surveillance: algo.rogerson – R documentation

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algo.rogerson

Modified CUSUM method as proposed by Rogerson and Yamada (2004)

Description

Modified Poisson CUSUM method that allows for a time-varying in-control parameter θ_{0,t} as proposed by Rogerson and Yamada (2004). The same approach can be applied to binomial data if distribution="binomial" is specified.

Usage

algo.rogerson(disProgObj, control = list(range = range,
   theta0t = NULL, ARL0 = NULL, s = NULL, hValues = NULL,
   distribution = c("poisson","binomial"), nt = NULL, FIR=FALSE, 
   limit = NULL, digits = 1))

Arguments

disProgObj

object of class disProg that includes a matrix with the observed number of counts

control

list with elements

range: vector of indices in the observed matrix of disProgObj to monitor
theta0t: matrix with in-control parameter, must be specified
ARL0: desired average run length γ
s: change to detect, see findH for further details
hValues: matrix with decision intervals h for a sequence of values θ_{0,t} (in the range of theta0t)
distribution: "poisson" or "binomial"
nt: optional matrix with varying sample sizes for the binomial CUSUM
FIR: a FIR CUSUM with head start \frac{\code{h}}{2} is applied to the data if TRUE, otherwise no head start is used; see details
limit: numeric that determines the procedure after an alarm is given, see details
digits: the reference value and decision interval are rounded to digits decimal places. Defaults to 1 and should correspond to the number of digits used to compute hValues

Details

The CUSUM for a sequence of Poisson or binomial variates x_t is computed as

S_t = \max \{0, S_{t-1} + c_t (x_t- k_t)\} , \, t=1,2,… ,

where S_0=0 and c_t=\frac{h}{h_t} ; k_t and h_t are time-varying reference values and decision intervals. An alarm is given at time t if S_t ≥q h.

If FIR=TRUE, the CUSUM starts with a head start value S_0=\frac{\code{h}}{2} at time t=0. After an alarm is given, the FIR CUSUM starts again at this head start value.

The procedure after the CUSUM gives an alarm can be determined by limit. Suppose that the CUSUM signals at time t, i.e. S_t ≥q h. For numeric values of limit, the CUSUM is bounded above after an alarm is given, i.e. S_{t} is set to \min\{\code{limit} \cdot h,S_{t}\} . Using limit=0 corresponds to resetting S_t to zero after an alarm as proposed in the original formulation of the CUSUM. If FIR=TRUE, S_{t} is reset to \frac{\code{h}}{2} (i.e. limit=\frac{\code{h}}{2} ). If limit=NULL, no resetting occurs after an alarm is given.

Value

Returns an object of class survRes with elements

`alarm`	indicates whether the CUSUM signaled at time t or not (1 = alarm, 0 = no alarm)
`upperbound`	CUSUM values S_{t}
`disProgObj`	`disProg` object
`control`	list with the alarm threshold h and the specified control object

Note

algo.rogerson is a univariate CUSUM method. If the data are available in several regions (i.e. observed is a matrix), multiple univariate CUSUMs are applied to each region.

References

Rogerson, P. A. and Yamada, I. Approaches to Syndromic Surveillance When Data Consist of Small Regional Counts. Morbidity and Mortality Weekly Report, 2004, 53/Supplement, 79-85

Examples

# simulate data (seasonal Poisson)
set.seed(123)
t <- 1:300
lambda <- exp(-0.5 + 0.4 * sin(2*pi*t/52) + 0.6 * cos(2*pi*t/52))
data <- create.disProg(week = t, observed = rpois(length(lambda), lambda))

# determine a matrix with h values
hVals <- hValues(theta0 = 10:150/100, ARL0=500, s = 1, distr = "poisson")

# apply modified Poisson CUSUM
res <- algo.rogerson(data, control=c(hVals, list(theta0t=lambda,range=1:300)))
plot(res)

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30