Biostatistics, Data Science, and Machine Learning Applications in Public Health

Dear Colleagues,

The field of modern biostatistics is undergoing rapid transformation driven by the exponential growth of complex, high-dimensional data and the increasing integration of data science and machine learning methodologies into both theoretical and applied research. At the same time, the digitalization of public health systems has produced vast datasets from sources such as electronic health records, surveillance systems, mobile health technologies, and administrative databases. Consequently, governments and health authorities are increasingly relying on data-driven evidence to guide policy decisions, resource allocation, and health system planning. These developments are reshaping traditional approaches to statistical inference, model development, and validation in the context of large-scale, heterogeneous, and complex data structures, creating a strong demand for robust biostatistical and data science methods capable of generating reliable, interpretable, and actionable insights.

In this context, the Special Issue “Biostatistics, Data Science, and Machine Learning Applications in Public Health” seeks to curate high-quality contributions that advance both methodological innovation and substantive application within public health research. The focus is on bridging rigorous statistical theory with practical, data-driven solutions that address pressing and emerging public health challenges. Ultimately, this collection will contribute to advancing the frontiers of biostatistics and data science while ensuring strong translational relevance to real-world public health problems, particularly in the context of rapidly digitizing and data-intensive health systems.

Dr. Peter Modupi Mphekgwana
Dr. Daniel Maposa
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. Stats 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 1600 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.

Publisher’s Notice

Following discussions between the Stats editorial office and the Guest Editor, a new Guest Editor, Dr. Daniel Maposa, has been added to the Special Issue. This change has been approved by the Stats Editorial Board, and the Special Issue website has been updated accordingly on 21 July 2026. The Special Issue will continue to be handled by the new Guest Editor team in accordance with MDPI’s Special Issue and editorial policies.

• machine learning and statistical learning theory
• high-dimensional data analysis
• model selection, uncertainty quantification, and validation frameworks for complex data structures
• Bayesian and frequentist approaches to high-dimensional
• multivariate analyses
• regression
• computational statistics
• public health
• longitudinal data analyses
• classification (supervised and unsupervised) neural networks
• real-world applications in epidemiology, clinical research, health policy, genomics, and health systems analytics
• precision medicine/individualized treatment rules