Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis

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).