# algo.cusum: CUSUM method In surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

 algo.cusum R Documentation

## CUSUM method

### Description

Approximate one-side CUSUM method for a Poisson variate based on the cumulative sum of the deviation between a reference value k and the transformed observed values. An alarm is raised if the cumulative sum equals or exceeds a prespecified decision boundary h. The function can handle time varying expectations.

### Usage

algo.cusum(disProgObj, control = list(range = range, k = 1.04, h = 2.26,
m = NULL, trans = "standard", alpha = NULL))


### Arguments

 disProgObj object of class disProg (including the observed and the state chain) control control object: rangedetermines the desired time points which should be evaluated kis the reference value hthe decision boundary mhow to determine the expected number of cases – the following arguments are possible numerica vector of values having the same length as range. If a single numeric value is specified then this value is replicated length(range) times. NULLA single value is estimated by taking the mean of all observations previous to the first range value. "glm" A GLM of the form \log(m_t) = \alpha + \beta t + \sum_{s=1}^S (\gamma_s \sin(\omega_s t) + \delta_s \cos(\omega_s t)), where \omega_s = \frac{2\pi}{52}s are the Fourier frequencies is fitted. Then this model is used to predict the range values. transone of the following transformations (warning: Anscombe and NegBin transformations are experimental) rossistandardized variables z3 as proposed by Rossi standardstandardized variables z1 (based on asymptotic normality) - This is the default. anscombeanscombe residuals – experimental anscombe2nd anscombe residuals as in Pierce and Schafer (1986) based on 2nd order approximation of E(X) – experimental pearsonNegBincompute Pearson residuals for NegBin – experimental anscombeNegBinanscombe residuals for NegBin – experimental none no transformation alphaparameter of the negative binomial distribution, s.t. the variance is m+\alpha *m^2

### Value

algo.cusum gives a list of class "survRes" which includes the vector of alarm values for every timepoint in range and the vector of cumulative sums for every timepoint in range for the system specified by k and h, the range and the input object of class "disProg".

The upperbound entry shows for each time instance the number of diseased individuals it would have taken the cusum to signal. Once the CUSUM signals no resetting is applied, i.e. signals occurs until the CUSUM statistic again returns below the threshold.

In case control$m="glm" was used, the returned control$m.glm entry contains the fitted "glm" object.

### Note

This implementation is experimental, but will not be developed further.

### Author(s)

M. Paul and M. Höhle

### References

G. Rossi, L. Lampugnani and M. Marchi (1999), An approximate CUSUM procedure for surveillance of health events, Statistics in Medicine, 18, 2111–2122

D. A. Pierce and D. W. Schafer (1986), Residuals in Generalized Linear Models, Journal of the American Statistical Association, 81, 977–986

### Examples

# Xi ~ Po(5), i=1,...,500
set.seed(321)
stsObj <- sts(observed = rpois(500,lambda=5))
# there should be no alarms as mean doesn't change
res <- cusum(stsObj, control = list(range = 100:500, trans = "anscombe"))
plot(res, xaxis.labelFormat = NULL)

# simulated data
disProgObj <- sim.pointSource(p = 1, r = 1, length = 250,
A = 0, alpha = log(5), beta = 0, phi = 10,
frequency = 10, state = NULL, K = 0)
plot(disProgObj)

# Test weeks 200 to 250 for outbreaks
surv0 <- algo.cusum(disProgObj, control = list(range = 200:250))
plot(surv0, xaxis.years = FALSE)

# alternatively, using the newer "sts" interface
stsObj <- disProg2sts(disProgObj)
surv <- cusum(stsObj, control = list(range = 200:250))
plot(surv)
stopifnot(upperbound(surv) == surv0\$upperbound)


surveillance documentation built on June 22, 2024, 11:09 a.m.