`algo.cdc.Rd`

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

`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.

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

`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.

`algo.rkiLatestTimepoint`

,`algo.bayesLatestTimepoint`

and `algo.bayes`

for the Bayes system.

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.

```
# 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))
```