The CDC Algorithm
Surveillance using the CDC Algorithm
algo.cdcLatestTimepoint(disProgObj, timePoint = NULL,
control = list(b = 5, m = 1, alpha=0.025))
algo.cdc(disProgObj, control = list(range = range, b= 5, m=1,
alpha = 0.025))disProgObj |
object of class disProg (including the observed and the state chain). |
timePoint |
time point which should be evaluated in |
control |
control object: |
Using the reference values for calculating an upper limit, alarm is
given if the actual value is bigger than a computed threshold.
algo.cdc calls algo.cdcLatestTimepoint for the values
specified in range and for the system specified in
control. The threshold is calculated from the predictive
distribution, i.e.
mean(x) + z_{α/2} * sd(x) * √(1+1/k),
which corresponds to Equation 8-1 in Farrington and Andrews (2003).
Note that an aggregation into 4-week blocks occurs in
algo.cdcLatestTimepoint and m denotes number of 4-week
blocks (months) to use as reference values. This function currently
does the same for monthly data (not correct!)
algo.cdcLatestTimepoint returns a list of class survRes (surveillance result), which
includes the alarm value (alarm = 1, no alarm = 0) for recognizing an
outbreak, the threshold value for recognizing the alarm and
the input object of class disProg.
algo.cdc gives a list of class survRes which
includes the vector of alarm values for every timepoint in
range, the vector of threshold values for every timepoint
in range for the system specified by b, w,
the range and the input object of class disProg.
M. Höhle
Stroup, D., G. Williamson, J. Herndon, and J. Karon (1989). Detection of aberrations in the occurence of notifiable diseases surveillance data. Statistics in Medicine 8, 323-329.
Farrington, C. and N. Andrews (2003). Monitoring the Health of Populations, Chapter Outbreak Detection: Application to Infectious Disease Surveillance, pp. 203-231. Oxford University Press.
algo.rkiLatestTimepoint,algo.bayesLatestTimepoint
and algo.bayes for the Bayes system.
# Create a test object
disProgObj <- sim.pointSource(p = 0.99, r = 0.5, length = 500,
A = 1,alpha = 1, beta = 0, phi = 0,
frequency = 1, state = NULL, K = 1.7)
# Test week 200 to 208 for outbreaks with a selfdefined cdc
algo.cdc(disProgObj, control = list(range = 400:500,alpha=0.025))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.