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  • 1. Meyer, Sebastian
    et al.
    Held, Leonhard
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance2017Inngår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 77, nr 11Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

  • 2. Salmon, Maelle
    et al.
    Schumacher, Dirk
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance2016Inngår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 70, nr 10, s. 1-35Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. 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 functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. 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.

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