Multivariate modelling of infectious disease surveillance data

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.