momo | R Documentation |
Weekly number of all cause mortality from 1994-2008 in each of the eight age groups <1, 1-4, 5-14, 15-44, 45-64, 65-74, 75-84 and 85+ years, see Höhle and Mazick (2010).
data(momo)
An object of class "sts"
containing the weekly
number of all-cause deaths in Denmark, 1994-2008 (782 weeks), for each
of the eight age groups <1, 1-4, 5-14, 15-44, 45-64, 65-74,
75-84 and 85+ years. A special feature of the EuroMOMO data is that
weeks follow the ISO 8601 standard, which can be
handled by the "sts"
class.
The population
slot of the momo
object contains the
population size in each of the eight age groups.
These are yearly data obtained from the StatBank Denmark.
European monitoring of excess mortality for public health action (EuroMOMO) project. https://www.euromomo.eu/.
Department of Epidemiology, Statens Serum Institute, Copenhagen, Denmark StatBank Denmark, Statistics Denmark, https://www.statistikbanken.dk/
Höhle, M. and Mazick, A. (2010). Aberration detection in R
illustrated by Danish mortality monitoring. In T. Kass-Hout and X.
Zhang (eds.), Biosurveillance: A Health Protection Priority,
chapter 12. Chapman & Hall/CRC.
Preprint available at
https://staff.math.su.se/hoehle/pubs/hoehle_mazick2009-preprint.pdf
data("momo")
momo
## show the period 2000-2008 with customized x-axis annotation
## (this is Figure 1 in Hoehle and Mazick, 2010)
oopts <- surveillance.options("stsTickFactors" = c("%G" = 1.5, "%Q"=.75))
plot(momo[year(momo) >= 2000,], ylab = "", xlab = "Time (weeks)",
par.list = list(las = 1), col = c(gray(0.5), NA, NA),
xaxis.tickFreq = list("%G"=atChange, "%Q"=atChange),
xaxis.labelFreq = list("%G"=atChange), xaxis.labelFormat = "%G")
surveillance.options(oopts)
if (surveillance.options("allExamples")) {
## stratified monitoring from 2007-W40 using the Farrington algorithm
phase2 <- which(epoch(momo) >= "2007-10-01")
momo2 <- farrington(momo, control = list(range=phase2, alpha=0.01, b=5, w=4))
print(colSums(alarms(momo2)))
plot(momo2, col = c(8, NA, 4), same.scale = FALSE)
## stripchart of alarms (Figure 5 in Hoehle and Mazick, 2010)
plot(momo2, type = alarm ~ time, xlab = "Time (weeks)", main = "",
alarm.symbol = list(pch=3, col=1, cex=1.5))
}
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