Abstract
Public health surveillance aims at lessening disease burden, e.g., in case of infectious diseases by timely recognizing emerging outbreaks. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionality for the visualization, modelling and monitoring of surveillance time series. With respect to modelling we focus on univariate time series modelling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. This ranges from illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g, by spline-modelling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-Multinomial modelling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihood-ratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.
Highlights
Nowadays, the fight against infectious diseases does require treating patients and setting up measures for prevention and demands the timely recognition of emerging outbreaks in order to avoid their expansion
In this paper we present the R package surveillance, which implements a range of methods for aberration detection in time series of counts and proportions
We introduce an implementation of the negative binomial cumulative sum (CUSUM) of Hohle and Paul (2008) that allows the detection of sustained shifts by accumulating evidence over several timepoints
Summary
The fight against infectious diseases does require treating patients and setting up measures for prevention and demands the timely recognition of emerging outbreaks in order to avoid their expansion. Along these lines, health institutions such as hospitals and public health authorities collect and store information about health events – typically represented as individual case reports containing clinical information, and subject to specific case definitions. In this paper we present the R package surveillance, which implements a range of methods for aberration detection in time series of counts and proportions.
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