Evaluation of timepoints with the Bayes subsystem 1, 2, 3 or a self defined Bayes subsystem.

algo.bayesLatestTimepoint(disProgObj, timePoint = NULL,
       control = list(b = 0, w = 6, actY = TRUE,alpha=0.05))
  algo.bayes(disProgObj, control = list(range = range,
       b = 0, w = 6, actY = TRUE,alpha=0.05))
  algo.bayes1(disProgObj, control = list(range = range))
  algo.bayes2(disProgObj, control = list(range = range))
  algo.bayes3(disProgObj, control = list(range = range))

Arguments

disProgObj

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

timePoint

time point which should be evaluated in algo.bayes LatestTimepoint. 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, w is the half window width for the reference values around the appropriate timepoint and actY is a boolean to decide if the year of timePoint also contributes w reference values. The parameter alpha is the \((1-\alpha)\)-quantile to use in order to calculate the upper threshold. As default b, w, actY are set for the Bayes 1 system with alpha=0.05.

Value

survRes

algo.bayesLatestTimepoint returns a list of class survRes (surveillance result), which includes the alarm value for recognizing an outbreak (1 for alarm, 0 for no alarm), the threshold value for recognizing the alarm and the input object of class disProg. algo.bayes gives a list of class survRes which includes the vector of alarm values for every timepoint in range and the vector of threshold values for every timepoint in range for the system specified by b, w and actY, the range and the input object of class disProg. algo.bayes1 returns the same for the Bayes 1 system, algo.bayes2 for the Bayes 2 system and algo.bayes3 for the Bayes 3 system.

Details

Using the reference values the \((1-\alpha)\cdot 100\%\) quantile of the predictive posterior distribution is calculated as a threshold. An alarm is given if the actual value is bigger or equal than this threshold. It is possible to show using analytical computations that the predictive posterior in this case is the negative binomial distribution. Note: algo.rki or algo.farrington use two-sided prediction intervals -- if one wants to compare with these procedures it is necessary to use an alpha, which is half the one used for these procedures.

Note also that algo.bayes calls algo.bayesLatestTimepoint for the values specified in range and for the system specified in control. algo.bayes1, algo.bayes2, algo.bayes3 call algo.bayesLatestTimepoint for the values specified in range for the Bayes 1 system, Bayes 2 system or Bayes 3 system.

  • "Bayes 1" reference values from 6 weeks. Alpha is fixed a t 0.05.

  • "Bayes 2" reference values from 6 weeks ago and 13 weeks of the previous year (symmetrical around the same week as the current one in the previous year). Alpha is fixed at 0.05.

  • "Bayes 3" 18 reference values. 9 from the year ago and 9 from two years ago (also symmetrical around the comparable week). Alpha is fixed at 0.05.

The procedure is now able to handle NA's in the reference values. In the summation and when counting the number of observed reference values these are simply not counted.

See also

algo.call, algo.rkiLatestTimepoint and algo.rki for the RKI system.

Author

M. Höhle, A. Riebler, C. Lang

Source

Riebler, A. (2004), Empirischer Vergleich von statistischen Methoden zur Ausbruchserkennung bei Surveillance Daten, Bachelor's thesis.

Examples

    disProg <- sim.pointSource(p = 0.99, r = 0.5, length = 208, A = 1,
                                    alpha = 1, beta = 0, phi = 0,
                                    frequency = 1, state = NULL, K = 1.7)

    # Test for bayes 1 the latest timepoint
    algo.bayesLatestTimepoint(disProg)

    # Test week 200 to 208 for outbreaks with a selfdefined bayes
    algo.bayes(disProg, control = list(range = 200:208, b = 1,
                                                w = 5, actY = TRUE,alpha=0.05))
    # The same for bayes 1 to bayes 3
    algo.bayes1(disProg, control = list(range = 200:208,alpha=0.05))
    algo.bayes2(disProg, control = list(range = 200:208,alpha=0.05))
    algo.bayes3(disProg, control = list(range = 200:208,alpha=0.05))