surveillance (version 1.24.0)

algo.cdc: The CDC Algorithm

Description

Surveillance using the CDC Algorithm

Usage

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))

Value

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.

Arguments

disProgObj

object of class disProg (including the observed and the state chain).

timePoint

time point which should 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.

Author

M. Höhle

Details

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. $$mean(x)+z_{\alpha /2}\ast sd(x)\ast \sqrt{1+1/k},$$ 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!)

References

Stroup, D., G. Williamson, J. Herndon, and J. Karon (1989). Detection of aberrations in the occurrence of notifiable diseases surveillance data. Statistics in Medicine 8, 323--329. tools:::Rd_expr_doi("10.1002/sim.4780080312")

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.

See Also

algo.rkiLatestTimepoint,algo.bayesLatestTimepoint and algo.bayes for the Bayes system.

Examples

# 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))