sts_creation {surveillance} | R Documentation |
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.
sts_creation(theta, beta, gamma1, gamma2, m, overdispersion, dates, sizesOutbreak, datesOutbreak, delayMax, alpha, densityDelay)
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 |
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.
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)