# The CDC Algorithm

`algo.cdc.Rd`

Surveillance using the CDC Algorithm

## 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.

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

## 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.

## See also

`algo.rkiLatestTimepoint`

,`algo.bayesLatestTimepoint`

and `algo.bayes`

for the Bayes system.

## 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.
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.

## 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))
```