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This chapter provides a comprehensive overview of statistical approaches in infectious disease research, comparing Frequentist and Bayesian frameworks through both theoretical exposition and practical examples. While Frequentist methods emphasize hypothesis testing and long-run frequency properties, Bayesian methods incorporate prior knowledge and yield full posterior distributions for parameters of interest. This chapter illustrates how Bayesian tools, such as credible intervals, hypothesis testing via the Probability of Direction (PD), Region of Practical Equivalence (ROPE), and Bayes factors computed using the Savage–Dickey Density Ratio (SDDR), offer intuitive and flexible alternatives to p-values. It also includes a discussion of Bayesian prior specification, computational tools for Bayesian inference, and estimation convergence. Emphasis is placed on how Bayesian analysis can complement traditional Frequentist inference to support decision-making under uncertainty in infectious disease contexts.

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Bayesian and Frequentist Approaches in Infectious Disease Data Analysis

  • 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 provides a comprehensive overview of statistical approaches in infectious disease research, comparing Frequentist and Bayesian frameworks through both theoretical exposition and practical examples. While Frequentist methods emphasize hypothesis testing and long-run frequency properties, Bayesian methods incorporate prior knowledge and yield full posterior distributions for parameters of interest. This chapter illustrates how Bayesian tools, such as credible intervals, hypothesis testing via the Probability of Direction (PD), Region of Practical Equivalence (ROPE), and Bayes factors computed using the Savage–Dickey Density Ratio (SDDR), offer intuitive and flexible alternatives to p-values. It also includes a discussion of Bayesian prior specification, computational tools for Bayesian inference, and estimation convergence. Emphasis is placed on how Bayesian analysis can complement traditional Frequentist inference to support decision-making under uncertainty in infectious disease contexts.