Multivariate Count Time Series Modeling of Surveillance Data


Surveillance data on various notifiable diseases usually consist of multiple time series of daily, weekly, or monthly counts of new infections. Data are typically reported in several strata defined through administrative geographical areas, gender and/or age groups. Statistical modeling of the resulting multivariate time series is an important task in infectious disease epidemiology. We will discuss time series models - specifically developed for multivariate surveillance count data - that can be used for two distinct roles, understanding and prediction of disease spread. We will describe such applications using the hhh4 model class in the open source R package surveillance.


Michael Höhle, Associate Professor, Department of Mathematics, Stockholm University, Sweden


Michael Höhle works (part-time) as an associate professor inmathematical statistics at Stockholm University. After his Ph.D. in2003, he obtained his post-doctoral qualification (habilitation) in 2009 at the Department of Statistics, LMU Munich, Germany. Before moving to Stockholm University in 2013, he obtained practical experiences in applied infectious disease epidemiology while working as senior statistician at the Robert Koch Institute in Berlin during 2010-2013.

His research interests cover biostatistical methods in general and statistical methods for infectious disease epidemiology in particular. He is the initiator of the R package surveillance available from the Comprehensive R Archive Network (CRAN).

Leonhard Held, Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich

Prof. Held obtained his Ph.D. in 1997 at the Department of Statistics at LMU Munich under the supervision of Ludwig Fahrmeir. During his Ph.D. studies, he spent a year at the Department of Statistics of the University of Washington in Seattle, USA. He was Lecturer and Senior Lecturer in Medical Statistics at Imperial College London (2000-2001) and Lancaster University (2001-2002), UK. He then spent four years (2003-2006) at LMU Munich as Associate Professor of Biostatistics. Since September 2006 he is full professor at the Univeristy of Zurich.

His methodological research interests are in spatial and spatio-temporal statistics, longitudinal data analysis and Bayesian inference. Most of his research is motivated by epidemiological and clinical applications. He is particularly well-known for his contributions to Spatial Epidemiology, Infectious Disease Epidemiology and Bayesian Biostatistics.

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Event/Publication Date: 
September, 2016

March 13, 2017

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