Principles of Biostatistics in Epidemiology and Public Health

Dear Colleagues,

Biostatistics has long served as the methodological backbone of epidemiology and public health, evolving from early demographic analyses and vital statistics into a sophisticated discipline integrating probability theory, statistical inference, and computational modeling. Landmark developments—such as life tables, regression modeling, and hypothesis testing—have enabled researchers to quantify disease patterns, identify risk factors, and evaluate interventions at the population level. In the modern era, the expansion of digital health data, large-scale cohort studies, and global surveillance systems has further strengthened the central role of biostatistics in informing evidence-based decision-making and policy development.

This Special Issue aims to provide a comprehensive and forward-looking perspective on the principles of biostatistics as applied to epidemiology and public health. It seeks to bridge classical statistical foundations with contemporary analytical approaches, emphasizing methodological rigor, reproducibility, and translational relevance. The scope includes both theoretical advancements and applied research that contribute to a deeper understanding of population health dynamics and support the development of effective public health strategies.

Recent advances in artificial intelligence, machine learning, and high-dimensional data analysis are reshaping the landscape of biostatistical research. Innovative approaches—such as causal inference frameworks, Bayesian modeling, survival analysis extensions, and real-time epidemic forecasting—are increasingly employed to address complex public health challenges. Integration of multi-source data (e.g., clinical, genomic, environmental, and behavioral) enables more precise risk stratification and predictive modeling, while explainable AI methods aim to enhance transparency and trust in data-driven decisions.

We welcome original research articles, systematic reviews, methodological papers, and case studies that highlight novel statistical approaches or provide critical insights into epidemiological practice. Submissions may address topics such as study design optimization, bias and confounding control, advanced modeling techniques, validation of predictive tools, and applications of biostatistics in emerging public health threats. Interdisciplinary contributions that connect biostatistics with clinical medicine, health informatics, and policy-making are particularly encouraged.

Dr. Cristina Gena Dascalu
Dr. Sara Conti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Epidemiologia is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.