Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis
暫譯: 傳染病數據分析中的經典與貝葉斯統計方法

Khan, Noor Muhammad, Baldi, Ileana, Chiaruttini, Maria Vittoria

Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis
  • 出版商: Springer
  • 出版日期: 2025-10-29
  • 售價: $2,550
  • 貴賓價: 9.5 折 $2,423
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032067464
  • ISBN-13: 9783032067463
  • 相關分類: 機率統計學 Probability-and-statistics

    • 海外代購書籍(需單獨結帳)

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商品描述

This open access book is a comprehensive guide that delves into the statistical methodologies used in public health and infectious disease surveillance. It contrasts the foundational principles and methodologies of both Bayesian and Frequentist statistical approaches, providing a detailed exploration of how these methods are applied to the analysis and interpretation of infectious disease data.

The book offers practical guidance on the application of these methods in real-life studies, both for surveillance and research purposes. It highlights the strengths and limitations of each approach and showcases how they can be effectively utilized in various scenarios. A set of R instructions and data examples to reproduce the analyses are provided. Among the topics covered are:

  • Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Independent Data
  • Machine Learning Models for Probabilistic Inference and Prediction
  • Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data
  • Residuals and Overdispersion in Generalized Linear Models
  • Interrupted Time Series Model in Infectious Disease Research and Surveillance
  • Generalized Linear Models with Missing Data

This topic is of particular importance to the field at this time due to the increasing need for accurate analysis and interpretation of infectious disease data, which is crucial for effective decision-making and policy formulation.

Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis is primarily intended for public health professionals in local, national or international agencies; researchers and academics; students; and veterinary and one-health specialists. These readers would find this book valuable for its in-depth analysis, practical guidance, and the critical insights it provides into the application of statistical methods in the ever-evolving field of infectious disease surveillance.

商品描述(中文翻譯)

這本開放存取的書籍是一本全面的指南，深入探討公共衛生和傳染病監測中使用的統計方法。它對比了貝葉斯（Bayesian）和頻率主義（Frequentist）統計方法的基本原則和方法論，詳細探討了這些方法如何應用於傳染病數據的分析和解釋。

本書提供了這些方法在實際研究中的應用指導，無論是監測還是研究目的。它突顯了每種方法的優勢和限制，並展示了如何在各種情境中有效利用這些方法。書中提供了一組 R 語言指令和數據範例，以重現分析結果。涵蓋的主題包括：

- 傳染病分析和監測中的廣義線性模型：獨立數據的方法
- 用於概率推斷和預測的機器學習模型
- 傳染病分析和監測中的廣義線性模型：相關數據的方法
- 廣義線性模型中的殘差和過度離散
- 傳染病研究和監測中的中斷時間序列模型
- 包含缺失數據的廣義線性模型

由於對傳染病數據進行準確分析和解釋的需求日益增加，這一主題在當前特別重要，這對於有效的決策制定和政策制定至關重要。

《傳染病數據分析中的經典與貝葉斯統計方法》主要針對地方、國家或國際機構的公共衛生專業人員；研究人員和學者；學生；以及獸醫和一體化健康專家。這些讀者會發現本書在深入分析、實用指導以及對統計方法在不斷演變的傳染病監測領域應用的關鍵見解方面具有很高的價值。

作者簡介

Noor Muhammad Khan is a doctoral researcher in Biostatistics and Clinical Epidemiology at the University of Padova in Italy. He works with diverse health data such as infectious disease registries, longitudinal electronic records, patient-reported outcomes, and high-resolution neural signals and turns these information sources into evidence that guides clinical practice and public health policy. By integrating classical and Bayesian approaches, he applies regression, hierarchical, and time-series models to support infectious disease surveillance. His research demonstrates how rigorous statistical thinking converts methodological advances into practical tools for clinical and epidemiologic investigations.

