surveillance (version 1.12.1)

sts_creation: Function for simulating a time series

Description

Function for simulating a time series and creating a sts-object As the counts are generated using a negative binomial distribution one also gets the (1-alpha) quantile for each timepoint (can be interpreted as an in-control upperbound for in-control values). The baseline and outbreaks are created as in Noufaily 2012.

Usage

sts_creation(theta, beta, gamma1, gamma2, m, overdispersion, dates,
  sizesOutbreak, datesOutbreak, delayMax, alpha, densityDelay)

Arguments

theta

baseline frequency of reports

beta

time trend

gamma1

seasonality

gamma2

seasonality

m

seasonality

overdispersion

overdispersion (size in rnbinom for the parameterization with mean and size)

dates

dates of the time series

sizesOutbreak

sizes of all the outbreaks (vector)

datesOutbreak

dates of all the outbreaks (vector) # alpha

delayMax

maximal delay in time units

alpha

alpha for getting the (1-alpha) quantile of the negative binomial distribution at each timepoint

densityDelay

density distribution for the delay

References

An improved algorithm for outbreak detection in multiple surveillance systems, Noufaily, A., Enki, D.G., Farrington, C.P., Garthwaite, P., Andrews, N.J., Charlett, A. (2012), Statistics in Medicine, published online.

Examples

set.seed(12345)
# Time series parameters
scenario4 <- c(1.6,0,0.4,0.5,2)
theta <- 1.6
beta <- 0
gamma1 <-0.4
gamma2 <- 0.5
overdispersion <- 1
m <- 1
# Dates
firstDate <- "2006-01-01"
lengthT=350
dates <- as.Date(firstDate,origin='1970-01-01') + 7 * 0:(lengthT - 1)
# Maximal delay in weeks
D=10
# Dates and sizes of the outbreaks
datesOutbreak <- c(as.Date("2008-03-30"),as.Date("2011-09-25",origin="1970-01-01"))
sizesOutbreak <- c(2,5)
# Delay distribution
data("salmAllOnset")
in2011 <- which(formatDate(epoch(salmAllOnset), "%G") == 2011)
rT2011 <- salmAllOnset@control$reportingTriangle$n[in2011,]
densityDelay <- apply(rT2011,2,sum, na.rm=TRUE)/sum(rT2011, na.rm=TRUE)
# alpha for the upperbound
alpha <- 0.05
# Create the sts with the full time series
stsSim <- sts_creation(theta=theta,beta=beta,
                       gamma1=gamma1,gamma2=gamma2,
                       m=m,overdispersion=overdispersion
                       ,dates=dates,sizesOutbreak=sizesOutbreak,
                       datesOutbreak=datesOutbreak,
                       delayMax=D,
                         densityDelay=densityDelay,
                         alpha=alpha)
plot(stsSim)

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