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Statistical Methods for Analyzing Epidemiological Data

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

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Modelling multisera data: the estimation of new joint and conditional epidemiological parameters.

N Hens¹, M Aerts, Z Shkedy

¹Center for Statistics, Hasselt University, Campus Diepenbeek, Agoralaan-Gebouw D, Diepenbeek, Belgium. niel.hens@uhasselt.be

Statistics in Medicine

|November 1, 2007

Summary

This study introduces new epidemiological parameters to analyze co-infections with Varicella-Zoster virus and Parvo B19 virus. The findings help understand disease dynamics and population mixing for infectious diseases.

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Area of Science:

Epidemiology
Infectious Disease Dynamics
Biostatistics

Background:

Antibody testing in serum samples is crucial for diagnosing infectious diseases.
Simultaneous testing for multiple antigens in serum samples enhances feasibility and cost-effectiveness.
Analyzing diseases with similar transmission routes offers insights into disease dynamics.

Purpose of the Study:

To model multisera data for Varicella-Zoster virus and Parvo B19 virus in Belgium.
To derive age-dependent marginal, joint, and conditional force of infection (FOI) parameters.
To investigate the association between Varicella-Zoster and Parvo B19 infections and population mixing.

Main Methods:

Utilized flexible marginal and conditional models for multisera data analysis.
Derived age-dependent marginal, joint, and conditional force of infection (FOI).
Developed methods to test for associations and proportionality of age-dependent FOI curves.

Main Results:

Introduced novel age-dependent joint and conditional FOI parameters.
Demonstrated the ability to study associations between co-infections.
Provided a framework to assess population mixing based on infection patterns.

Conclusions:

The new epidemiological parameters offer a deeper understanding of co-infection dynamics.
The study framework allows for the assessment of population mixing related to infectious diseases.
Findings contribute to the epidemiological study of viral co-infections.