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for example, using the S4 sts class for gathering data and methods, and
using likelihood ratio-based cumulative sum (CUSUM) algorithms.
At the time of this writing, only few other R packages exist aimed at help-
ing epidemiologists in their outbreak detection and outbreak investigation.
Retrospective cluster detection is available, for example, in the DCluster
package (Gõmez-Rubio et al., 2005) and visualization of outbreak data can
be performed by epitools (Aragon, 2008). Retrospective and—to some
extent—prospective investigations of structural changes in time series can
also be performed by the package strucchange (Zeileis et al., 2002), which,
however, aims more at the econometrics community.
12.1.1 The euroMOMO Project
The program European Monitoring of Excess Mortality for Public Health
Action (EuroMOMO) is a 3-year project representing a network of 23 part-
ners from 21 countries in the European region. The project is cofunded by the
European Commission and coordinated by Statens Serum Institut Denmark
(Mazick, 2007; Anonymous, 2009).
The aim of EuroMOMO is to develop and strengthen real-time moni-
toring of mortality across Europe; this will enhance the management of
serious public health risks such as pandemic influenza, heat waves, and
cold snaps. EuroMOMO's general objective is to develop and operate a
routine public health system that monitors all-cause mortality in order to
detect and measure—in a timely manner—excess number of deaths related
to influenza and other known or emerging public health threats across
European countries. Main actions include the creation of an inventory of
existing mortality monitoring systems in Europe; the definition of minimal
requirements for a mortality monitoring system; retrospective analysis of
mortality data; identification of an optimal common analytical approach;
and piloting of such a consensus system for mortality monitoring in sev-
eral European countries.
Mortality monitoring is useful for early detection and monitoring of severe
impacts of health threats and is as such an indicator-based surveillance sys-
tem that provides important information within the framework of epidemic
intelligence. The latter comprises the collection, collation, analysis, and assess-
ment of information from different sources to rapidly identify and respond
to known and unknown public health threats (Kaiser et al., 2006). Vital statis-
tics are accessible for all European countries. However, often these data are
not readily available in a timely manner during health crises or for imminent
health threats. On the other hand, decision makers will request up-to-date
mortality data in case of the threat of epidemics or emergence of new dis-
eases (for example, pandemic influenza, AIDS, or SARS). As these threats are
not restricted by borders, not only a national but also a common European
approach to detect and estimate the magnitude of deaths is required. This
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