algo.cdc {surveillance} | R Documentation |
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 shoud be evaluated in algo.cdcLatestTimepoint . The
default is to use the latest timepoint. |
control |
control object: range determines the desired
timepoints which should be evaluated, b describes the number of years to go
back for the reference values, m is the half window width for the reference
values around the appropriate timepoint (see details). The standard definition is b =5 and m =1.
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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 by the predictive
version, i.e.
mean(x) + z_{α/2} * sd(x) * sqrt(1+1/k),
which corresponds to Equation 8-1 in the Farrington and Andrews
chapter.
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!)
survRes |
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.
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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))