SI", nsims = 10, nsteps = 300)
mod <- icm(param, init, control)

## ----icmSiprint----------------------------------------------------------
mod

## ----icmSiSumm-----------------------------------------------------------
summary(mod, at = 125)

## ----icmSIAsDf-----------------------------------------------------------
head(as.data.frame(mod, out = "mean"))
tail(as.data.frame(mod, out = "vals", sim = 1))

## ----icmSiPlot-----------------------------------------------------------
plot(mod)

## ----icmSiPlot2----------------------------------------------------------
plot(mod, y = "i.num", sim.lines = TRUE, mean.smooth = FALSE,
     qnts.smooth = FALSE)

## ----icmSirDet-----------------------------------------------------------
param <- param.dcm(inf.prob = 0.2, act.rate = 0.8, rec.rate = 1 / 50,
                   a.rate = 1 / 100, ds.rate = 1 / 100, di.rate = 1 / 90,
                   dr.rate = 1 / 100)
init <- init.dcm(s.num = 900, i.num = 100, r.num = 0)
control <- control.dcm(type = "SIR", nsteps = 300)
det <- dcm(param, init, control)

## ----icmSirSto, results = "hide"-----------------------------------------
param <- param.icm(inf.prob = 0.2, act.rate = 0.8, rec.rate = 1 / 50,
                   a.rate = 1 / 100, ds.rate = 1 / 100, di.rate = 1 / 90,
                   dr.rate = 1 / 100)
init <- init.icm(s.num = 900, i.num = 100, r.num = 0)
control <- control.icm(type = "SIR", nsteps = 300, nsims = 10)
sim <- icm(param, init, control)

## ----icmSirSto2, results = "hide"----------------------------------------
control <- control.icm(type = "SIR", nsteps = 300, nsims = 10,
                       a.rand = FALSE, d.rand = FALSE)
sim2 <- icm(param, init, control)

## ----icmSirPlot----------------------------------------------------------
plot(det, alpha = 0.75, lwd = 4, main = "DCM and ICM Comparison")
plot(sim, qnts = FALSE, sim.lines = FALSE, add = TRUE, mean.lty = 2,
     legend = FALSE)
plot(sim2, qnts = FALSE, sim.lines = FALSE, add = TRUE, mean.lty = 3,
     legend = FALSE)

## ----icmSirPlot2, fig.height = 4-----------------------------------------
par(mfrow = c(1, 2), mar = c(3, 3, 2, 1), mgp = c(2, 1, 0))
plot(sim, y = "di.flow", mean.line = FALSE,
     sim.lines = TRUE, sim.alpha = 0.15, ylim = c(0, 20),
     main = "di.flow: Full Stochastic Model")
plot(sim2, y = "di.flow", mean.line = FALSE,
     sim.lines = TRUE, sim.alpha = 0.15, ylim = c(0, 20),
     main = "di.flow: Limited Stochastic Model")

## ----icmSirAdf-----------------------------------------------------------
icm.compare <- rbind(round(as.data.frame(sim, out = "sd")[50, ], 2),
                     round(as.data.frame(sim2, out = "sd")[50, ], 2))
row.names(icm.compare) <- c("full", "lim")
icm.compare

## ----icmSisSim, results = "hide"-----------------------------------------
param <- param.icm(inf.prob = 0.2, inf.prob.g2 = 0.1, act.rate = 0.5,
                   balance = "g1", rec.rate = 1 / 25, rec.rate.g2 = 1 / 50,
                   a.rate = 1 / 100, a.rate.g2 = NA, ds.rate = 1 / 100,
                   ds.rate.g2 = 1 / 100, di.rate = 1 / 90, di.rate.g2 = 1 / 90)
init <- init.icm(s.num = 500, i.num = 1, s.num.g2 = 500, i.num.g2 = 1)
control <- control.icm(type = "SIS", nsteps = 500, nsims = 10)

set.seed(1)
sim <- icm(param, init, control)

## ----icmSISPlot1---------------------------------------------------------
par(mfrow = c(1, 1))
plot(sim)

## ----icmSisPlot2---------------------------------------------------------
plot(sim, y = c("i.num", "i.num.g2"), mean.lwd = 3, sim.lines = TRUE,
     sim.col = c("steelblue", "firebrick"), legend = TRUE,
     main = "Disease Prevalence: Means and Individual Simulations")