Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis

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.

Table of Contents:
Chapter 1 Bayesian and Frequentist Approaches in Infectious Disease Data Analysis.- Chapter 2 Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Independent Data.- Chapter 3 Variable Selection in Generalized Linear Models.- Chapter 4 Machine Learning Models for Probabilistic Inference and Prediction.- Chapter 5 Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data.- Chapter 6 Residuals and Overdispersion in Generalized Linear Models.- Chapter 7 Interrupted Time Series Model in Infectious Disease Research and Surveillance.- Chapter 8 Generalized Linear Models with Missing Data.

About the Author :
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).