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This chapter explores the methodological challenges and solutions in modeling correlated data within infectious disease surveillance and research. Focusing on hospital-based and longitudinal settings, it highlights how ignoring correlation can lead to underestimated variability and flawed inference. The chapter reviews three major modeling frameworks: Generalized Linear Mixed Models (GLMMs), Generalized Estimating Equations (GEEs), and Bayesian Hierarchical Models (BHMs). Through applied examples using real and simulated data, it illustrates their assumptions, strengths, and interpretation. Bayesian approaches are further extended to incorporate informative priors, showcasing their utility in stabilizing estimates and leveraging external evidence.

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Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Noor Muhammad Khan,
  • Ileana Baldi,
  • Maria Vittoria Chiaruttini,
  • Dario Gregori

摘要

This chapter explores the methodological challenges and solutions in modeling correlated data within infectious disease surveillance and research. Focusing on hospital-based and longitudinal settings, it highlights how ignoring correlation can lead to underestimated variability and flawed inference. The chapter reviews three major modeling frameworks: Generalized Linear Mixed Models (GLMMs), Generalized Estimating Equations (GEEs), and Bayesian Hierarchical Models (BHMs). Through applied examples using real and simulated data, it illustrates their assumptions, strengths, and interpretation. Bayesian approaches are further extended to incorporate informative priors, showcasing their utility in stabilizing estimates and leveraging external evidence.