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Related Experiment Video

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Multivariate modelling of infectious disease surveillance data.

M Paul¹, L Held, A M Toschke

¹Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.

Statistics in Medicine

|September 19, 2008

Summary

This study presents a novel model for analyzing infectious disease time series, accounting for pathogen dependence and spatial spread. The R package surveillance facilitates these advanced epidemiological analyses.

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Area of Science:

Epidemiology
Biostatistics
Mathematical Modeling

Background:

Analyzing multivariate time series of infectious disease counts is crucial for public health surveillance.
Existing methods may not adequately capture dependencies between different pathogens or spatio-temporal dynamics.

Purpose of the Study:

To develop and present a model-based approach for analyzing multivariate infectious disease count data.
To extend existing methods by incorporating pathogen dependence and spatio-temporal dispersal information.
To provide practical examples using real-world disease surveillance data.

Main Methods:

A model-based approach is proposed to analyze multivariate time series of infectious disease counts.
The model accounts for potential dependence between counts of different pathogens.
Spatio-temporal information, including global pathogen dispersal and air traffic data, is integrated.
Maximum likelihood estimates are obtained using general optimization routines within the R package surveillance.

Main Results:

The methodology is demonstrated through analyses of weekly influenza and meningococcal disease counts in Germany.
The spatio-temporal spread of influenza in the USA (1996-2006) is analyzed using air traffic data.
The R package surveillance provides a practical implementation for these complex models.

Conclusions:

The proposed model offers a robust framework for analyzing complex infectious disease surveillance data.
Incorporating pathogen dependence and spatio-temporal factors enhances epidemiological analysis.
The R package surveillance facilitates the application of these advanced statistical methods in public health.