Ileana Baldi, PhD, is Associate Professor of Medical Statistics at the University of Padova, Italy. With advanced training in statistics and epidemiology, she is an expert in statistical modeling for health and biomedical research. Her work spans both classical and Bayesian frameworks, applied to complex data from clinical trials, electronic health records, and digital health technologies. She is particularly engaged in developing and refining analytical methods that improve the reliability and interpretability of health data. This book reflects her deep understanding of statistical theory and her commitment to making sophisticated modeling approaches both accessible and practical for epidemiologic applications.

Maria Vittoria Chiaruttini is completing her doctoral research in Biostatistics and Clinical Epidemiology at the University of Padova, Italy. Her work focuses on both the design of clinical and epidemiological studies and the application of advanced statistical methods to analyze longitudinal registry data for population health research. By integrating Bayesian inference, hierarchical modeling, and explainable machine learning techniques, she emphasizes transparency in uncertainty quantification and promotes reproducibility. Passionate about translating data into actionable insights, Maria Vittoria is dedicated to bridging methodological rigor with practical impact in clinical decision-making and public health policy.

Dario Gregori is full Professor of Medical Statistics at University of Padova, Italy. After graduation in Statistics at Pennsylvania State University (US) he got a PhD in Applied Statistics in 1995 at University of Firenze. He is Director of the residency program in Medical Statistics and Biometrics and Coordinator of the Ph.D. Program in Specialized and Translational Medicine "G.B. Morgagni" at University of Padova. His interests include clinical predictive modeling and machine learning algorithms for biomedical research, as well as the use of big data for primary and secondary prevention. He holds several grants in this field from national and international agencies. He published more than 700 papers (H-index 54).

作者簡介(中文翻譯)

Noor Muhammad Khan 是義大利帕多瓦大學生物統計學與臨床流行病學的博士研究員。他處理各種健康數據，如傳染病登記、縱向電子記錄、病人報告的結果以及高解析度神經信號，並將這些信息來源轉化為指導臨床實踐和公共衛生政策的證據。通過整合經典和貝葉斯方法，他應用回歸、層級和時間序列模型來支持傳染病監測。他的研究展示了嚴謹的統計思維如何將方法論的進步轉化為臨床和流行病學研究的實用工具。

Ileana Baldi, PhD, 是義大利帕多瓦大學醫學統計學的副教授。她擁有統計學和流行病學的高級訓練，是健康和生物醫學研究中統計建模的專家。她的工作涵蓋經典和貝葉斯框架，應用於臨床試驗、電子健康記錄和數位健康技術的複雜數據。她特別致力於開發和完善分析方法，以提高健康數據的可靠性和可解釋性。本書反映了她對統計理論的深刻理解，以及她致力於使複雜建模方法既可接觸又實用於流行病學應用的承諾。

Maria Vittoria Chiaruttini 正在義大利帕多瓦大學完成生物統計學與臨床流行病學的博士研究。她的工作專注於臨床和流行病學研究的設計，以及應用先進的統計方法分析縱向登記數據以進行人口健康研究。通過整合貝葉斯推斷、層級建模和可解釋的機器學習技術，她強調不確定性量化的透明性並促進可重複性。Maria Vittoria 熱衷於將數據轉化為可行的見解，致力於在臨床決策和公共衛生政策中橋接方法論的嚴謹性與實際影響。

Dario Gregori 是義大利帕多瓦大學醫學統計學的全職教授。他在美國賓夕法尼亞州立大學獲得統計學學位後，於1995年在佛羅倫斯大學獲得應用統計學博士學位。他是醫學統計學與生物統計學住院醫師計畫的主任，以及帕多瓦大學「G.B. Morgagni」專業與轉化醫學博士課程的協調員。他的研究興趣包括臨床預測建模和生物醫學研究的機器學習算法，以及利用大數據進行初級和次級預防。他在這一領域獲得了來自國內外機構的多項資助，並發表了超過700篇論文（H指數54）。

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